PATENT DOCUMENT

Publication Number: US-8611655-B2
Application Number: US-201113021701-A
Country: US
Kind Code: B2

Title: Hue-based color matching

Abstract:
Some embodiments provide a computer program for performing a color matching operation. The computer program identifies first and second images. Each image includes several pixels. Each pixel includes a hue component value. The computer program identifies a set of hue ranges for the first image based on analysis of the hue component values of pixels in the first image. The computer program identifies a set of hue ranges for the second image based on analysis of the hue component values of pixels in the second image. The computer program modifies pixel values of the first image to match pixel values of the second image based on the sets of hue ranges for the first and second images.

Claims:
We claim: 
     
       1. A non-transitory computer readable medium storing a computer program which when executed by at least one processing unit performs a color matching operation, the computer program comprising sets of instructions for:
 identifying first and second images, each image comprising a plurality of pixels, each pixel comprising a hue component value; 
 identifying a set of hue ranges for the first image based on analysis of the hue component values of pixels in the first image; 
 identifying a set of hue ranges for the second image based on analysis of the hue component values of pixels in the second image; and 
 modifying hue component values of pixels in the first image by (1) determining a set of hue shift operations that each shifts hues of pixels in a particular hue range in the set of hue ranges for the first image towards hues in a corresponding hue range for the second image and (2) performing the set of hue shift operations on pixels in the first image. 
 
     
     
       2. The non-transitory computer readable medium of  claim 1 , wherein the hue ranges in the first set of hue ranges represent dominant hues in the first image and the hue ranges in the second set of hue ranges represent dominant hues in the second image. 
     
     
       3. The non-transitory computer readable medium of  claim 1 , wherein the computer program further comprises a set of instructions for identifying at least one hue range in the set of hue ranges for the first image that is similar to a hue range in the set of hue ranges for the second image. 
     
     
       4. The non-transitory computer readable medium of  claim 1 , wherein each hue shift operation in the set of hue shift operations is for shifting hues of pixels in a particular hue range in the set of hue ranges for the first image such that hues of pixels in the first image that are in the middle of the particular hue range are shifted towards the middle of the corresponding hue range for the second image. 
     
     
       5. The non-transitory computer readable medium of  claim 1 ,
 wherein determining the set of hue shift operations comprises determining a set of transforms for implementing the set of hue shift operations, 
 wherein performing the set of hue shift operations on the first image comprises applying the set of transforms to the first image. 
 
     
     
       6. The non-transitory computer readable medium of  claim 5 , wherein the set of transforms are a set of transformation matrices. 
     
     
       7. The non-transitory computer readable medium of  claim 1 , wherein the first image is a frame of a video clip. 
     
     
       8. The non-transitory computer readable medium of  claim 7 , wherein the video clip is a first video clip, wherein the second image is a frame of a second video clip. 
     
     
       9. The non-transitory computer readable medium of  claim 1 , wherein the first image is a still image. 
     
     
       10. A non-transitory computer readable medium storing a computer program which when executed by at least one processing unit matches colors of a first image to colors of a second image, each image comprising a plurality of pixels, each pixel comprising a hue component value, the computer program comprising sets of instructions for:
 identifying a first dominant hue range in the first image based on a first distribution of hue component values of pixels in the first image; 
 identifying a second dominant hue range in the second image based on a second distribution of hue component values of pixels in the second image; 
 determining whether a similarity between the first dominant hue range and the second dominant hue range passes a threshold based on a set of criteria; 
 modifying, when the determined similarity between the first and second dominant hue ranges passes the threshold, hue component values of pixels in the first image so that hues of pixels having hue values falling in the first dominant hue range are shifted towards hues in the second dominant hue range. 
 
     
     
       11. The non-transitory computer readable medium of  claim 10 , wherein the set of criteria includes a ratio between a peak distribution of pixels of each of the first and second dominant hue ranges. 
     
     
       12. The non-transitory computer readable medium of  claim 10 , wherein the set of criteria includes a ratio between a number of different hue component values in each of the first and second dominant hue ranges. 
     
     
       13. The non-transitory computer readable medium of  claim 10 , wherein the set of criteria includes an amount of different hue component values that the first and second dominant hue ranges share. 
     
     
       14. The non-transitory computer readable medium of  claim 13 , wherein the similarity is determined to not pass the threshold when the first and second dominant hue ranges do not share any hue component values. 
     
     
       15. The non-transitory computer readable medium of  claim 10 , wherein the set of instructions for modifying the hue component values of pixels in the first image comprises a set of instructions for determining a set of transforms for modifying the hue component values of pixels in the first image so that hues of pixels having hue values falling in the first dominant hue range are shifted towards hues in the second dominant hue range. 
     
     
       16. The non-transitory computer readable medium of  claim 15 , wherein the set of instructions for modifying the hue component values of pixels in the first image further comprises a set of instructions for applying the set of transforms to pixels in the first image. 
     
     
       17. The non-transitory computer readable medium of  claim 15 , wherein the set of transforms comprises a hue shift transform for shifting the colors of the first dominant hue range in the first image towards the colors of the second dominant hue range in the second image. 
     
     
       18. The non-transitory computer readable medium of  claim 15 , wherein the set of transforms comprises a contrast transform for matching the contrast of the colors of the first dominant hue range in the first image to the contrast of the colors of the second dominant hue range in the second image. 
     
     
       19. The non-transitory computer readable medium of  claim 15 , wherein the set of transforms comprises a saturation transform for matching the saturation of the colors of the first dominant hue range in the first image to the saturation of the colors of the second dominant hue range in the second image. 
     
     
       20. A method for color matching a first image to a second image, each image comprising a plurality of pixels, each pixel comprising a hue component value, the method comprising:
 analyzing the hue component values of the plurality of pixels in the first image to identify a first set of dominant hue ranges; 
 analyzing the hue component values of the plurality of pixels in the second image to identify a second set of dominant hue ranges; 
 determining a first dominant hue range in the first set of dominant hue ranges that is similar to a second dominant hue range in the second set of dominant hue ranges; 
 determining a set of transforms for modifying hue component values of pixels in the first image so that hues of pixels having hue values within the first dominant hue range are shifted towards hues in the second dominant hue range; and 
 modifying the hue component values of pixels in the first image by applying the set of transforms to the pixels in the first image. 
 
     
     
       21. The method of  claim 20 , wherein the set of transforms comprises a hue shift transform for shifting the first dominant hue range to align with the second dominant hue range. 
     
     
       22. The method of  claim 20 , wherein analyzing the hue component values of the plurality of pixels in the first image comprises determining a distribution of hue component values of the first image, wherein the identification of the first set of dominant hue ranges is based on the distribution of hue component values of the first image. 
     
     
       23. The method of  claim 22 , wherein the distribution of hue component values of the first image is a first distribution, wherein analyzing the hue component values of the plurality of pixels in the second image comprises determining a second distribution of hue component values of the second image, wherein the identification of the second set of dominant hue ranges is based on the distribution of hue component values of the second image. 
     
     
       24. The method of  claim 23 , wherein analyzing the hue component values of the plurality of pixels in the first image further comprises filtering the first distribution in order to smooth the first distribution of hue component values, wherein analyzing the hue component values of the plurality of pixels in the second image further comprises filtering the second distribution in order to smooth the second distribution of hue component values. 
     
     
       25. A method for color matching a first image to a second image, each image comprising a plurality of pixels, each pixel comprising a hue component value, the method comprising:
 analyzing the hue component values of the plurality of pixels in the first and second images to identify a first set of dominant hue ranges and a second set of dominant hue ranges; 
 determining (1) a first dominant hue range in the first set of dominant hue ranges that is similar to a second dominant hue range in the second set of dominant hue ranges and (2) a third dominant hue range in the first set of dominant hue ranges that is similar to a fourth dominant hue range in the second set of dominant hue ranges; 
 determining a (1) first set of transforms for modifying hue component values of pixels in the first image so that hues of pixels having hue values that are within the first dominant hue range are shifted towards hues in the second dominant hue range and (2) a second set of transforms for modifying hue component values of pixels in the first image so that hues of pixels having hue values that are within the third dominant hue range are shifted towards hues in the fourth dominant hue range; and 
 modifying hue component values of pixels in the first image by applying the first and second sets of transforms to the pixels in the first image. 
 
     
     
       26. The method of  claim 25 , wherein analyzing the hue component values of the plurality of pixels in the first and second images to identify a first set of dominant hue ranges and a second set of dominant hue ranges; comprises:
 analyzing a distribution of hue component values of the plurality of pixels in the first; and 
 analyzing a distribution of hue component values of the plurality of pixels in the second images. 
 
     
     
       27. The method of  claim 25 , wherein for each dominant hue range in the first set of dominant hue ranges, the first image includes a threshold amount of pixels having a particular hue component value that falls in the dominant hue range. 
     
     
       28. The method of  claim 25 , wherein for each dominant hue range in the second set of dominant hue ranges, the second image includes a threshold amount of pixels having a particular hue component value that falls in the dominant hue range.

Description:
CROSS REFERENCE TO RELATED APPLICATIONS 
     This Application is related to the following applications: U.S. patent application Ser. No. 13/021,694, filed Feb. 4, 2011, now published as U.S. Patent Publication 2012/0201451; and U.S. patent application Ser. No 13/021,699, filed Feb. 4, 2011, now published as U.S. Patent Publication 2012/0201452. 
     BACKGROUND 
     When editing videos or images, users of a media-editing application often want to reproduce the look and appearance of the colors of another video or image. In order to do so, users typically must make numerous adjustments to various different properties (e.g., saturation, contrast, exposure, etc.) of the media. This becomes a cumbersome process that may take a large amount of time. 
     Moreover, users may also want to modify the appearance of particular colors in a video or image to be similar to the appearance of the particular colors in another video or image. For example, user may wish to match the sky in an image to the sky in one of the user&#39;s favorite images. The user must isolate the portion of the image (e.g., the sky) that the user is interested in modifying and then make adjustments to the various different properties of that portion of the image. This process is similarly cumbersome and time-consuming for the user. 
     BRIEF SUMMARY 
     Some embodiments of the invention provide a novel color matching tool for a media-editing application. The color matching tool automatically matches colors of an image (e.g., target image or destination image) or video clip to colors of another image (e.g., source image or reference image) or video clip. In some embodiments, the color matching tool matches the colors of a target image to the colors of a source image by modifying the color attributes of pixels in the target image so that the colors of the target image appear the same or similar to the colors of the source image. The image that is matched (modified) will be referred to below as the target image and the image to which the target image is matched will be referred to below as the source image. 
     In some embodiments, the media-editing application provides a user interface tool with which a user invokes a color matching operation that matches colors of a target image to colors of a source image. Different embodiments use different techniques to implement the color matching operation. For instance, some embodiments utilize a luma-based color matching technique to match the colors of the target image to the colors of the source image. Some embodiments match the colors of the images using a hue-based color matching operation. Some embodiments use color segmentation to match the colors of the target image to the colors of the source image. 
     Some embodiments that employ a luma-based color matching technique identify several luma ranges (e.g., ranges of luma levels) for the target image and corresponding luma ranges for the source image based on the distribution of luma component values of pixels in each of the respective images. In some embodiments, the target image is matched to the source image in a luma-range-by-luma-range-basis. 
     For each identified luma range of the target image and the corresponding identified luma range of the source image, some embodiments (i) match the contrast of the pixel values in the target image that have luma component values within the luma range of the target image to the contrast of the pixel values in the source image that have luma component values within the corresponding luma range of the source image, (ii) match the average color of the pixel values in the target image that have luma component values within the luma range of the target image to the average color of the pixel values in the source image that have luma component values within the corresponding luma range of the source image, and (iii) match the saturation of the pixel values in the target image that have luma component values within the luma range of the target image to the saturation of the pixel values in the source image that have luma component values within the corresponding luma range of the source image. 
     To match such attributes of the target image to the corresponding attributes of the source image, some embodiments determine a set of transforms for modifying pixel values of the source image so that the above described attributes of the target image are the same or similar to the corresponding attributes of the source image. As noted above, some embodiments match the target image to the source image in a luma-range-by-luma-range-basis. In some of these embodiments, the luma-based color matching operation determines a set of transforms for each luma range of the target image and the corresponding luma range of the source image. In addition, some embodiments blend the sets of transforms to smooth out transitions between the transforms. 
     Instead of, or in conjunction with, a luma-based color matching operations, some embodiments provide a hue-based color matching operation. The hue-based technique of some embodiments identifies dominant hue ranges (e.g., ranges of dominant hues) in each of the images based on the distribution of hue component values of pixels in each of the respective images. Some embodiments identify dominant hue ranges in the target image that are similar to dominant hue ranges in the source image as corresponding dominant hue ranges. 
     To identify corresponding dominant hue ranges, some embodiments determine whether a dominant hue range in the target image is similar to a dominant hue range in the source based on a set of criteria. For instance, some embodiments determine the similarity between dominant hue ranges based on the peak distribution of pixels (e.g., the height of the hue ranges) of each of the hue ranges, the number of different hue component values in the hue ranges (e.g., the width of the hue ranges in a color space), and the amount of hue component values that the dominant hue ranges share (i.e., the intersection of the hue ranges). 
     Based on the corresponding dominant hues, the hue-based color matching operation of some embodiments determines transforms for matching the hues of pixels in the target image that are within a dominant hue to the hues of pixels in the source image that are within the corresponding dominant hue. In some embodiments, the transforms include hue shift transforms that shift the dominant hues of the target image to align with the corresponding dominant hues of the source image. The transforms are applied to the target image to match the colors of the target image to the colors of the source image. 
     As mentioned previously, some embodiments implement the color matching operation using color segmentation, either instead of or in conjunction with the color matching techniques described above. In some embodiments, the color matching operation applies a transform for mapping particular colors of the target image to a color in a set of colors and mapping particular colors of the source image to a color in a corresponding set of colors. For example, the transform of some embodiments maps blue colors and bright colors (e.g., highlights) to blue colors and maps red colors, brown colors, and dark colors (e.g., shadows) to red and/or brown colors. In some embodiments, the transform is a modified version of a transform for converting the pixel values of the images to a device-independent color space that is optimized for identifying colors in an image (e.g., an XYZ color space). Such embodiments take advantage of the device-independent color space&#39;s ability to better identify colors (e.g., highlights and shadows) in the image compared to other color spaces (e.g., an RGB color space) to more accurately map particular colors to a color in the set of colors. 
     After the colors in each image are mapped into the set of colors, each color of the set of colors in the target image is matched to the corresponding color in the source image. Some embodiments match a set of characteristics of a color in the set of colors in the target image to a corresponding set of characteristics of the color in the source image. The set of characteristics include the average color value of the color, the average color value of dark portions of the image with the color, the average color value of bright portions of the image with the color, the average saturation value of the color, and the contrast of the color, in some embodiments. 
     To determine the set of characteristics of a particular color in an image, some embodiments identify the pixel values of the pixels with the particular color and determine the characteristics based on the pixel values of those pixels. For example, average color values of the pixels with the particular color are determined by averaging the color values of the pixels. The average saturation value of pixels with the particular color is similarly determined by averaging the saturation values of the pixels. Some embodiments determine the average color of bright pixels and dark pixels of the particular color using luminance thresholds to identify bright and dark pixels and then averaging the color values of those pixels, respectively. 
     After determining the set of characteristics of the target image and the source image, some embodiments determine transforms to match the set of characteristics of each color in the set of colors of the target image to the set of characteristics of each corresponding color in the source image. In some embodiments, the transforms modify the pixel values of the target image so that the set of characteristics of each color in the set of colors of the source image matches the set of characteristics of each color of the source image. The transforms are then applied to the target image in order to match the colors in the set of colors of the target image to the corresponding colors of the source image. 
     Before converting the target and source images to a device-independent color space in order to map the colors of the images to a set of colors and matching the colors in the set of colors of the target image to the corresponding colors of the source image, some embodiments perform an overall color matching operation. The overall color matching operation of some embodiments matches a set of characteristics of the target image to a corresponding set of characteristics of the source image. Different embodiments define different combinations of characteristics of an image for the overall color matching operation. Examples of characteristics include the average color of the image, the average color of dark portions of the image, the average color of bright portions of the image, the average saturation of the image, the contrast of the image, among other characteristics. 
     Some embodiments analyze the pixel values of an image in order to determine the set of characteristics of the image. In some embodiments, the characteristics of a color described above are determined in a similar manner except the characteristics are determined based on the pixel values of all the pixels in the image instead of the pixel values of pixels that have a particular color. Once the overall color matching operation determines the set of characteristics for the target image and the source image, some embodiments determine transforms for matching the set of characteristics of the target image to the corresponding set of characteristics of the source image. These transforms modify the pixel values of pixels in the target image so that the set of characteristics of the target image match to the corresponding set of characteristics of the source image. After these transform are determined, the transforms are applied to the target image to match the set of characteristics of the target image to the corresponding set of characteristics of the source image. 
     The preceding Summary is intended to serve as a brief introduction to some embodiments of the invention. It is not meant to be an introduction or overview of all inventive subject matter disclosed in this document. The Detailed Description that follows and the Drawings that are referred to in the Detailed Description will further describe the embodiments described in the Summary as well as other embodiments. Accordingly, to understand all the embodiments described by this document, a full review of the Summary, Detailed Description and the Drawings is needed. Moreover, the claimed subject matters are not to be limited by the illustrative details in the Summary, Detailed Description and the Drawing, but rather are to be defined by the appended claims, because the claimed subject matters can be embodied in other specific forms without departing from the spirit of the subject matters. 
    
    
     
       BRIEF DESCRIPTION OF THE DRAWINGS 
       The novel features of the invention are set forth in the appended claims. However, for purposes of explanation, several embodiments of the invention are set forth in the following figures. 
         FIG. 1  conceptually illustrates a graphical user interface of a media-editing application that provides a color matching tool of some embodiments. 
         FIG. 2  conceptually illustrates a software architecture of a color matching tool of some embodiments. 
         FIG. 3  conceptually illustrates a process of some embodiments for color matching images based on the images&#39; luma. 
         FIG. 4  conceptually illustrates the graphical user interface of a media-editing application illustrated in  FIG. 1  that provides a color matching tool of some embodiments. 
         FIG. 5  conceptually illustrates a software architecture of a color matching tool of some embodiments. 
         FIG. 6  conceptually illustrates a process of some embodiments for color matching images based on the images&#39; hues. 
         FIG. 7  conceptually illustrates a process of some embodiments for color matching images by color segmenting the images. 
         FIG. 8  illustrates an example preview display area of a graphical user interface of a media-editing application of some embodiments. 
         FIG. 9  illustrates another example preview display area of a graphical user interface of a media-editing application of some embodiments. 
         FIG. 10  conceptually illustrates a process of some embodiments for determining transforms for color matching images based on the images&#39; luma. 
         FIG. 11  illustrates histograms of example distributions of luma component values of pixels for a target image and a source image. 
         FIG. 12  conceptually illustrates a process of some embodiments for identifying luma ranges for a target image and corresponding luma ranges for a source image. 
         FIG. 13  illustrates the luma ranges illustrated in  FIG. 11  after a split operation has been performed according to some embodiments of the invention. 
         FIG. 14  conceptually illustrates a process of some embodiments for splitting luma ranges. 
         FIG. 15  conceptually illustrates a process of some embodiments for merging luma ranges. 
         FIG. 16  conceptually illustrates a process of some embodiments for determining transforms for matching colors of a target image to colors of a source image. 
         FIG. 17  conceptually illustrates the luma ranges illustrated in  FIG. 13  after gain and lift operations have been applied to the target image according to some embodiments of the invention. 
         FIG. 18  illustrates an example of a set of transforms that is generated for each luma range of a target image illustrated in  FIG. 13  according to some embodiments of the invention. 
         FIG. 19  illustrates an example of a transformation matrix associated with each luma level of the target image illustrated in  FIG. 18  according to some embodiments of the invention. 
         FIG. 20  conceptually illustrates a process of some embodiments for determining gain and lift operations. 
         FIG. 21  illustrates an example mapping of luma levels of a luma range of a target image to luma levels of a source image according to some embodiments of the invention. 
         FIG. 22  illustrates another example mapping of luma levels of a luma range of a target image to luma levels of a source image according to some embodiments of the invention. 
         FIG. 23  conceptually illustrates a process of some embodiments for determining black balance and white balance operations. 
         FIG. 24  illustrates examples of average CbCr component values based on histograms of example distributions of CbCr component values of a target image and a source image. 
         FIG. 25  illustrates an example of black balance and white balance operations that match the colors of the target image to the colors of the source image. 
         FIG. 26  conceptually illustrates a process of some embodiments for determining saturation operations. 
         FIG. 27  illustrates an example of saturation operations that match the saturation of the target image to the saturation of the source image. 
         FIG. 28  conceptually illustrates a process of some embodiments for blending transforms. 
         FIG. 29  illustrates an example of blending a transform associated with a luma level of a target image. 
         FIG. 30  conceptually illustrates a process of some embodiments for applying transforms to a target image. 
         FIG. 31  illustrates an example of determining new values for a pixel of a target image. 
         FIG. 32  conceptually illustrates a process of some embodiments for analyzing the target image and the source image based on the images&#39; hues. 
         FIG. 33  illustrates a histogram of an example distribution of hue component values of pixels in an image. 
         FIG. 34  illustrates an example of the distribution of hue component values illustrated in  FIG. 33  after the distribution has been filtered according to some embodiments of the invention. 
         FIG. 35  conceptually illustrates a process of some embodiments for filtering a distribution of hue component values of an image. 
         FIG. 36  illustrates an example of an identified hue component value and the hue component values that neighbor the identified hue component value 
         FIG. 37  conceptually illustrates a process of some embodiments for identifying dominant hue ranges in an image. 
         FIG. 38  illustrates examples of identifying dominant hue ranges in the histogram of the filtered distribution illustrated in  FIG. 34 . 
         FIG. 39  illustrates an example of filtering of dominant hue ranges identified by the process illustrated in  FIG. 38  according to some embodiments of the invention based on a predefined threshold. 
         FIG. 40  conceptually illustrates a process of some embodiments for identifying dominant hue ranges in a source image that match dominant hue ranges in a target image. 
         FIG. 41  illustrates an example of dominant hue ranges in a target image and a source image. 
         FIG. 42  illustrates a dominant hue range in a target image that is determined to be the most similar hue range of a dominant hue ranges in a source image, for each dominant hue range in the source image. 
         FIG. 43  illustrates an example of identifying a dominant hue range in a target image that matches a dominant hue range in a source image. 
         FIG. 44  illustrates another example of identifying a dominant hue range in a target image that matches a dominant hue range in a source image. 
         FIG. 45  illustrates an example of matching dominant hues in a target image and a source image that are identified by the process illustrated in  FIG. 40  according to some embodiments of the invention. 
         FIG. 46  conceptually illustrates a process of some embodiments for determining the similarity between a hue range in the target image and a hue range in the source image. 
         FIG. 47  illustrates an example an intersection between hue ranges. 
         FIG. 48  illustrates an example of a dominant hue range in a source image that is not similar to any dominant hue range in a source image. 
         FIG. 49  conceptually illustrates a process of some embodiments for determining transforms for matching dominant hues of a target image to dominant hues of a source image. 
         FIG. 50  conceptually illustrates a process of some embodiments for applying transforms to a target image to match the colors of the target image to the colors of a source image. 
         FIG. 51  conceptually illustrates a process of some embodiments for matching colors of a target image to colors of a source image by segmenting the colors of the images. 
         FIG. 52  conceptually illustrates a process of some embodiments for determining transforms to match characteristics of a target image to characteristics of a source image. 
         FIG. 53  illustrates a process of some embodiments for defining color ranges. 
         FIG. 54  conceptually illustrates a process of some embodiments for segmenting the colors of an image into a set of colors. 
         FIGS. 55-58  illustrates examples of segmenting colors of an image into a set of colors. 
         FIG. 59  conceptually illustrates a process of some embodiments for applying transforms to a target image to match the colors of the target image to the colors of a source image. 
         FIG. 60  illustrates a process of some embodiments for matching the colors of each frame of a target video clip to the colors of a source image. 
         FIG. 61  conceptually illustrates a process of some embodiments for identifying a frame in a source video clip to match a frame of a target video clip against. 
         FIG. 62  illustrates an example of determining closeness between two images. 
         FIG. 63  conceptually illustrates the software architecture of a media editing application of some embodiments. 
         FIG. 64  conceptually illustrates an electronic computer system with which some embodiments of the invention are implemented. 
     
    
    
     DETAILED DESCRIPTION 
     In the following detailed description of the invention, numerous details, examples, and embodiments of the invention are set forth and described. However, it will be clear and apparent to one skilled in the art that the invention is not limited to the embodiments set forth and that the invention may be practiced without some of the specific details and examples discussed. 
     Some embodiments of the invention provide a novel color matching tool for a media-editing application. The color matching tool automatically matches colors of an image (e.g., target image or destination image) or video clip to colors of another image (e.g., source image or reference image) or video clip. In some embodiments, the color matching tool matches the colors of a target image to the colors of a source image by modifying the color attributes of pixels in the target image so that the colors of the target image appear the same or similar to the colors of the source image. The image that is matched (modified) will be referred to below as the target image and the image to which the target image is matched will be referred to below as the source image. In some embodiments, the target image and the source image may each be a still image, an image (e.g., frame or field) in a video, or any other type of image. 
     An image in some embodiments is an array of pixels (e.g., 800×600 pixels, 1024×768 pixels, 1600×1200 pixels). Each pixel represents a portion of the image and includes the color and brightness information for such portion of the image. Different embodiments represent the color and brightness information of pixels in an image differently for different color spaces. For instance, for an image defined in an RGB color space, the pixels&#39; color and brightness information are represented by a red component value, a green component value, and a blue component value in some embodiments. In other embodiments, the color and brightness of pixels of an image defined in a Y′CbCr color space are represented using a luma (Y′) component value for brightness and a blue-difference (Cb) component value and a red-difference (Cr) component value for chrominance (i.e., color). In some embodiments, the luma component is the weighted sum of the nonlinear gamma compressed R′G′B′ components. In some of these embodiments, R′G′B′ is gamma corrected red, green, and blue components. Other ways of representing the pixels&#39; color and brightness are possible for images defined in other color spaces. A video clip is a sequence of images (e.g., frames) in some embodiments. 
       FIG. 1  conceptually illustrates a graphical user interface (GUI)  100  of a media-editing application of some embodiments that provides such a color matching tool. Specifically,  FIG. 1  illustrates the GUI  100  at four different stages  110 - 140  of a color matching operation of the color matching tool that matches the colors of a target image to the colors of a source image. 
     As shown in  FIG. 1 , the GUI  100  includes a media library  150 , a preview display area  155 , and a compositing display area  160 . The preview display area  155  displays a preview of a composite presentation that the application creates by compositing several media clips (e.g., video clips, audio clips, audio and video clips, still images, etc.). 
     The media library  150  (also referred to as an “organizer display area”) is an area in the GUI  100  through which a user of the application can select media clips to add to a presentation that the user is compositing with the media-editing application. In addition, the media library  150  of some embodiments can be used for other purposes, such as organizing media clips, compositing media clips, etc. The media clips in the media library  150  are represented as thumbnails that can be selected and added to the compositing display area  160  (e.g., through a cursor operation or a menu selection operation). The media clips in the media library  150  may also be represented as a list, a set of icons, or any form of representation that allows a user to view and select the various media clips in the media library  150 . In some embodiments, the media library  150  may include audio clips, video clips, audio and video clips, text overlays, pictures, sequences of media clips, and other types of media clips. 
     The compositing display area  160  provides a visual representation of the composite presentation being created by the user. Specifically, it displays one or more geometric shapes that each represents one or more media clips that are part of the composite presentation. In some embodiments, the compositing display area  160  specifies a description of a composite presentation (also referred to as a “composite media presentation” or a “composite representation”). 
     As shown in  FIG. 1 , the compositing display area  160  includes a central compositing lane  165  and a user selectable user interface (UI) item  170 . The central compositing lane  165  spans a timeline and displays a graphical representation of the composite presentation by displaying thumbnail representations of media clips that form the composite presentation. One or more media clips can be placed on the central compositing lane  165 . 
     The user selectable UI item  170  is a conceptual illustration of one or more UI items that allows the color matching tool to be invoked (e.g., by a cursor operation such as clicking a mouse, tapping a touchpad, or touching the UI item on a touchscreen). Different embodiments implement the UI item  170  differently. Some such embodiments implement the UI item  170  as a UI button while other embodiments implement the UI item  170  as a menu selection command that can be selected through a pull-down, drop-down, or pop-up menu. Still other embodiments implement the UI item  170  as a keyboard command that can be invoked through one or more keystrokes or a series of keystrokes. Yet other embodiments allow the user to invoker the color matching tool through two or more of such UI implementations or other UI implementations. 
     The operation of the GUI  100  will now be described by reference to the four different stages  110 - 140  that are illustrated in  FIG. 1 . The first stage  110  illustrates that a user has selected media clip  175  in the compositing display area  160  using a cursor (e.g., by clicking a mouse button, tapping a touchpad, or touching a touchscreen). The selection is illustrated by a bolding of the border of the media clip  175 . In this example, the user selects the media clip  175  as the media clip that the user wants to modify (e.g., the target media clip). As shown, the thumbnail representation of the media clip  175  shows an image of a house, a fence, and a sun in the sky. For this example, the media clip  175  is a still image. However, the media clip  175  can be any other type of media clip, as mentioned above. 
     The first stage  110  also illustrates the image of the media clip  175  displayed in the preview display area  155 . In some embodiments, the media-editing application displays the image of the media clip  175  in the preview display area  155  when the media-editing application receives the selection of the media clip  175  from the user. 
     The second stage  120  shows that the user has selected the user selectable UI item  170  (e.g., by clicking a mouse button, tapping a touchpad, or touching the media clip  175  on a touchscreen) to activate the color matching tool. The second stage  120  illustrates this activation by changing the appearance of the UI item  170 . After the user has activated the color matching tool, the user can select another media clip (e.g., a source media clip) to which the user wishes to match the media clip  175 . In some embodiments, after the user has activated the color matching tool, the media-editing application provides another user selectable UI item (e.g., a “Cancel” button) for deactivating the color matching tool. The user can select this UI item in order to deactivate the color matching tool without applying a color matching operation to the media clip  175 . In some cases where the user selects this UI item to deactivate the color matching tool, the media-editing application returns to the state illustrated in the first stage  110  (without the media clip  175  selected and bolded in some embodiments). 
     The third stage  130  illustrates that the user has selected media clip  180  displayed in the media library  150  using the cursor (e.g., by clicking a mouse button, tapping a touchpad, or touching the media clip  180  displayed on a touchscreen). The selection of the media clip  180  is indicated by a bolding of the border of the media clip  180 . Similar to the media clip  175 , the media clip  180  is a still image in this example, but the media clip  180  may be any other type of media clip. As shown, the thumbnail representation of the media clip  180  shows a dark image as indicated by a gray cast in the thumbnail. As mentioned above, after the color matching tool is activated, the user selects another media clip to which the user wants to match the media clip  175 . In this example, the user has selected media clip  180  as the media clip to which the user wants to match the media clip  175 . 
     The third stage  130  also shows that the text of the UI item  170  has changed. As shown, the text of the UI item  170  has changed from “Match” to “Done”. The media-editing application of some embodiments modifies the text of the UI item  170  from “Match” to “Done” and displays the modified UI item  170  when the media-editing application receives the selection of UI item  170  as described above in the second stage  120 . After the user has selected a media clip to which the user wishes to match the media clip  175 , the user can select the modified UI item  170  to invoke a color matching operation that matches the colors of the media clip  175  to the colors of the selected media clip. 
     As noted above, the media clip  180  is a still image in the example illustrated in  FIG. 1 . In cases where the user wants to select an image from a video clip to which the user wishes to match the media clip  175 , some embodiments of the color matching tool allow the user to identify a frame in the video clip using a playhead indicator (also referred to as a scrubber bar) and to select an identified frame in the video clip using a cursor control operation (e.g., clicking a mouse button, tapping a trackpad, or touching a touchscreen). In some embodiments, the user might want to match the colors of a frame in a video clip to the colors of another frame in the same video clip. Some such embodiments provide the user with the same method noted above for identifying (i.e., using a playhead indicator) and selecting the frames in a video clip. 
     The fourth stage  140  illustrates that the user has selected the modified UI item  170  to invoke a color matching operation that matches the colors of the media clip  175  to the colors of the media clip  180 . The fourth stage  140  illustrates the selection of the modified UI item  170  by changing the appearance of the UI item  170 . As shown at the stage  140 , the color matching tool has modified the colors of the media clip  175  to match the colors of the media clip  180  as indicated by the similar gray cast shown in the thumbnail representation of the media clip  175 . In addition, the fourth stage  140  shows a preview of the modified media clip  175  displayed in the preview display area  155 . The media-editing application of some embodiments displays the preview of the modified media clip  175  in the preview display area  155  when the media-editing application receives the selection of the modified UI item  170 . 
     After the color matching tool completes the color matching operation, the media editing application of some embodiments removes the bolding of the border of the media clips  175  and  180  that were used in the color matching operation, as shown in the fourth stage  140 . Furthermore, some embodiments of the media-editing application deactivate the color matching tool after the color matching tool completes the color matching operation by changing the text of the modified UI item  170  from “Done” back to “Match”. 
     Some embodiments of the media-editing application provide a preview of a color matching operation applied to the target media clip before invoking the color matching operation to modify the target media clip. In this manner, the user can see how the target media clip would look with the color matching operation applied without actually applying the color matching operation to the target media clip. However, some embodiments apply the color matching operation to the target media clip when providing a preview of the color matching operation applied to the target media clip. The color matching operation might be applied to the thumbnail representation of the target media clip in some embodiments in order to save processing power when creating a preview. This allows the user to quickly browse through different media clips to select a source media clip to which the user wants to match the target media clip. 
     Different embodiments provide the preview of the color matching operation applied to the target media clip differently. For example, some embodiments of the media-editing application provide the preview in the preview display area  155 . In such embodiments, the media-editing application displays the preview of the target media clip with the color matching operation applied to it when the user selects a source media clip to which the user wishes to match the target image (e.g., in the third stage  130 ). Each time the user selects a media clip in the media library  150  or the compositing display area  160 , the media-editing application of these embodiments generates a preview of the target media clip with the color matching operation applied to it based on the selected source media clip. As another example, some embodiments of the media-editing application provide a second preview display area for displaying the preview when the color matching tool is activated (e.g., in the second stage  120 ). In some such embodiments, the second preview display area may be part of a picture-in-picture arrangement with the preview display area  155  (e.g., as the main picture or as the inset picture) while, in other such embodiments, the second preview display area is a display area separate from the preview display area  155 . 
       FIG. 1  illustrates one arrangement of a GUI of a media-editing application. However, different embodiments of the media-editing application can be arranged any number of different ways. For example, in some embodiments, the media library  150  may be located on the right side of the GUI  100 , the preview display area  155  may be located on the left side of the GUI  100 , and the compositing display area  160  may be located near the top region of the GUI  100 . In addition, some embodiments allow the user to move these display areas around the GUI  100 . The GUI of a media-editing application of some embodiments can include additional and/or other UI elements than those illustrated in  FIG. 1 . For instance, some embodiments may provide a menu tool bar, user selectable UI items to resize the GUI  1  and/or display areas  150 - 160 , other display areas, etc. 
     As described above,  FIG. 1  illustrates one way a user of the color matching tool of some embodiments may invoke a color matching operation. Some embodiments of the color matching tool allow the user to invoke the color matching operation by first selecting two media clips and then selecting the UI item  170  to match the colors of one of the selected media clips with the colors of the other selected media clips. Conversely, some embodiments of the color matching tool allow the user to invoke the color matching operation by first selecting the UI item  170  and then selecting two media clips to match the colors of one of the selected media clips with the colors of the other selected media clip. The color matching tool of some embodiments allows the user to invoke the color matching operation using any combination of different methods described above. 
     The stages  110 - 140  of  FIG. 1  show the user selecting various UI elements in the GUI  100  using a cursor. However, other embodiments provide other ways of selecting UI elements. For example, some embodiments of the media-editing application allow the user to select UI elements by touching the UI elements that are displayed on a touchscreen. As another example, the media-editing application of some embodiments allows user to select the UI elements through a keyboard command (e.g., a keystroke, a combination of keystrokes, or a series of keystrokes, etc.). In some embodiments, the media-editing application allows the user select the UI elements through a command included in a menu (e.g., a drop-down menu, a pull-down menu, a pop-up menu, etc.) Other ways of selecting the various UI elements of the GUI  100  are possible. 
     While the example illustrated in  FIG. 1  shows a particular sequence of operations for a color matching operation, other sequences of operations are possible. For instance, after the user has activated the color matching tool in the second stage  120 , the user may select any number of different media clips in the media library  150  and the compositing display area  160  in order to find a media clip (e.g., a source media clip) to which the user wishes to match the media clip  175  (e.g., a target media clip). The media clip that the user most recently selected before selecting the modified UI item  170  is the media clip to which the media clip  175  will be matched. 
     The following  FIG. 2  conceptually illustrates a software architecture  200  of a color matching tool of some embodiments. As shown,  FIG. 2  illustrates the software architecture  200  at three different hierarchical levels. The top level of the software architecture  200  includes a color matcher  210 . As illustrated at the top level, the color matcher  210  receives a target image (or a frame from a video clip) and a source image (or a frame from a video clip). In some embodiments, the color matcher  210  receives the images from a media-editing application or any other application that provides the color matching tool. The color matcher  210  analyzes the attributes (e.g., contrast, saturation, luminance, luma, hue, etc.) of each of the received images. In some embodiments, the color matcher  210  converts the color space of the received images (e.g., from RGB to Y′CbCr) before analyzing their attributes. 
     Based on the analyses of the images, the color matcher  210  of some embodiments modifies the target image (as indicated by the “Target′” notation) so that the colors of the target image match the colors of the source image. Instead of modifying the target image, some embodiments of the color matcher  210  generate a version of the target image with the colors that match the colors of the source image. This leaves an unmodified copy of the target image as well as the modified version with the colors matched to the colors of the source image. In some embodiments, the color matcher  210  outputs the color matched target image (e.g., to a preview display area of a GUI). 
     The middle level of the software architecture  200  illustrates the modules that are included in the color matcher  210  of some embodiments. As shown at this level by dashed brackets, the color matcher  210  includes a transform generator  220  and a color transform engine  230 . The transform generator  220  receives the target image and the source image as input. In some embodiments, the transform generator  220  analyzes the attributes of the target image and the source image in order to determine and generate transforms that match the colors of the target image to the colors of the source image. The transform generator  220  then sends the generated transforms to the color transform engine  230 . 
     As shown at the middle level of the software architecture  200 , the color transform engine  230  receives as input the target image and receives from the transform generator  220  the transforms generated by the transform generator  220 . As mentioned, the generated transforms are for matching the colors of the target image to the colors of the source image. The color transform engine  230  of some embodiments applies the transforms to the target image so that the colors of the target image match the colors in the source image. In some embodiments, the color transform engine  230  applies the transforms to an unmodified version (e.g., a copy) of the target image (since the transform generator  220  of some embodiments modifies the target image in order to determine some transforms). After applying the transforms to the target image (or a copy of the target image), some embodiments of the color transform engine  230  output the modified target image (e.g., to a preview display area of a GUI). 
     At the bottom level of the software architecture  200 ,  FIG. 2  illustrates the modules that are included in the transform generator  220  of some embodiments. As shown at this level by dashed brackets, the transform generator  220  includes a luma matcher  240 , a hue matcher  250 , and a saturation matcher  260 . Each of the modules  240 - 260  determines different transforms for matching different attributes of the target image to the attributes of the source image. 
     As shown, the bottom level also illustrates conceptual representations of the color values (e.g., pixel values) of an example target image and an example source image in a three-dimensional color space. In particular, the bottom level of  FIG. 2  illustrates the conceptual effects that the transforms determined by each of the modules  240 - 260  have on the representations of the colors of the target image in an HSL color space (also referred to as a HLS color space), which is a hue, saturation, and luma (or lightness in some embodiments) color space in this example. In the HSL color space, the angle around a central vertical axis represents different hues, the distance from the central vertical axis represents different saturation levels, and the distance along the central vertical axis represents different levels of luma. 
     While these conceptual representations are shown as contiguous cylinders, one of ordinary skill in will recognize that the pixel values of an image are actually a set of discrete pixel values that may occupy an arbitrary set of points in a color space (e.g., the HSL color space shown in this example). Transforms applied by the color matching tool of some embodiments will affect each pixel value separately. In some embodiments, the pixel values of a particular pixel are the color values assigned to the pixel in a particular color space (e.g., the hue, saturation, and luma values). In addition, although the conceptual representations illustrated in  FIG. 2  represent the color values of pixels in the images, the conceptual representations could instead represent the color values of pixels in the images having luma values within a particular range of luma levels. Some embodiments apply particular transforms to the pixels of an image that have luma values in a particular range of luma levels in order to modify the pixel values of those pixels. 
     As illustrated in  FIG. 2 , the luma matcher  240  receives the target image and the source image as input. As shown, a short and thin cylinder is shown for a color space representation  270  of the pixel values of the target image in the HSL color space and a tall and thick cylinder  280  with the bottom portion of the cylinder shifted towards the right is shown for a color space representation  280  of the pixel values of the source image. In some embodiments, the luma matcher  240  determines transforms for matching the luma (e.g., contrast) of the target image to the luma of the source image. 
     As mentioned above, the distance along the central vertical axis of the three-dimensional color space represents different levels of luma. Therefore, in this example, the luma matcher  240  determines transforms that match the range of luma levels of the pixel values of the target image to the range of luma levels of the pixel values of the source image. The effect of these transforms is conceptually illustrated by the vertical stretching of the color space representation  270  of the colors of the target image to match the vertical length of the color space representation  280  of the colors of the source image. In some embodiments, the luma matcher  240  applies the determined transform to the target image and then sends the modified target image to the hue matcher  250 . 
     The hue matcher  250  receives from the luma matcher  240  the target image to which transforms determined by the luma matcher  240  have been applied. The hue matcher  250  also receives as input the source image. The hue matcher  250  of some embodiments determines transforms for matching the hues (e.g., colors) of the target image to the hues (e.g., colors) of the source image. In some such embodiments, the transforms are shear transformations that shift the pixel values of the target image toward the pixel values of the source image. In some embodiments, a two-dimensional shear transformation maintains a fixed axis and shifts all points parallel to the fixed axis by a distance proportional to their perpendicular distance from the fixed axis and a three-dimensional shear transformation maintains a fixed plane and shifts all planes parallel to the fixed plane by a distance proportional to their perpendicular distance from the fixed plane. 
     The angle around the central vertical axis of the HSL color space represents different hues, as noted above. Thus, in this example, the hue matcher  250  determines transforms that match the average pixel value at each of the levels of luma of the target image to the average pixels values at each of the corresponding levels of luma of the source image. The effect of these transforms is conceptually illustrated by the horizontal shifting (e.g., shearing) along the vertical axis of the color space representation of the target image so that the center of the circles along the vertical axis of the color space representation of the target image aligns with the center of the corresponding circles along the vertical axis of the color space representation of the source image. As shown, the lower portion of the color space representation of the target image is shifted towards the right to match the color space representation of the source image. Some embodiments of the hue matcher  250  apply the determined transform to the target image that the hue matcher  250  received from the luma matcher  240  (to which the luma matcher  240  has already applied the transforms determined by the luma matcher  240 ). The hue matcher  250  of some of these embodiments then sends the modified target image to the saturation matcher  260 . 
     As shown at the bottom level of the software architecture  200  illustrated in  FIG. 2 , the saturation matcher  260  receives from the hue matcher  250  the target image (or a copy of the target image) that has the transforms determined by the luma matcher  240  and the hue matcher  250  applied to it. In addition, the saturation matcher  260  receives as input the source image. Some embodiments of the hue matcher  250  determine transforms for matching the saturation of the target image to the saturation of the source image. 
     As mentioned above, the distance from the central vertical axis of the HSL color space represents different saturation levels. As such, in this example, the saturation matcher  260  determines transforms that adjusts (e.g., increases or decreases) the saturation of the colors of the target image such that the saturation of the colors of the target image matches the saturation of the colors of the source image. The effect of these transforms is conceptually illustrated by a horizontal expansion of the color space representation of the target image to match the color space representation of the source image. As shown,  FIG. 2  conceptually illustrates that the color space representation of the target image appears the same or similar to the color space representation of the source image after the transforms determined by the modules  240 - 260  have been applied to the target image. 
     While many of the features have been described as being performed by one module (e.g., the luma matcher  240 , the hue matcher  250 , the saturation matcher  260 , etc.), one of ordinary skill in the art will recognize that the functions described herein might be split up into multiple modules. Similarly, functions described as being performed by multiple different modules might be performed by a single module in some embodiments (e.g., the color matching function, the transform generating function, etc.). 
     The above  FIG. 2  conceptually illustrates matching the colors (e.g., luma, hue, and saturation) of an image to the colors of another image according to some embodiments of the invention. These embodiments match the colors in a luma-based manner.  FIG. 3  conceptually illustrates a luma-based color matching process  300  of some embodiments. In some embodiments, the process  300  is performed by the color matching tool when it performs a color matching operation (e.g., when the user selects the modified UI item  170  in the third stage  130  as described above by reference to  FIG. 1 ). 
     As shown, the process  300  begins by identifying (at  310 ) a target image. The process  300  then identifies (at  320 ) a source image. As mentioned above, the target image and the source image may each be a still image, an image (e.g., frame) from a video, or any other type of image. In some embodiments, the identified images are images that are selected by a user through a GUI of an application (e.g., GUI  100 ) that provides the color matching tool. 
     After the target image and the source image have been identified, the process  300  then determines (at  330 ) transforms for matching the colors of the target image to the colors of the source image based on each image&#39;s luma. In some embodiments, a set of transforms is determined for every luma level (e.g., luma component value) of an image while, in other embodiments, a set of transforms is determined for each of several luma ranges (e.g., ranges of luma levels).  FIG. 10 , as will be described in more detail below, conceptually illustrates a process of some embodiments for determining transforms for each of several luma ranges. 
     Finally, the process  300  applies (at  340 ) the transforms to the target image to match the colors of the target image to the colors of the source image.  FIG. 30 , which is described in further detail below, conceptually illustrates such a process for applying transforms to a target image. For each pixel in the target image, some embodiments apply a set of transforms to the pixel. After all the pixels in the target image are processed, the colors of the target image match the colors of the source image. 
     While the above figures describe a technique for matching the colors of the target image to the colors of the source image based on the images&#39; luma (also referred to as global color matching), the following will describe another technique for matching the colors of a target image to the colors of a source image. Some embodiments of the color matching tool provide a more localized color matching technique that matches the colors of the images based on the images&#39; hues (also referred to as local color matching). Some of these embodiments utilize a dominant hue-dependent color matching technique. In such a technique, dominant hues in the target image and the source image are identified and dominant hues in the target image are shifted to match dominant hues in the source image that are the same or similar. 
     Different embodiments implement dominant hue-dependent color matching differently. For instance, some embodiments match the colors of the images based on the images&#39; dominant hues and independent of the luma (or luminance) of the images. In other embodiments, after the dominant hue-dependent color matching technique is performed, all of the operations described above by reference to  FIG. 2  (or a subset of them) are also performed on the images. Unlike the previously described color matching technique, such embodiments analyze the hues of the images and adjust (e.g., shift) the hues in the target image before performing the operations described by reference to  FIG. 2 . 
     As noted above, some of the operations described by reference to  FIG. 2  are conceptually viewed as vertical adjustments of the colors of a target image along the vertical axis, horizontal shifts of colors of the target image along the vertical axis, and horizontal expansion or contraction of the colors of the target image along the vertical axis. In this same conceptual example, the dominant hue-dependent color matching technique can be viewed as a rotation of the colors (e.g., hues) of the target around the vertical axis that is independent of luma (or lightness). Alternatively, the dominant hue-dependent color matching can be conceptually viewed as rotations of a two-dimensional color space (e.g., a two-dimensional color wheel) around a center point. 
       FIG. 4  conceptually illustrates a GUI  400  of a media-editing application that provides both local and global color matching tools. As shown, the GUI  400  is similar as the GUI  100  illustrated in  FIG. 1  except the GUI  400  includes media clip  460  instead of the media clip  180  and includes an additional user selectable UI item  450  that is labeled “Match  2 ”. In addition, the UI item  170  is labeled “Match  1 ” accordingly. The UI item  450  is similar to the UI item  170 , but instead of allowing for the global color matching tool to be invoked, the UI item  450  allows the local color matching tool to be invoked. As such, the UI item  450  is a conceptual illustration of one or more UI items for invoking the local color matching tool and can be implemented using the many different ways described above for the UI item  170 . 
     The operation of the GUI  400  will now be described by reference to four different stages  410 - 440  that are illustrated in  FIG. 4 . The first stage is the same as the first stage  110  that is described above by reference to  FIG. 1 . In the first stage  410 , the user has selected the media clip  175 , which is the media clip that the user wants to modify (e.g., the target media clip). 
     The second stage  420  is similar to the second stage  120 , which is described above by reference to  FIG. 1 . However, in the second stage  420 , the user has selected the UI item  450  (e.g., by clicking a mouse button, tapping a touchpad, or touching the media clip  175  on a touchscreen) instead of the UI item  170  in order to activate the local color matching tool after which the user can select another media clip to which the user wishes to match the media clip  175 . This stage  420  similarly illustrates the selection of the UI item  450  by changing the appearance of the UI item  450 . After the user has activated the local color matching tool, the media-editing application of some embodiments provides another user selectable UI item (e.g., a “Cancel” button) for deactivating the local color matching tool. The user can select this UI item in order to deactivate the local color matching tool without applying a local color matching operation to the media clip  175 . In some instances where the user selects this UI item to deactivate the local color matching tool, the media-editing application returns to the state illustrated in the first stage  410  (without the media clip  175  selected and bolded in some embodiments). 
     The third stage  430  is similar to the third stage  130  that is described above by reference to  FIG. 1  except, at the third stage  430 , the user has selected the media clip  460  as the media clip to which the user wants to match the media clip  175 . The selection of the media clip  460  is indicated by a similar bolding of the border of the media clip  460 . In this example, the media clip  460  is a still image. However, as mentioned above, the media clip  460  may be any other type of media clip. As shown, the thumbnail representation of the media clip  460  shows an image of mountains, a sun, and trees. 
     Similar to the third stage  130 , the third stage  430  of the GUI  400  shows the text of the UI item  450  modified from “Match  2 ” to “Done” and displayed when the media-editing application receives the selection of UI item  450  as described above in the second stage  420 . After the user has selected a media clip to which the user wishes to match the media clip  175 , the user can select the modified UI item  450  to invoke a local color matching operation that matches the colors of the media clip  175  to the colors of the selected media clip. 
     The fourth stage  440  is similar to the fourth stage  140  that is described above by reference to  FIG. 1  but, in this fourth stage  440 , the user has selected the modified UI item  450  to invoke a local color matching operation that matches the colors of the media clip  175  to the colors of the media clip  460 . The fourth stage  440  illustrates the selection of the modified UI item  450  by similarly changing the appearance of the UI item  450 . As shown at the fourth stage  440 , the local color matching tool has modified the colors of the media clip  175  to match the colors of the media clip  460 . In particular, the green color of the grass in the media clip  175  is matched to the green color of the trees in the media clip  460 , as indicated by the matching medium gray color of the grass. In addition, the yellow color of the sun in the media clip  175  is matched to the yellow color of the sun in the media clip  460 , which is indicated by the matching light gray of the sun. The fourth stage  440  also shows a preview of the modified media clip  175  displayed in the preview display area  155 . In some embodiments, the media-editing application displays the preview of the modified media clip  175  in the preview display area  155  when the media-editing application receives the selection of the modified UI item  450 . 
     For purposes of explanation and simplicity, the local color matching illustrated in the fourth stage  440  shows that only the green color of the grass in the media clip  175  matched to the green color of the trees in the media clip  460  and the yellow color of the sun in the media clip  175  matched to the yellow color of the sun in the media clip  460  in this example. However, other colors in the media clip  175  may also be matched as well (e.g., the blue color of the sky in the media clip  175  can be matched to the blue color of the sky in the media clip  460 ). 
     After the local color matching tool completes the local color matching operation, the media editing application of some embodiments removes the bolding of the media clips  175  and  460  that were used in the local color matching operation, as shown in the fourth stage  440 . Moreover, some embodiments of the media-editing application deactivate the local color matching tool after the local color matching tool completes the local color matching operation by changing the text of the modified UI item  450  from “Done” back to “Match  2 ”. 
     Different embodiments of the media-editing application provide a preview of a local color matching operation applied to the target media clip before invoking the local color matching operation to modify the target media clip in the various different ways described above by reference to  FIG. 1 . That is, some embodiments provide the preview in the preview display area  155 , and some embodiments of the media-editing application provide a second preview display area (e.g., in a picture-in-picture fashion or as a preview display area separate from a first preview display area) for displaying the preview when the local color matching tool is activated (e.g., in the second stage  420 ). 
     Like to the GUI  100 , the display areas  150 - 160  in GUI  400  can be arranged differently, resized, moved, etc. in some embodiments, as described above. In addition, the GUI  400 , in some embodiments, can invoke the local color matching tool in the numerous different ways that are described above for the GUI  100 . Also, the user media-editing application of some embodiments allows the user to select the numerous UI elements in the GUI  400  in the different ways, as describe above for the GUI  100 . 
     While the example illustrated in  FIG. 4  shows a particular sequence of operations for a local color matching operation, other sequences of operations are possible. For example, after the user has activated the local color matching tool in the second stage  420 , the user may select any number of different media clips in the media library  150  and the compositing display area  160  in order to find a media clip (e.g., a source media clip) to which the user wishes to match the media clip  175  (e.g., the target media clip). The most recent media clip that the user selects before selecting the modified UI item  450  is the media clip to which the media clip  175  is matched. 
       FIG. 5  conceptually illustrates a software architecture  500  of a local color matching tool of some embodiments. Specifically,  FIG. 5  illustrates the software architecture  500  at three different hierarchical levels. As shown, the top level of the software architecture  500  includes a color matcher  510  that receives a target image (e.g., a frame from a video clip) and a source image (e.g., a frame from a video clip). Some embodiments of the color matcher  510  receive the images from a media-editing application or any other application that provides the local color matching tool. The color matcher  510  analyzes the hue attributes of each of the received images in order to identify dominant hues in each of the images. In some embodiments, the color matcher  510  converts the color space of the received images (e.g., from RGB to Y′CbCr) before performing this analysis. 
     Based on the dominant hues that were identified in each of the images, some embodiments of the color matcher  510  determine matching dominant hues in each of the images. In this manner, dominant hues in the target image are matched to the same or similar dominant hues in the source image. After identifying and matching dominant hues in each of the images, the color matcher  510  of some embodiments modifies the target media clip (as indicated by the “Target” notation) so that the colors of the target media clip match the colors of the source media clip. Instead of modifying the target image, some embodiments of the color matcher  510  generate a version of the target image with the colors that match the colors of the source image. In some embodiments, the color matcher  510  outputs the color matched target image (e.g., to a preview display area of a GUI). 
     The middle level of the software architecture  500  illustrates the modules that are included in the color matcher  510 . As shown at this level by dashed brackets, the color matcher  510  includes a hue engine  520  and a color transform engine  530 . The hue engine  520  receives as input the target image and the source image. In some embodiments, the hue engine  520  analyzes the hue attributes of the target image and the source image to identify the dominant hues in the target image and the dominant hues in the source image. In addition, the hue engine  520  of such embodiments matches dominant hues in the target image with dominant hues in the source image that are the same or similar. Based on the analyses of the images&#39; hues, the hue engine  520  shifts the hues in the target image so that the dominant hues in the target image align with the matching dominant hues in the source image. In some embodiments, the hue engine  520  determines and generates transforms that match the colors of the dominant hues of the target image to the colors of the dominant hues of the source image. The hue engine  520  then sends the generated transforms to the color transform engine  530 . 
     The color transform engine  530  is similar in many ways to the color transform engine  230  describe above. However, in this example, the color transform engine  530  receives as input the target image and receives from the hue engine  520  the transforms generated by the hue engine  520 . As mentioned, the generated transforms are for matching the colors of the target image to the colors of the source image. Some embodiments of the color transform engine  530  apply the transforms to the target image so that the dominant hues of the target image match to corresponding dominant hues in the source image. In some embodiments, the color transform engine  530  applies the transforms to an unmodified version (e.g., a copy) of the target image (since the transform generator  520  of some embodiments modifies the target image in order to determine some transforms). After applying the transforms to the target image (or a copy of the target image in some embodiments), some embodiments of the color transform engine  530  output the modified target image (e.g., to a preview display area of a GUI). 
     At the bottom level of the software architecture  500 ,  FIG. 5  illustrates the modules that are included in the hue engine  520 . As shown, the hue engine  520  includes a dominant hue identifier  540 , a dominant hue matcher  550 , and a hue shifter  560 . 
     In addition, the bottom level illustrates conceptual representations of the color values (e.g., pixel values) of an example target image and an example source image in a two-dimensional color wheel. Specifically, the bottom level shows the conceptual effects that the functions performed by each of the modules  540 - 560  have on the representations of the hues of the target image. In this example, the angle around the center of the color wheel represents different hues. As such, a “wedge” in the color wheel represents a range of hues. 
     While these conceptual representations are shown as contiguous circles and wedges, one of ordinary skill in will realize that the pixel values of an image are actually a set of discrete pixel values that may occupy an arbitrary set of points in a color space (e.g., the HSL color space show in this example). These pixel values may be more highly concentrated in the regions of the color space represented by the wedges in this  FIG. 5 . Transforms applied by the color matching tool of some embodiments will affect each pixel value separately. In some embodiments, the pixel values of a particular pixel are the color values assigned to the pixel in a particular color space (e.g., the hue, saturation, and luma values). 
     As illustrated in  FIG. 5 , the dominant hue identifier  540  receives the target image and the source image as input. As shown, a color wheel that represents the hues in the target image and is indicated with a “T” and a color wheel that represents the hues in the source image is indicated with an “S”. Some embodiments of the dominant hue identifier  540  identify dominant hues in the target image and dominant hues in the source image. 
     In this example, the dominant hue identifier  540  has identified three dominant hue ranges in the target image and three dominant hue ranges in the source image. As shown, three wedges in the color wheel that represents the hues in the target image represent three dominant hue ranges of pixel values in the target image. In addition, three wedges in the color wheel that represents the hues in the source image represent three dominant hue ranges of pixel values in the source image. After identifying the dominant hues in the target image and the source image, the dominant hue identifier  540  sends to the dominant hue matcher  550  information that indicates the dominant hues in the images along with the target image and the source image. 
     The dominant hue matcher  550  receives from the dominant hue identifier  540  the target image, the source image, and information indicating the dominant hues that the dominant hue identifier  540  has identified in each of the images. Based on this information, the dominant hue matcher  550  of some embodiments matches dominant hue ranges of pixel values in the target image with corresponding same or similar dominant hue ranges of pixel values in the source image. In some embodiments, the dominant hue matcher  550  matches the dominant hue ranges of pixel values in the target image with corresponding same or similar dominant hue ranges of pixels in the source image based on similarity factors, such as the range pixel values of dominant hues (e.g., the angle formed by the wedge that represents the range of pixel values of dominant hue in the color wheel), the size of the portion (e.g., the number of pixels) of each of the images that has pixel values in the dominant hue ranges, and the amount of overlapping hues in the dominant hues (e.g., the amount of overlap of pixel values in each of the color wheels). 
     For this example, the dominant hue matcher  550  has matched the three dominant hue ranges of pixel values in the target image with the three dominant hue ranges of pixel values in the source image, as indicated by the same type of lines filled in the matching wedges of the color wheels of the images. As shown, the dominant hue matcher  550  matched the wedge filled with the horizontal lines in the color wheel of the target image with the wedge filled with the horizontal lines in the color wheel of the source image. Likewise, the dominant hue matcher  550  matched the wedge filled with the vertical lines in the color wheel of the target image with the wedge filled with the vertical lines in the color wheel of the source image and matched the wedge filled with the horizontal and vertical lines in the color wheel of the target image with the wedge filled with the horizontal and vertical lines in the color wheel of the source image. After matching the dominant hue ranges of pixel values in the target image and the source image, the dominant hue matcher  550  sends to the hue shifter  560  information regarding the matched dominant hue ranges of pixel values in the images along with the target image and the source image. 
     As shown at the bottom level of the software architecture  500  illustrated in  FIG. 5 , the hue shifter  560  receives from the dominant hue matcher  550  the target image, the source image, and information that indicates matching dominant hue ranges of pixel values in the images. Based on this information, the hue shifter  560  of some embodiments performs a hue shift for each of the matching dominant hue ranges of pixel values in the target image and the source image so that each dominant hue range of pixel values in the target image is shifted towards the corresponding matching dominant hue range of pixel values in the source image. Some embodiments of the hue shifter  560  send the hue shifted target image and the source image (not shown) to a transform generator  570 . 
     The transform generator  570  of some embodiments determines and generates transforms that match the colors of the target image to the colors of the source image. In particular, some of these embodiments determine transforms that match the color attributes of the dominant hues in the target image to the color attributes of the matching dominant hues in the source image. As indicated by the dashed box of the transform generator  570 , the transform generator  570  does not determine and generate these transforms in some embodiments. Instead, the hue shifter  520 , in some embodiments, determines and generates such transforms before sending the hue shifted target image to the color transform engine  530 . 
     While many of the features have been described as being performed by one module (e.g., the dominant hue identifier  540 , the dominant hue matcher  550 , the hue shifter  560 , etc.), one of ordinary skill in the art will realize that the functions described herein might be split up into multiple modules. Similarly, functions described as being performed by multiple different modules might be performed by a single module in some embodiments (e.g., the color matching function, the hue analysis function, etc.). 
     Several of the figures illustrated above describe a local color matching tool that matches the colors of the target image to the colors of the source image based on the images&#39; hues. The following  FIG. 6  conceptually illustrates a process  600  of some embodiments for color matching images based on the images&#39; hues. In some embodiments, the process  600  is performed by the local color matching tool when it performs a local color matching operation (e.g., when the user selects the modified UI  450  in the third stage  430  as described above by reference to  FIG. 4 ). 
     The process  600  begins in the same way as the process  300 , which is described above by reference to  FIG. 3 . Operations  610  and  620  are the same as described above for operations  310  and  320 . At these operations, the process  600  identifies a target image and a source image. As noted above, the target image and the source image may each be a still image, an image (e.g., frame) from a video, or any other type of image. In some embodiments, the identified images are selected by a user through a GUI of an application (e.g., GUI  400 ) that provides the color matching tool. 
     After identifying the target image and the source image, the process  600  then analyzes (at  630 ) the target image and the source image based on the images&#39; hue in order to identify dominant hues in the target image that match dominant hues in the source image. In some embodiments, hue is the degree to which a color is similar to or different from the colors red, blue, green, and yellow. 
     The process  600  of some embodiments analyzes the target and source images by examining the hue distribution of each image and identifying dominant hues (e.g., hue bumps) in the image. After the dominant hues are identified in each image, some embodiments identify dominant hues of the target image and dominant hues of the source image as matching dominant hues based on the similarity of the dominant hues. For example, dominant red and blue hues in a target image may be identified as matching dominant maroon (i.e., dark red) and navy (i.e., dark blue) hues, respectively, in a source image. 
     Next, the process  600  determines (at  640 ) a set of transforms for matching colors of the target image to the colors of the source image based on analysis at operation  630 . Operation  640  is similar to operation  330 . However, in some embodiments, a hue shift is performed to align the dominant hues in the target image with the corresponding matching dominant hues in the source image before the transforms are determined. 
     Finally, the process  600  applies (at  650 ) the transforms to the target image to match the colors of the target image to the colors of the source image. Operation  650  is similar to operation  340  except only pixels in the target image that have hues that are identified as dominant hues in the target image are modified using the transforms. In some embodiments, pixels in the target image that have hues that are identified as not within a dominant hue in the target image are unmodified. In this manner, a more localized color matching method is provided so that dominant colors of the target image are matched to corresponding dominant colors of the source image.  FIG. 50 , which is described in further detail below, conceptually illustrates a process of some embodiments for applying transforms to the target image to match the colors of the target image to the colors of the source image based on the images&#39; hues. 
     The figures above describe some techniques for global color matching and for local color matching. However, some embodiments provide other techniques for global color matching and local color matching. For instance, some embodiments implement global color matching by analyzing the overall characteristics of the images and using a color segmentation technique. 
     Some embodiments segment colors of an image by modifying transforms that are used to convert the color space of the image to a color space that facilitates identifying certain colors in the image (e.g., whites and blacks). In some such embodiments, the transforms are modified so that certain colors are shifted (or skewed) towards other colors. Segmenting an image in this manner allows some embodiments to identify different subject types in the image based on the color of the subject types. For example, white and blue colors can be used to identify sky, green colors can be used to identify foliage, and red and brown colors can be used to identify earth or terrain. These embodiments match the colors of the images by matching the colors of the subject types in the target image to the colors of the corresponding subject types in the source image. 
       FIG. 7  conceptually illustrates a process  700  of some embodiments for color matching images by color segmenting the images. As shown, the process  700  starts by determining (at  710 ) transforms to match a set of characteristics of the target image to the set of characteristics of the source image. Different embodiments of the transforms match different combinations of characteristics of the images. Examples of characteristics include the average color of the image, the average color of dark portions of the image, the average color of bright portions of the image, the average saturation of the image, the contrast of the image, among other characteristics. 
     The process  700  then segments (at  720 ) the colors of the target image to identify a set of colors in the target image and segments the colors of the source image to identify the set of colors in the source image. Some embodiments segment the colors of an image by converting the color space of the image to a device-independent color space (e.g., a XYZ color space). Some such embodiments segment the colors of the image by modifying a set of transforms to convert the image to the device-independent color space so that certain colors in the device-independent color space are shifted towards other colors. For instance, the transform can be modified to shift white colors (e.g., highlights) towards blue colors and to shift dark colors (e.g., shadows) towards red and brown colors. Different embodiments modify the transform to shift other and/or additional colors towards other colors. 
     Next, the process  700  determines (at  730 ) transforms for matching a set of characteristics of each color in the set of colors in the target image to a corresponding set of characteristics of the color in the source image. Different embodiments of these transforms match different combinations of characteristics of each color in the set of colors in the images. Examples of characteristics include the average color value of the color in the image, the average color value of dark portions in the image with the color, the average color value of bright portions of the image with the color, the average saturation value of the color in the image, the contrast of the color in the image, among other characteristics. 
     Finally, the process  700  applies (at  740 ) the transforms to the target image in order to match the colors of the target image to the colors of the source image. In some embodiments, the transforms determined at operation  710  are applied to the target image so that the set of characteristics of the target image is matched to the corresponding set of characteristics of the source image. In some embodiments, the transforms determined at operation  730  are applied to the target image so that the set of characteristics of each color in the set of colors of the target image is matched to the corresponding set of characteristics of the color of the source image. 
     While the process  700  illustrates the transforms determined at the operations  710  and  730  applied to the target image at the operation  740 , some embodiments of the process  700  apply the transforms determined at the operation  710  before the operation  720 , and apply the transforms determined at the operation  730  at the operation  740 . 
     The GUI  100  and the GUI  400  illustrated in  FIGS. 1 and 4 , respectively, both include a preview display area (e.g., the preview display area  155 ) for displaying a target image in some embodiments. 
     As mentioned above, some embodiments provide a preview of a color matching operation applied to a target media clip before invoking the color matching operation to modify the target media clip so that a user can see how the target media clip would look with the color matching operation applied without actually applying the color matching operation to the target media clip. Some of these embodiments provide a preview of a color matching operation applied to a target media clip and a preview of the unmodified target clip in the same preview display area. 
       FIG. 8  illustrates an example preview display area of a GUI  800  of a media-editing application. As shown, the GUI  800  is similar the GUI  100 , but the preview display area  155  provides a preview  810  of a color matching operation applied to a target media clip (e.g., the media clip  175  in this example) and a preview  820  of the unmodified target clip. This example illustrates the GUI  800  at a stage after a color matching tool is activated and a source media clip (e.g., the media clip  180  in this example) has been selected. In some embodiments, the media-editing application provides the previews  810  and  820  when the media-editing application receives a selection of a source media clip. 
     In addition, some embodiments of the media-editing application provide a first preview display area for displaying an unmodified target clip and a second preview display area for displaying a preview of a color matching operation applied to a target media clip. For instance, some embodiments provide the second preview display area as part of a picture-in-picture arrangement with the first preview display area.  FIG. 9  illustrates an example of such picture-in-picture arrangement. 
       FIG. 9  illustrates another example preview display area of a GUI  900  of a media-editing application. The GUI  900  is similar to the GUI  800  except the preview display area  155  and another preview display area  910  are arranged in a picture-in-picture manner. Specifically, the preview display area  155  is the main picture of the picture-in-picture arrangement and the preview display area  910  is the inset picture of the picture-in-picture arrangement. As shown, the preview display area  155  provides a preview of the unmodified target clip (e.g., the media clip  175  in this example) and a preview of a color matching operation applied to a target media clip. This example illustrates the GUI  900  at a stage after a color matching tool is activated and a source media clip (e.g., the media clip  180  in this example) has been selected. In some embodiments, the media-editing application provides the picture-in-picture arrangement when the media-editing application the color matching tool is activated. 
     Several different techniques for matching colors of a target image to colors of a source image are described above. However, different embodiments may employ different combinations of these techniques to match the images&#39; color. For example, some embodiments may match the images&#39; colors based on the images&#39; hues and then use the color segmentation technique to further match the images&#39; colors (or vice versa). Other combinations are possible. 
     Different types of applications may provide the method of automatically matching colors of a target image to colors of a source image. As described above, some embodiments provide such features in a media-editing application (e.g., Final Cut Pro® and iMovie®) in order to match the colors of an image or video clip to the colors of another image or video clip. In some embodiments, image-editing applications (e.g., Aperture®), image organizers, image viewers, and any other type of image application provide the automatic color matching functionality of some embodiments to match the colors of an image to the colors of another image. Furthermore, the color matching functionality may be provided by an operating system of a computing device (e.g., a desktop computer, tablet computer, laptop computer, smartphone, etc.) in some embodiments. 
     Several more detailed embodiments of the invention are described in the sections below. Section I provides a conceptual description of matching colors of images based on the images&#39; luma. Next, Section II conceptually describes matching colors of images based on the images&#39; hues. Section III follows this with a description of matching colors of images using a color segmentation of some embodiments. Next, Section IV describes the software architecture of a media editing application that provides a color matching tool of some embodiments. Finally, Section V describes a computer system that implements some embodiments of the invention. 
     I. Color Matching Based on Luma 
     The numerous figures and examples above illustrate a variety of different techniques that a color matching tool of some embodiments might utilize to match the colors of an image to the colors of another image in a media-editing application. One of those techniques utilizes a luma-based approach to match the colors of one image to the colors of another image. The following sections will describe many examples and embodiments of this luma-based technique. 
     A. Determining Transforms 
     Some embodiments of a color matching tool match the colors of an image to the colors of another image by determining transforms that modify the colors of an image to match the colors of another image. The transforms, in some embodiments, are mathematical operations that are applied to the pixel values of an image in order to modify the pixel values. For example, some embodiments, as described with respect to the process  300 , match colors of a target image to colors of a source image by determining a set of transforms for matching the images&#39; colors based on the images&#39; luma. The following  FIG. 10  conceptually illustrates a process  1000  of some embodiments for determining transforms for color matching images based on the images&#39; luma. As noted above, the process  1000  is performed by the process  300  of some embodiments (e.g., at the operation  330 ). 
     The process  1000  begins by determining (at  1010 ) the luma component values of pixels in the target image and the luma component values of pixels in the source image. Different embodiments determine the luma component values of pixels in an image differently. For example, some such embodiments convert the target image and the source image to a color space that uses a luma component to represent pixels. A Y′CbCr color space is an example of such a color space. As mentioned above, in a Y′CbCr color space of some embodiments, the color and brightness of pixels in an image are represented using a luma component value, a blue-difference component value, and a red-difference component value. Some embodiments apply a transform to the pixels of the image based on the color space in which the image is defined in order to determine the pixels&#39; luma component values. 
     Next, the process  1000  determines (at  1020 ) the distribution of luma component values of pixels in the target image and the distribution of luma component values of pixels in the source image.  FIG. 11  illustrates histograms  1110  and  1120  of example distributions of luma component values of pixels in a target image and pixels in a source image, respectively. As shown, the horizontal axis of the histogram  1110  represents different luma levels. In this example, the luma component value of the pixels in the target image can have twenty different levels of luma (i.e., 0-19). The left side of the histogram  1110  (i.e., the low luma levels) represents pixels in the target image that do not have any brightness or have a low amount of brightness (e.g., black pixels and dark pixels). The right side of the histogram  1110  (i.e., high luma levels) represents pixels in the target image that have a high amount of brightness or have a full amount of brightness (e.g., light pixels and pure white pixels). The middle of the histogram  1110  represents pixels that have a medium amount of brightness (e.g., medium gray pixels) Different embodiments define the luma component to represent a different number of luma levels. For example, the luma component of some embodiments can be defined to represent 256 different luma levels (e.g., 0-255). The luma component can be defined to represent any number of luma levels in other embodiments. The vertical axis of the histogram  1110  represents the number of pixels in the target image that have a particular luma component value. 
     As shown by the distribution curve of the histogram  1110 , the target image has a number of dark (e.g., low brightness) pixels and a larger number of bright pixels. Specifically, approximately 25 percent of the pixels have a luma component value of between 0-9, 25 percent of the pixels have a luma component value between 10-13, 25 percent of the pixels have a luma component value between 14-17, and 25 percent of the pixels have a luma component value between 18 and 19, as shown by the indicated percentiles. 
     The histogram  1120  illustrates the distribution of luma component values of the source image using the same graphing scale. The distribution curve of the histogram  1120  indicates that the source image has a number of dark and bright pixels and a larger number of pixels that are between dark and bright (e.g., medium brightness). As illustrated by the indicated percentiles, approximately 25 percent of the pixels have a luma component value of between 0-5, 25 percent of the pixels have a luma component value between 6-8, 25 percent of the pixels have a luma component value between 9-11, and 25 percent of the pixels have a luma component value between 12-19. 
     Returning to  FIG. 10 , the process  1000  identifies (at  1030 ) luma range for the target image based on the distribution of the luma component values of the target image. In addition, the process  1000  identifies (at  1030 ) corresponding luma ranges for the source image based on the distribution of the luma component values of the source image. The luma ranges of an image are identified in some embodiments based on percentiles (e.g., 25 percent, 50 percent, and 75 percent or 20 percent, 40 percent, 60 percent, and 80 percent) of the distribution of luma component values of pixels in the image.  FIG. 12 , which is described in further detail below, conceptually illustrates an example of such a process of some embodiments. 
     Referring again to  FIG. 11 , the identified luma ranges of the target image and the source image based on the respective distributions of luma component values of the target image and the source image are illustrated in this figure. As shown, the luma ranges identified for the target image and the source image is based on the 25 percent, 50 percent, and 75 percent percentiles of the respective distributions of luma component values of the target image and the source image. Thus, each luma range represents the luma range of 25 percent of the pixels in the image. 
     The process  1000  then determines (at  1040 ) transforms for matching colors of the target image to the colors of the source image. In some embodiments, a set of transforms is determined for each identified luma range of the target image. Each set of transforms is for matching the colors of pixels in the target image that have luma component values within the luma range of the target image to the colors of the pixels in the source image that have luma component values within the corresponding luma range of the source image.  FIG. 16 , which will be described in more detail below, conceptually illustrates a process of some embodiments for determining transforms for matching colors of the target image to colors of the source image. Referring to  FIG. 11  as an example, a set of transforms determined for the first luma range of the target image is for matching the colors of the pixels in the target image that have luma component values within the first luma range of the target image (i.e., 0-9) to the colors of the pixels in the source image that have luma component values within the first luma range of the source image (i.e., 0-5). 
     Finally, the process  1000  performs (at  1050 ) a blending operation on the determined transforms. As described with respect to the process  300 , some embodiments determine a set of transforms for each a luma range of the target image. As such sharp transitions may exist among transforms of luma levels near the border of luma ranges. Referring again to  FIG. 11  as an example, a sharp transition may exist between the transform of last luma level (luma level  9 ) in the first luma range and the first luma level (luma level  10 ) in the second luma range. As such, some embodiments blend these sharp transitions. For instance, some embodiments employ a neighbor-averaging technique, such as the one described in further detail below by reference to  FIG. 28 , to blend sharp transitions among the transforms. Other blending techniques are possible in other embodiments. 
     i. Identifying Luma Ranges 
     As noted above, some embodiments determine transforms for each luma range of several luma ranges of a target image. Different embodiments may identify these luma ranges of the target image differently. For instance, some embodiments identify luma ranges for the target image based on the distribution of the luma component values of the target image and identify corresponding luma ranges for the source image based on the distribution of the luma component values of the source image. 
     The following  FIG. 12  conceptually illustrates a process  1200  of some embodiments for identifying luma ranges for the target image and corresponding luma ranges for the source image. As mentioned above, the process  1200  is performed by the process  1000  of some embodiments (e.g., at the operation  1030 ). The process  1200  will be described by reference to  FIG. 13 , which illustrates different stages  1310 - 1330  of an example of identifying luma ranges according to some embodiments of the invention. 
     The process  1200  starts by identifying (at  1210 ) luma ranges for the target image based on predefined (e.g., default) percentiles of the distribution of luma component values of target image. The process  1200  also identifies corresponding luma ranges for the source image based on the predefined percentiles of the distribution of luma component values of source image. Different embodiments define different numbers of different percentiles of distribution. For instance, quartile distributions are predefined as the percentiles of distribution in some embodiments. Any number of quantiles (e.g., tertiles, quintiles, sextiles, etc.) can be predefined as the percentiles of distribution in other embodiments. As shown, the first stage  1310  of  FIG. 13  illustrates luma ranges of the target image and the source image that are identified based on quartiles of the distribution of luma component values of the images that are illustrated in  FIG. 11 . 
     Next, the process  1200  splits (at  1220 ) groups of luma ranges that are larger than a maximum threshold. Some embodiments examine the luma ranges of the target image to determine whether to perform a split operation while other embodiments examine the luma ranges of the source image to determine whether to perform a split operation. In addition, some embodiments examine the luma ranges of the target image and the source image to determine whether to perform a split operation.  FIG. 14 , which is described in more detail below, illustrates a process that examines luma ranges of the target image and the source image to determine whether to perform a split operation. 
     Different embodiments define a maximum threshold differently. For instance, some embodiments define the threshold in terms of an amount of luma levels (e.g., five luma levels, ten luma levels, etc.) while other embodiments define the threshold in terms of a percentage (e.g., 30 percent, 40 percent, 50 percent, etc.) of all possible luma levels. Other ways of defining the threshold are possible. 
     Referring to  FIG. 13 , the second stage  1320  illustrates the luma ranges illustrated in the first stage  1310  after operation  1220  has been performed. For this example, a percentage threshold of 40 percent is defined as the threshold used to determine whether to perform a split operation. That is, a split operation is performed on a luma range in the target/source image and its corresponding luma range in the source/target image when the number of luma levels in the luma range of either the target image or the source image is greater than eight. 
     As shown in second stage  1320 , since the number of luma levels in first luma range of the target image is greater than eight (i.e., nine), the first luma range of the target image and the source image illustrated in the first stage  1310  are each split into two equal ranges. Specifically, the first luma range of luma levels  0 - 9  of the target image is split into a luma range of luma levels  0 - 4  and a luma range of luma levels  5 - 9 . The corresponding first luma range of luma levels  0 - 5  of the source image is also split into a luma range of luma levels  0 - 2  and a luma range of luma levels  3 - 5 . A split operation is not performed on any of the other luma ranges (i.e., the second, third, and fourth luma ranges of the target image and source image) because none of other luma ranges have a number of luma levels that is greater than eight. 
     After splitting luma ranges, the process  1200  then merges (at  1230 ) groups of consecutive luma ranges that are smaller than a minimum threshold. The luma ranges of the target image are examined to determine whether to perform a merge operation in some embodiments where the luma ranges of the source image are examined to determine whether to perform a merge operation in other embodiments.  FIG. 15 , which is described in further detail below, illustrates a process that examines luma ranges in the target image to determine whether to perform a merge operation. Still, some embodiments examine the luma ranges of the target image and the source image to determine whether to perform a split operation. 
     Different embodiments define a minimum threshold differently. For example, some embodiments define the threshold in terms of an amount of luma levels (e.g., five luma levels, ten luma levels, etc.) whereas other embodiments define the threshold in terms of a percentage (e.g., 30 percent, 40 percent, 50 percent, etc.) of all possible luma levels. Other ways of defining the threshold are possible as well. 
     Referring back to  FIG. 13 , the third stage  1330  illustrates the luma ranges illustrated in the second stage  1320  after operation  1230  has been performed. In this example, a percentage threshold of 35 percent is defined as the threshold used to determine whether to perform a merge operation. Thus, a merge operation is performed on a group of consecutive luma ranges in the target/source image and its corresponding luma ranges in the source/target image when the total number of luma levels in the group of consecutive luma ranges is less than seven. 
     As illustrated in the third stage  1330 , the first and second luma ranges are not merged because the total number of luma levels of first and second luma ranges is greater than or equal to seven (i.e., ten). Similarly, the second and third luma ranges and the third and fourth luma ranges are not merged. However, the fourth and fifth luma ranges are merged because the total number of luma levels of fourth and fifth luma ranges is less than seven (i.e., six). The third stage  1330  illustrates that the fourth luma range of luma levels  14 - 17  and the fifth luma range of luma levels  18  and  19  of the target image have been merged into a single luma range of luma levels  14 - 19 . The corresponding fourth luma range of luma levels  9 - 11  of the source image has also been merged with the fifth luma range of luma levels  12 - 19  to create a single luma range of luma levels  9 - 19 . 
     After merging groups of consecutive luma ranges, the process  1200  ends. As shown in  FIG. 12 , the merging operation is performed after the splitting operation. However, in some cases, the merging operation creates luma ranges that would otherwise have been split in the splitting operation (e.g., the number of luma levels of the merged luma range is larger than the maximum threshold). For example, the fourth luma range of the source image illustrated in third stage  1330  of  FIG. 13  is the result of the merging of the fourth and fifth luma ranges of the source image shown in the second stage  1320 . The fourth luma range of the source image in the third stage  1330  would otherwise have been split (the fourth luma range includes eleven luma levels) in the splitting operation. Thus, in some embodiments, the process  1200  performs the merge operation before the split operation. However, the splitting operation may create luma ranges that would otherwise have been merged in the merging operation (e.g., the number of luma levels of the split luma ranges is less than the minimum threshold). Therefore, the process  1200  of some embodiments repeats operations  1220  and  1230  until the luma ranges are no longer split or merged. In other embodiments, the process  1200  repeats operations  1220  and  1230  a defined number of times. 
       FIG. 14  conceptually illustrates a process  1400  of some embodiments for splitting luma ranges. As mentioned above, the process  1400  is performed by the process  1200  of some embodiments (e.g., at the operation  1220 ). The process  1400  begins by identifying (at  1410 ) a luma range of the target image and the corresponding luma range of the source image. 
     The process  1400  then determines (at  1420 ) whether the number of luma levels in the luma range of the target image or the number of luma levels in the luma range of the source image is greater than a threshold. As described above, some embodiments define the threshold in terms of an amount of luma levels while other embodiments define the threshold in terms of a percentage of all possible luma levels. Some embodiments define multiple different types of thresholds (e.g., a threshold of an amount of luma levels and a threshold of a percentage of all possible luma levels) as well. 
     When the process  1400  determines that the number of luma levels in the luma range of either the target image or the source image is greater than the threshold, the process  1400  splits (at  1430 ) the luma range of the target/source image and the corresponding luma range of the source/target image. In some embodiments, the process  1400  splits the luma ranges into a set (e.g., two, three, five, etc.) of luma ranges that each has the same number of luma levels. In other embodiments, the process  1400  splits the luma ranges into a set of luma ranges that have different numbers of luma levels. For example, some embodiments split the luma range of the target image into a set of luma ranges such that each luma range in the set of luma ranges includes the same distribution of luma component values and, accordingly, split the luma range of the source image into the set same set of corresponding luma ranges. Rather than splitting the luma ranges based on the distribution of luma component values of the target image&#39;s luma range, some embodiments split the luma range of the source image into a set of luma ranges so that each of the luma ranges in the set of luma ranges includes the same distribution of luma component values and split the luma range of the target image into the set same set of corresponding luma ranges. 
     When the process  1400  determines that the number of luma levels in the luma range of either the target image or the source image is not greater than the threshold, the process  1400  then determines (at  1440 ) whether any luma range is left to process. When the process  1400  determines that there is a luma range to process, the process  1400  returns to the operation  1410  to process any remaining luma ranges. Otherwise, the process  1400  ends. 
       FIG. 15  conceptually illustrates a process  1500  of some embodiments for merging groups of consecutive luma ranges. As mentioned above, the process  1500  is performed by the process  1200  of some embodiments (e.g., at the operation  1230 ). The process  1500  begins by identifying (at  1510 ) a group (two in this example) of consecutive luma ranges of the target image. For instance, referring to  FIG. 13 , the first and second luma ranges are a group of consecutive luma ranges, the second and third luma ranges are a group of consecutive luma ranges, the third and fourth luma ranges are a group of consecutive luma ranges, and the fourth and fifth luma ranges are a group of consecutive luma ranges. 
     The process  1500  then determines (at  1520 ) whether the total number of luma levels included in the group of consecutive luma ranges is less than a threshold. As mentioned above, some embodiments define the threshold in terms of an amount of luma levels while other embodiments define the threshold in terms of a percentage of all possible luma levels. Some embodiments define multiple different types of thresholds (e.g., a threshold of an amount of luma levels and a percentage threshold of all possible luma levels) as well. 
     When the process  1500  determines that the total number of luma levels in the group of consecutive luma ranges of the target image is not less than the threshold, the process  1500  proceeds to operation  1550 . When the process  1500  determines that the number of luma levels in the group of consecutive luma ranges of the target image is less than the threshold, the process  1500  identifies (at  1530 ) the group of corresponding consecutive luma ranges of source image. 
     Next, the process  1500  merges (at  1540 ) the group of consecutive luma ranges of the target image into a single luma range and merges the group of corresponding consecutive luma ranges of the source image into a single luma range. 
     At  1550 , the process  1500  determines whether any group of consecutive luma ranges of the target image is left to process. When the process  1500  determines that there is a group of consecutive luma ranges to process, the process  1500  returns to the operation  1510  to process the remaining groups of consecutive luma ranges. When the process  1500  determines that there is not a group of consecutive luma ranges left to process, the process  1500  ends. As described above, the process  1500  examines luma ranges of the target image in order to determine whether to merge a group of consecutive luma ranges. However, some embodiments of the process  1500  also examine luma ranges of the source image in order to determine whether to merge a group of consecutive luma ranges. 
     ii. Operations for Determining Color Matching Transforms 
     After identifying the luma ranges of the target image and the corresponding luma ranges of the source image, some embodiments determine transforms for matching the colors of the target image to the colors of the source image. As described below, some of these embodiments determine the transforms for the luma ranges on a luma-range-by-luma-range basis.  FIG. 16  conceptually illustrates a process  1600  of some embodiments for determining transforms to match the colors of the target image to the colors of the source image. In some embodiments, the process  1600  is performed by the process  1000  (e.g., at the operation  1040 ), as described above. 
     The process  1600  starts by determining (at  1610 ) gain and lift operations to match the contrast of the target image to the contrast of the source image. In some embodiments, a lift operation uniformly lightens or darkens (e.g., increases or decreases luma) an image by adjusting shadows, midtones, and highlights by the same amount. A gain operation, in some embodiments, adjusts contrast by raising or lowering the white point (e.g., the point at which solid white occurs) of an image while leaving the black point (e.g., the point at which solid black occurs) pinned in place, and scaling the midtones between the new white point and the black point. 
     In some embodiments, gain and lift operations map luma levels of the target image to luma levels of the source image in order to match the contrast of the target image to the contrast of the source image.  FIG. 20 , which will be described in more detail below, illustrates a process of some embodiments for determining such gain and lift operations. The gain and lift operations of some embodiments are represented by a transformation matrix. 
     Next, the process  1600  applies (at  1620 ) the determined gain and lift operations to the target image (or a copy of the target image). By applying the gain and lift operations to the target image, the contrast of the target image and the contrast of the source image are matched. The gain and lift operations of some embodiments match the contrast of the target image to the contrast of the source image by mapping luma levels of the target image to luma levels of the source image in order to match the distribution of the luma of pixels in the target image to the distribution of the luma of pixels in the source image. In other words, each of the luma ranges of the target image has the same range of luma levels as the corresponding luma range of the source image after such gain and lift operations have been applied to the target image. Examples of such mapping of luma levels are illustrated in  FIGS. 21 and 22 , which are described in below by reference to  FIG. 20 . As noted above, some embodiments use a transformation matrix to represent the gain and lift operations. In such embodiments, the gain and lift operations are applied to the target image by applying the transformation matrix to the target image. 
       FIG. 17  conceptually illustrates the luma ranges of the target image and source image illustrated in  FIG. 13  after gain and lift operations have been applied to the target image. As shown, the distribution of the luma of pixels in the target image matches the distribution of the luma of pixels in the source image. That is, 12.5 percent of the pixels in the target image and in the source image each have luma component values of 0-2, 12.5 percent of the pixels in the target image and in the source image each have luma component values of 3-5, 25 percent of the pixels in the target image and in the source image each have luma component values of 6-8, and 50 percent of the pixels in the target image and in the source image each have luma component values of 9-19. 
     The process  1600  then determines (at  1630 ) black balance and white balance operations to match the colors of the target image (to which the determined gain and lift operations have been applied at operation  1620 ) to the colors of the source image. In some embodiments, the white balance operation matches the average color (e.g., chrominance in a Y′CbCr color space) of pixels in the target image that have luma component values equal to the top of a luma range to the average color of pixels in source image at the same luma level. In such embodiments, the black balance operation matches the average color (e.g., chrominance in a Y′CbCr color space) of pixels in the target image that have luma component values equal to the bottom of a luma range to the average color of pixels in source image at the same luma level. Further, the process illustrated in  FIG. 23 , which will be described in further detail below, is used to determine black balance and white balance operations in some embodiments. The black balance and white balance operations of some embodiments are represented by a transformation matrix. 
     Next, the process  1600  applies (at  1640 ) the determined gain and lift operations and the determined black balance and white balance operations to the original target image (or a copy of the original target image). In some embodiments, the process  1600  applies just the determined black balance and white balance operations to the target image (or a version of the target image) used to determine the black balance and white balance operations since the determined gain and lift operations are already applied to that target image. As mentioned above, some embodiments use a transformation matrix to represent the black balance and white balance operations. The black balance and white balance operations are applied to the target image by applying the transformation matrix to the target image, in such embodiments. 
     At  1650 , the process  1600  determines saturation operations to match the saturation of the target image (to which the determined gain and lift operations and the determined black balance and white balance operations have been applied) to the saturation of the source image. In some embodiments, the saturation is the intensity of a specific color. In some embodiments, saturation is the colorfulness of a specific color relative to its own brightness. The saturation operations of some embodiments match the saturation level at a percentile of the distribution of saturation levels of pixels in the target image to the saturation level at the corresponding percentile of the distribution of saturation levels of pixels in the source image.  FIG. 26 , which will be described in more detail below, illustrates a process of some embodiments for determining such saturation operations. 
     Finally, the process  1600  determines (at  1660 ) transforms to match colors of target image to color of source image based on the determined gain and lift operations, black balance and white balance operations, and saturation operation. As mentioned above, some embodiments determine a set of transforms for each luma range of the target image. In this fashion, the colors of the target image are matched to the colors of the source image on a luma range-by-luma range basis. 
       FIG. 18  illustrates an example of a set of transforms that is determined for each luma range of the target image illustrated in the third stage  1330  of  FIG. 13  according to some embodiments of the invention. As shown, the first set of transforms is associated with the first luma range (and each luma level in the first luma range), the second set of transforms is associated with the second luma range (and each luma level in the second luma range), the third set of transforms is associated with the third luma range (and each luma level in the third luma range), and the fourth set of transforms is associated with the fourth luma range (and each luma level in the fourth luma range). 
     In some embodiments, a transformation matrix is used to represent the set of transforms (e.g., the determined operations) determined for each luma range. In other words, the determined operations for a luma range are incorporated into a transformation matrix to match the colors of the target image to the colors of the source image for pixels in the target image that have luma component values in the luma range. For example, the gain and lift operations, black balance and white balance operations, and saturation operations may each be represented by a transformation matrix in some embodiments. In such embodiments, the transformation matrices of each operation are multiplied together in order to incorporate the operations into a single transformation matrix. Different embodiments define different transformation matrices with different dimensions to represent the set of transforms for a luma range. For instance, some embodiments define a single 3×4 transformation matrix to represent the set of transforms for a luma range. Other embodiments define a transformation matrix with different dimensions to represent the set of transforms for a luma range. 
     Furthermore, some embodiments associate a transformation matrix with each luma level of the target image.  FIG. 19  illustrates an example of a transformation matrix associated with each luma level of the target image illustrated in  FIG. 18  according to some embodiments of the invention. As shown, the transforms determined for each luma range of the target image (i.e., transforms  1 , transforms  2 , transforms  3 , and transforms  4 ) are the same as the ones illustrated in  FIG. 18 .  FIG. 19  also illustrates a transformation matrix associated with each of the 20 luma levels (i.e., luma levels  0 - 19 ), as indicated by the arrows associating a transformation matrix with a luma level. 
     Since a set of transforms determined for a luma range, the transformation matrices associated with luma levels in the luma range are the same (i.e., have the same values). For example, the transformation matrices associates with luma levels  0 - 4  are the same, the transformation matrices associates with luma levels  5 - 9  are the same, the transformation matrices associates with luma levels  10 - 13  are the same, and the transformation matrices associates with luma levels  14 - 19  are the same. 
     1. Determining Gain and Lift Operations 
     As described above by reference to the process  1600 , some embodiments determine gain and lift operations as part of the process for determining the transforms for matching the colors of a target image to the colors of a source image. In some embodiments, the gain and lift operations map luma levels of the target image to luma levels of the source image in order to match the contrast of the target image to the contrast of the source image.  FIG. 20  conceptually illustrates a process  2000  of some embodiments for determining such gain and lift operations. As described above, the process  2000  is performed by the process  1600  of some embodiments (e.g., at the operation  1610 ). The process  2000  will be described by reference to the third stage  1330  of  FIG. 13 , which illustrates luma ranges of a target image and corresponding luma ranges of a source image determined by the process  1200  of some embodiments. 
     The process  2000  begins by identifying (at  2010 ) a luma range of the target image. In some embodiments, the luma range is a luma range identified by the process  1200 , which is previously described above by reference to  FIG. 12 . 
     The process  2000  then identifies (at  2020 ) boundary luma levels of a luma range of the target image. In some embodiments, the boundary luma levels of a luma range are the bottom and top luma levels of the luma range. Referring to the third stage  1330  of  FIG. 13 , the boundary luma levels of the first luma range of the target image are luma level  0  (i.e., the bottom) and luma level  4  (i.e., the top), the boundary luma levels of the second luma range of the target image are luma level  5  (i.e., the bottom) and luma level  9  (i.e., the top), the boundary luma levels of the third luma range of the target image are luma level  10  (i.e., the bottom) and luma level  13  (i.e., the top), and the boundary luma levels of the fourth luma range of the target image are luma level  14  (i.e., the bottom) and luma level  19  (i.e., the top). 
     Next, the process identifies (at  2030 ) boundary luma levels of the corresponding luma range of the source image. Referring again to the third stage  1330  of  FIG. 13 , the boundary luma levels of the first luma range of the source image are luma level  0  (i.e., the bottom) and luma level  2  (i.e., the top), the boundary luma levels of the second luma range of the source image are luma level  3  (i.e., the bottom) and luma level  5  (i.e., the top), the boundary luma levels of the third luma range of the source image are luma level  6  (i.e., the bottom) and luma level  8  (i.e., the top), and the boundary luma levels of the fourth luma range of the source image are luma level  9  (i.e., the bottom) and luma level  19  (i.e., the top). 
     The process  2000  then calculates (at  2040 ) gain and lift operations to map luma levels of the identified luma range of the target image to the luma levels of the identified luma range of the source image based on the identified boundary luma levels of the target image and the source image. In some embodiments, the gain and lift operations are represented by a linear equation. In some such embodiments, the gain operation is expressed as the slope of the equation and the lift operation is expressed as the y-intercept. The following is an example of such an equation: 
                   y   =             luma   ⁢           ⁢   level   ⁢           ⁢   of   ⁢           ⁢     source   top       -     luma   ⁢           ⁢   level   ⁢           ⁢   of   ⁢           ⁢     source   bottom             luma   ⁢           ⁢   level   ⁢           ⁢   of   ⁢           ⁢     target   top       -     luma   ⁢           ⁢   level   ⁢           ⁢   of   ⁢           ⁢     target   bottom           ⁢   x     +   b             (   1   )               
where x is a luma level of the luma range of the target image, y is the corresponding luma level of the source image to which luma level x is mapped, and b is the lift operation. The following equation is an example of applying the above equation (1) with respect to the first luma ranges illustrated in the third stage  1330  of  FIG. 13 .
 
     
       
         
           
             
               
                 
                   
                     y 
                     = 
                     
                       
                         
                           
                             
                               2 
                               - 
                               0 
                             
                             
                               4 
                               - 
                               0 
                             
                           
                           ⁢ 
                           x 
                         
                         + 
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                       = 
                       
                         
                           
                             1 
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                           ⁢ 
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                         + 
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                     b 
                     = 
                     
                       
                         2 
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                             1 
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                           ⁢ 
                           
                             ( 
                             4 
                             ) 
                           
                         
                       
                       = 
                       0 
                     
                   
                   ⁢ 
                   
                     
 
                   
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                     y 
                     = 
                     
                       
                         1 
                         2 
                       
                       ⁢ 
                       x 
                     
                   
                 
               
               
                 
                   ( 
                   2 
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     In some embodiments, the linear equation does not directly map a luma level of the target image to a luma level of the source image. For instance, the linear equation determined for the first luma ranges of the third stage  1330  of  FIG. 13  maps luma level  1  of the target image to luma level  0 . 5  of the source image and maps luma level  3  of the target image to luma level  1 . 5  of the source image. In such cases, some embodiments utilize a rounding up method to map a luma level of the target image to a luma level of the source image. 
     An example of such rounding up method is illustrated in  FIG. 21 , which illustrates an example mapping of luma levels of a luma range of the target image to luma levels of the corresponding luma range of the source image according to some embodiments of the invention. Specifically,  FIG. 21  illustrates the mapping of the luma levels of the first luma ranges illustrated in the third stage  1330  of  FIG. 13  based on the above equation (2). As shown, luma level  1  of the target image is rounded up from 0.5 to 1 and mapped to luma level  1  of the source image. Similarly, luma level  3  of the target image is rounded up from 1.5 to 2 and mapped to luma level  2  of the source image. Luma levels  0 ,  2 , and  4  of the target image are mapped to luma levels  0 ,  1 , and  2  of the source image, respectively, because the above linear equation directly maps such levels to luma levels of the source image. Although  FIG. 21  illustrates a rounding up method, other embodiments use different methods, such as a rounding down method. 
     The following equation is an example of applying the above equation (1) with respect to the fourth luma ranges illustrated in the third stage  1330  of  FIG. 13 . 
     
       
         
           
             
               
                 
                   
                     y 
                     = 
                     
                       
                         
                           
                             
                               19 
                               - 
                               9 
                             
                             
                               19 
                               - 
                               14 
                             
                           
                           ⁢ 
                           x 
                         
                         + 
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                       = 
                       
                         
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                         + 
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                     b 
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                         19 
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                             ( 
                             19 
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                       = 
                       
                         - 
                         19 
                       
                     
                   
                   ⁢ 
                   
                     
 
                   
                   ⁢ 
                   
                     y 
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                         x 
                       
                       - 
                       19 
                     
                   
                 
               
               
                 
                   ( 
                   3 
                   ) 
                 
               
             
           
         
       
     
       FIG. 22  illustrates another example mapping of luma levels of a luma range of the target image to luma levels of the corresponding luma range of the source image according to some embodiments of the invention. In particular, this figure shows the mapping of the luma levels of the fourth luma ranges illustrated in the third stage  1330  of  FIG. 13  based on the above equation (3). As shown, luma level  14  of the target image is mapped to luma level  9  of the source image, luma level  15  of the target image is mapped to luma level  11  of the source image, luma level  16  of the target image is mapped to luma level  13  of the source image, luma level  17  of the target image is mapped to luma level  15  of the source image, luma level  18  of the target image is mapped to luma level  17  of the source image, and luma level  19  of the target image is mapped to luma level  19  of the source image. In this example, each of the luma levels of the target image is mapped directly to a luma level in the source image. Thus, no rounding up or down was required. 
     In some embodiments, the gain and lift operations determined for a luma range are represented by a transformation matrix that matches the contrast of the luma range of the target image to the contrast of the luma range of the source image. 
     Returning to  FIG. 20 , the process  2000  determines (at  2050 ) whether any luma range (e.g., a luma range of the target image) is left to process. When the process  2000  determines that there is a luma range to process, the process  2000  returns to the operation  2010  to process any remaining luma ranges. Otherwise, the process  2000  ends. 
     2. Determining Black Balance and White Balance Operations 
     In addition to matching the luma of the target image to the luma of the source image, some embodiments also match the colors of the target image to the colors of the source image. As described with respect to process  1600 , some embodiments determine black balance and white balance operations to match the colors of the target image to the colors of the source image. In some embodiments, gain and lift operations are applied to the target image before it is used to determine the black balance and white balance operations. That is, the distribution of the luma of pixels in the target image that is used in such embodiments matches the distribution of the luma of pixels in the source image, and each of the luma ranges of the target image has the same range of luma levels as the corresponding luma range of the source image. 
       FIG. 23  conceptually illustrates a process  2300  of some embodiments for determining such black balance and white balance operations. As mentioned above, the process  2300  is performed by the process  1600  of some embodiments (e.g., at the operation  1630 ). The process  2300  starts by identifying (at  2310 ) a luma range of the target image. In some embodiments, the luma range is a luma range identified by the process  1200 , which is previously described above by reference to  FIG. 12 . 
     Next, the process  2300  calculates (at  2320 ) average CbCr component values based on distributions of CbCr (i.e., chrominance) component values of pixels of the target image that have CbCr component values within a luma range of the target image. In some embodiments, the process  2300  determines a distribution of CbCr component values of pixels in the target image that have luma component values equal to the bottom luma level of the luma range and a distribution of CbCr component values of pixels in the target image that have luma component values equal to the top luma level of the luma range. The distribution of the bottom luma level is for determining the black balance operation. The distribution of the top luma level is for determining the white balance operation. 
       FIG. 24  illustrates two-dimensional CbCr planes  2410  and  2420  that indicate example average CbCr component values of the target image. In this example, the CbCr plane  2410  illustrates an average CbCr component value, indicated by a black dot, based on an example distribution (not shown) of pixels in the target image that have luma component values equal to the bottom luma level (i.e., luma level  0 ) of the first luma range of the target image shown in  FIG. 17 . The CbCr plane  2420  shows a CbCr component value, also indicated by a black dot, based on an example distribution (not shown) of pixels in the target image that have luma component values equal to the top luma level (i.e., luma level  2 ) of the first luma range of the target image illustrated in  FIG. 17 . 
     For this example, the horizontal axis of the CbCr plane  2410  represents different Cb component values (not shown) and the vertical axis of the CbCr plane  2410  represents different Cr component values (not shown). Different embodiments define the Cb and Cr components to each represent a different number of Cb and Cr component values. For example, some of these embodiments define the Cb and Cr components to each represent 256 possible Cb and Cr component values (e.g., 0-255, -127 to 128, etc.), respectively. Other embodiments define the Cb and Cr components to each represent any number of possible Cb and Cr component values. 
     The process  2300  then calculates (at  2330 ) average CbCr component values based on distributions of CbCr (i.e., chrominance) component values of pixels of the source image that have CbCr component values within the corresponding luma range of the source image. In some embodiments, the process  2300  determines a distribution of CbCr component values of pixels in the source image that have luma component values equal to the bottom luma level of the luma range and a distribution of CbCr component values of pixels in the source image that have luma component values equal to the top luma level of the luma range. As mentioned above, the distribution of the bottom luma level is for determining the black balance operation, and the distribution of the top luma level is for determining the white balance operation. 
     Returning to  FIG. 24 , this figure further illustrates two-dimensional CbCr planes  2430  and  2440  that indicate example average CbCr component values of the source image. As shown, CbCr planes  2430  and  2440  are similar to the CbCr planes  2410  and  2420 . That is, the horizontal axis of the CbCr planes  2420  and  2440  represents different Cb component values (not shown) and the vertical axis of the CbCr planes  2430  and  2440  represents different Cr component values (not shown). The CbCr plane  2430  illustrates an average CbCr component value, indicated by a black dot, based on an example distribution (not shown) of pixels in the source image that have luma component values equal to the bottom luma level (i.e., luma level  0 ) of the first luma range of the source image shown in  FIG. 17 . The CbCr plane  2440  shows a CbCr component value, also indicated by a black dot, based on an example distribution (not shown) of pixels in the source image that have luma component values equal to the top luma level (i.e., luma level  2 ) of the first luma range of the source image illustrated in  FIG. 17 . 
     The process  2300  then determines (at  2340 ) black balance and white balance operations for matching the colors of the target image to the colors of the source image based on the calculated averages. In some embodiments, the black balance and white balance operations are represented by a shear transformation for matching the colors of the target image to the colors of the source image. In some such embodiments, a shear transformation matches the colors of the target image to the colors of the source image by shifting the average CbCr component value of the pixels in the target image that have luma component values equal to the bottom of the luma range of the target image towards the average CbCr component value of the pixels in the source image that have luma component values equal to the bottom of the corresponding luma range of the source image. In addition, the shear transformation matches the colors of the target image to the colors of the source image by shifting the average CbCr component value of the pixels in the target image that have luma component values equal to the top of the luma range of the target image towards the average CbCr component value of the pixels in the source image that have luma component values equal to the top of the corresponding luma range of the source image. 
       FIG. 25  illustrates an example of black balance and white balance operations that match the colors of the target image to the colors of the source image based on a shear transformation. Specifically, this figure conceptually illustrates the CbCr planes  2410 - 2440  illustrated in  FIG. 24  in a three-dimensional Y′CbCr color space. As shown on the left side of  FIG. 25 , the CbCr plane  2410  is illustrated at the top of a three-dimensional representation  2510  of the colors in the first luma range of the target image. In addition, the CbCr plane  2420  is illustrated at the bottom of the three-dimensional representation  2510 . The left side of  FIG. 25  also shows the CbCr plane  2430  at the top of a three-dimensional representation  2520  of the colors in the first luma range of the source image and the CbCr plane  2440  at the bottom of the three-dimensional representation  2520 . 
     The right side of  FIG. 25  conceptually illustrates an example black balance and white balance operation that matches the colors of the target image to the colors of the source image for the first luma ranges illustrated in  FIG. 17 . In particular, the right side of  FIG. 25  illustrates a shear transformation that is applied to the three-dimensional representation  2510  of the colors of the target image. As noted above, a three-dimensional shear transformation, in some embodiments, maintains a fixed plane and shifts all planes parallel to the fixed plane by a distance proportional to their perpendicular distance from the fixed plane. As illustrated in this figure, a three-dimensional shear transformation is applied to the three-dimensional representation  2510  such that the average CbCr component value of luma level  0  of the target image matches the average CbCr component value of luma level  0  of the source image and the average CbCr component value of luma level  2  of the target image matches the average CbCr component value of luma level  2  of the source image. This matching is shown by the vertical dashed arrows indicating that the average CbCr component values of the corresponding luma levels are the same. As shown, the shear transformation also shifts luma levels in between the top and bottom luma levels (luma level  1  in this example) of the first luma range accordingly. 
     In some embodiments, the black balance and white balance operations determined for a luma range are represented by a transformation matrix that matches the black balance of the luma range of the target image to the black balance of the luma range of the source image and matches the white balance of the luma range of the target image to the white balance of the luma range of the source image. For instance, the embodiments that use a shear transformation described above can represent the shear transformation by a transformation matrix that matches black balance and white balance of the target image to the black balance and white balance of the source image. 
     Finally, the process  2300  determines (at  2350 ) whether any luma range (e.g., a luma range of the target image) is left to process. When the process  2300  determines that there is no luma range to process, the process  2300  ends. When the process  2300  determines that there is a luma range to process, the process  2300  returns to the operation  2310  to process any remaining luma ranges. Some embodiments use some or all of the calculations of the previously processed luma ranges when processing (e.g., determining the black balance and white balance operations) remaining luma ranges. For example, since the luma level at (or near) the top level of a particular luma range is the same or similar to a luma level at (or near) the bottom level of another adjacent luma range, some embodiments use the white balance operation (e.g., the calculated average CbCr component value) determined for the particular luma range as the black balance operation for the other luma range. 
     Referring to  FIG. 17  as an example, some embodiments might calculate an average CbCr component value based on pixels in the target image that have luma component values equal to 2.5 (i.e., between luma level  2  and luma level  3 ). Since this luma level is at (or near) the top of luma range  1  and at (or near) the bottom of luma range  2 , some embodiments use the calculated average CbCr component value for luma level  2 . 5  as the white balance of luma range  1  and the black balance of luma range  2 . 
     As mentioned, the process  2300  determines the black balance operation based on the distribution of CbCr component values of pixels that have luma component values equal to the bottom of a luma range and white balance operations based on the distribution of CbCr component values of pixels that have luma component values equal to the top of the luma range. However, in some embodiments, the black balance operation is determined based on the distribution of CbCr component values of pixels that have luma component values near the bottom of a luma range and white balance operations is determined based on based on the distribution of CbCr component values of pixels that have luma component values near the top of the luma range. 
     For instance, some embodiments determine such operations based on the distribution of CbCr component values of pixels that have luma component values within 2 luma levels of an end of a luma range. Referring to  FIG. 17 , such embodiments determine the white balance operation of the first luma range (and/or black balance operation of the second luma range) based on the distribution of CbCr component values of pixels that have luma component values in luma levels  1 - 4 , the white balance operation of the second luma range (and/or black balance operation of the third luma range) based on the distribution of CbCr component values of pixels that have luma component values in luma levels  4 - 7 , and the white balance operation of the third luma range (and/or black balance operation of the fourth luma range) based on the distribution of CbCr component values of pixels that have luma component values in luma levels  7 - 10 . 
     While  FIGS. 23 and 24  illustrate determining black balance and white balance operations based on CbCr component values of pixels, some embodiments determine such operations based on other types of component values of the pixels that represent the pixels&#39; colors. For instance, some such embodiments of the process  2300  determine black balance and white balance operations based on a hue component value while other such embodiments of the process  2300  determine the operations based on red, green, and blue component values. 
     3. Determining Saturation Operations 
     As mentioned above, some embodiments determine saturation operations to match the saturation of the target image to the saturation of the source image. As described with respect to process  1600 , gain and lift operations and black balance and white balance operations are applied to the target image before it is used to determine the saturation operations in some embodiments. Thus, at this point, each of the luma ranges of the target image has the same range of luma levels as the corresponding luma range of the source image. 
       FIG. 26  conceptually illustrates a process  2600  of some embodiments for determining saturation operations. As noted above, the process  2600  is performed by the process  1600  of some embodiments (e.g., at the operation  1650 ). The process  2600  begins by identifying (at  2610 ) a luma range of the target image. In some embodiments, the luma range is a luma range identified by the process  1200 , as described above by reference to  FIG. 12 . 
     The process  2600  then determines (at  2620 ) the distribution of saturation values of pixels in the target image that have luma component values that are within a luma range of the target image.  FIG. 27  illustrates histograms  2710  and  2720  of example distributions of saturation values. Specifically, this figure illustrates the histogram  2710  of an example distribution of saturation component values of pixels in the target image that have luma component values within the luma range. For this example, the horizontal axis of the histogram  2710  represents different saturation component values. Different embodiments define the saturation component value of an image to represent a different number of saturation component values. For example, some of these embodiments define the saturation component to represent 256 possible saturation component values (e.g., 0-255, -127 to 128, etc.). Other embodiments define the saturation component to represent any number of possible saturation component values. Furthermore, the vertical axis of the histogram  2710  represents the number of pixels in the target image that have a particular saturation component value. 
     Next, the process  2600  calculates (at  2630 ) the saturation component value associated with a predefined percentile of the distribution of the saturation component values determined at operation  2610 . In some embodiments, the predefined percentile is 90 percent. However, other embodiments can define the predefined percentile to be any number of different percentiles (e.g., 70 percent, 80 percent, 95 percent, etc.). 
     Continuing with the example illustrated in  FIG. 27 , the calculated predefined percentile of the distribution of saturation component values displayed in the histogram  2710  is indicated in the histogram  2710 . For this example, the predefined percentile is 90 percent. As such, 90 percent of the pixels in the target image have saturation component values that are less than or equal to the saturation component value (not shown) indicated in the histogram  2710 . 
     The process  2600  then determines (at  2640 ) the distribution of saturation values of pixels in the source image that have luma component values that are within the corresponding luma range of the source image. Referring to  FIG. 27 , this figure further illustrates the histogram  2730  of an example distribution of saturation component values of pixels in the source image that have luma component values within the corresponding luma range. Similar to the histogram  2710 , the horizontal axis of the histogram  2730  represents different saturation component values, and the vertical axis of the histogram  2730  represents the number of pixels in the source image that have a particular saturation component value. 
     Next, the process  2600  calculates (at  2650 ) the saturation component value associated with the predefined percentile of the distribution of the saturation component values determined at operation  2630 . Continuing with the example illustrated in  FIG. 27 , the calculated predefined percentile of the distribution of saturation component values indicated in the histogram  2720 . As noted above, the predefined percentile is 90 percent in this example. Thus, 90 percent of the pixels in the source image have saturation component values that are less than or equal to the saturation component value (not shown) indicated in the histogram  2730 . 
     The process  2600  then determines (at  2660 ) the saturation operations for matching the saturation of target image to the saturation of source image based on the calculated saturation component values of the predefined percentiles. In some embodiments, the saturation operations match the saturation of the target image to the saturation of the source image by adjusting the saturation component values of pixels in the target image that have luma component values within the luma range by an amount such that the saturation component value associated with the predefined percentile of the distribution of the saturation component values of pixels in the target image matches the saturation component value associated with the predefined percentile of the distribution of the saturation component values of pixels in the source image. In addition, some embodiments represent the saturation lift operations determined for a luma range using a transformation matrix that matches the contrast of the luma range of the target image to the contrast of the luma range of the source image. 
     Referring back to  FIG. 27 , this figure further illustrates an example of saturation operations that match the saturation of pixels in a luma range of the target image to the saturation of pixels in the luma range of the source image. In particular,  FIG. 27  illustrates increasing the saturation of pixels of a luma range of a target image to match the saturation component value associated with the 90 percentile of the target image to the saturation component value associated with the 90 percentile of the source image, as indicated by an arrow in the histogram  2720 . 
     Finally, the process  2600  determines (at  2670 ) whether any luma range (e.g., a luma range of the target image) is left to process. When the process  2600  determines that there is a luma range to process, the process  2600  returns to the operation  2610  to process any remaining luma ranges. Otherwise, the process  2600  ends. 
     iii. Blending of Transforms 
     To reduce or eliminate sharp transitions among transforms of luma levels near the border of luma ranges, some embodiments blend transforms in order to smooth out these sharp transitions. As described with respect to the process  1000 , some embodiments perform a blending operation on transforms after the transforms are determined.  FIG. 28  conceptually illustrates a process  2800  of some embodiments for blending transforms. As described above, the process  2800  is performed by the process  1000  of some embodiment (e.g., at the operation  1050 ). The process  2800  will be described by reference to  FIG. 29 , which illustrates an example of blending a transform associated with a luma level of the target image. In this example, a transformation matrix is associated with each luma level, similar to the example described above by reference to  FIG. 19 . As shown, transformation matrix  2910  is associated with luma level  11 , transformation matrix  2920  is associated with luma level  12 , and transformation matrix  2930  is associated with luma level  13 . Furthermore, 3×4 transformation matrices are used to represent the transforms in this example, as shown in  FIG. 29 . 
     The process  2800  begins by identifying (at  2810 ) a luma level of the target image. Referring to  FIG. 29 , luma level  12  of the target image is the luma level identified for this example. Since luma level  12  and luma level  13  are each part of different luma ranges, in some embodiments, the transformation matrices are also different and, thus, include different values. In addition, the transformation matrix  2910  associated with luma level  11  might include different values than the values of the transformation matrix  2920  associated with luma level  12 . For instance, the transformation matrix  2910  associated with luma level  11  might have been previously blended (e.g., with the transformation matrices associated with luma level  10  and luma level  12 ). 
     Next, the process  2800  identifies (at  2820 ) matrix associated with the identified luma level and the transformation matrices associated with neighboring luma levels. In this example, the neighboring luma levels are luma level  11  and luma level  13 . As shown, the 3×4 transformation matrix  2910  associated with luma level  11  includes the values A 1 -A 12 , the 3×4 transformation matrix  2920  associated with luma level  12  includes the values B 1 -B 12 , and the 3×4 transformation matrix  2930  associated with luma level  13  includes the values C 1 -C 12 . 
     The process  2800  then calculates (at  2830 ) the average of the values of the identified matrices. Referring to  FIG. 29 , the average of the values of the identified matrices are calculated by adding the corresponding values of each matrix and dividing by the number of matrices (three in this example). For instance, the blended transformation matrix  2940  illustrates that the average value of the values in the first column and first row of the transformation matrices is A 1 +B 1 +C 1 /3. As shown, the average values of the other values in the transformation matrices are calculated in a similar manner for the blended transformation matrix  2940 . 
     After calculating the average values of the identified matrices, the process  2800  associates (at  2840 ) the calculated average values with the transformation matrix associated with the identified luma level. For the example illustrated by  FIG. 29 , the calculated average values are associated with the blended transformation matrix  2940 , which is associated with luma level  12 . As such, the calculated average values are now the values of the transformation matrix associated with luma level  12 . 
     Finally, the process  2800  determines (at  2850 ) whether any luma range of the target image is left to process. When the process  2800  determines that there is a luma range to process, the process  2800  returns to the operation  2810  to process any remaining luma ranges. Otherwise, the process  2800  ends. 
     By averaging the values of the transformation matrix of a luma level with the transformation matrices of the luma level&#39;s neighboring luma levels, any sharp transitions (e.g., near the borders of luma ranges) among the transformation matrices is reduced by smoothing out the values of the transformation matrices. Further, some embodiments even repeat the process  2800  a predefined number of times to smooth the transitions among the transformation matrices even further. For instance, some of these embodiments repeat the process  2800  a predefined 32 times. The predefined number of times to repeat the process  2800  can be defined as any number in other embodiments. 
     While the example illustrated by  FIG. 29  and described with respect to the process  2800  shows averaging the values of the transformation matrix of the identified luma level with the transformation matrices of the luma levels immediately neighboring the identified luma level, different embodiments average the values of the transformation matrix of the identified luma level with the transformation matrices of a different number of neighboring luma levels. For instance, some embodiment average the values of the transformation matrix of the identified luma level with the transformation matrices of luma levels within two neighbors on each side of the identified luma level. Referring to  FIG. 29 , some such embodiments would average the values of the transformation matrix of luma level  12  with the transformation matrices of luma levels  10 ,  11 ,  13 , and  14 . 
     Furthermore, some embodiments might not perform the operations  2810 - 2840  for each luma level of the target image. Instead, some embodiments perform the operations  2810 - 2840  for a number of luma levels near the border of adjacent luma ranges. For instance, some such embodiments perform the operations  2810 - 2840  for luma levels immediately adjacent to a border of adjacent luma ranges. Referring to  FIG. 29  as an example, these embodiments would perform the operations  2810 - 2840  for luma levels  6 ,  7 ,  9 ,  10 ,  12 , and  13 . Other embodiments perform the operations  2810 - 2840  for luma levels within a defined number of luma levels (e.g., 2, 3, 4) from the border of adjacent luma ranges. For instance, in embodiments where the luma component is defined to represent 256 different luma levels (e.g., 0-255), these embodiments would blend the transformation matrix associated with a particular level with the transformation matrixes of 4 neighboring luma levels (e.g., 2 below and 2 above the particular luma level). In such cases, the transformation matrix associated with the particular luma level would not blend with transformation matrices associated with luma levels across multiple luma ranges as might be the case in the example illustrated in  FIG. 29 . 
     B. Matching Colors of Images 
     As described with respect to the process  300 , some embodiments apply transforms to the target image after the transforms for matching the colors of the target image to the colors of the source image are determined.  FIG. 30  conceptually illustrates a process  3000  of some such embodiments for applying transforms to a target image to match the colors of the target image to the colors of the source image. As mentioned above, the process  3000  is performed by the process  300  of some embodiment (e.g., at the operation  340 ). The process  3000  starts by identifying (at  3010 ) a pixel in the target image. 
     Next, the process  3000  determines (at  3020 ) the luma component value of the identified pixel. As mentioned above, for an image defined in a Y′CbCr color space, some embodiments represent the brightness of pixels in an image using a luma (Y′) component value (and chrominance values for representing the pixel&#39;s chrominance). In such embodiments, the luma component value of the identified pixel is already determined. However, for an image defined in other color spaces, such as an RGB color space, some embodiments apply a transform to determine the luma component value of a pixel in the image. In other embodiments, the image is converted to a color space that uses a luma component value (e.g., a Y′CbCr color space) before starting the process  3000 . 
     The process  3000  then identifies (at  3030 ) the transformation matrix associated with the determined luma component value. As described above by reference to  FIG. 19 , a transformation matrix is associated with each luma level of the target image in some embodiments. 
     After identifying the transformation matrix, the process  3000  applies (at  3040 ) the transformation matrix to the identified pixel to modify its color and brightness based on the transformation matrix.  FIG. 31  illustrates an example of determining new values for a pixel of a target image. For this example, the pixels of the target image are defined in an RGB color space. As such, each pixel in the target image includes a red component value, a blue component value, and a green component value, as mentioned above. As shown, the transformation matrix in this example is a 3×4 transformation matrix. The transformation matrix and a 4×1 matrix that includes the red, green, and blue component values of the pixel and a constant, K, are multiplied. In some embodiments, K is defined as 1. In other embodiments, K is defined as another value (e.g., −1, 0, etc.). In addition, the calculation of the new value of each of the red, green, and blue components is also shown in  FIG. 31 . 
     Finally, the process  3000  determines (at  3050 ) whether any pixel in the target image is left to process. When the process  3000  determines that there is a pixel in the target image left to process, the process  3000  returns to the operation  3010  to process any remaining pixels in the target image. When the process  3000  determines that there is not a pixel in the target image left to process, the process  3000  ends. At this point, the colors of the target image are matched to the colors of the source image. 
     II. Color Matching Based on Hue 
     The sections above describe various examples and embodiments of a technique for matching colors of a target image to colors of a source image based on the images&#39; luma. Another technique mentioned above employs a hue-based approach to match the colors of a target image to the colors of a source image. In particular, some embodiments of this hue-based technique identify the dominant hues in the target image and the source image and match the colors of the target image to the colors of the source image based on these identified dominant hues. The following sections will illustrate various examples and embodiments of a color matching tool that matches colors of a target image to colors of a source image based on the images&#39; hues. 
     A. Matching Hue Ranges 
     As described by reference to process  600 , some embodiments analyze the images based on the images&#39; hues in order to identify matching dominant hues in the images.  FIG. 32  conceptually illustrates a process  3200  of some embodiments for analyzing the target image and the source image based on the images&#39; hue in order to identify matching dominant hues in the images. 
     The process  3200  begins by determining (at  3210 ) the hue component values of pixels in the target image and the hue component values of pixels in the source image. Different embodiments determine the hue component values of pixels in an image differently. For instance, some embodiments convert the target image and the source image to a color space that uses a hue component to represent pixels. Examples of such color spaces include a hue, saturation, and lightness (HSL or HLS) color space and a hue, saturation, and brightness (HSB) color space, among other types of color spaces. In other embodiments, a transform or equation is applied to the pixels of the image based on the color space in which the image is defined in order to determine the pixels&#39; hue component values. 
     Next, the process  3200  determines (at  3220 ) the distribution of hue component values of pixels in the target image and the distribution of hue component values of pixels in the source image.  FIG. 33  illustrates a histogram  3310  of an example distribution of hue component values of pixels in an image. For this example, the horizontal axis of the histogram  3310  represents different hue levels (not shown). Different embodiments define the hue component to represent a different number of hue component values. For example, some embodiments define the hue component to represent 256 possible hue component values (e.g., 0-255, -127 to 128, etc.). In other embodiments, the hue component is defined to represent any number of possible hue component values. In addition, the vertical axis of the histogram  3310  represents the number of pixels in the image that have a particular hue component value. 
     The process  3200  then filters (at  3230 ) the distribution of the hue component values of the target image and the source image. In some embodiments, the distribution of the hue component values is filtered because the distribution may be uneven and/or have sharp transitions along different hue component values of the distribution. For example,  FIG. 33  illustrates a histogram  3310  of an example distribution of hue component values that is uneven and has such sharp transitions. Some embodiments filter the distribution of the hue component values by performing a neighbor-averaging technique to the distribution.  FIG. 35 , which is described in further detail below, conceptually illustrates a process of some embodiments that performs such neighbor-averaging technique.  FIG. 34  illustrates a histogram  3410  of the example distribution of hue component values illustrated in  FIG. 33  after the distribution has been filtered according to some embodiments of the invention. As shown, the distribution of hue component values is smoother and no longer has sharp transitions as shown in  FIG. 33 . 
     After filtering the distributions, the process  3200  identifies (at  3240 ) dominant hue ranges (e.g., ranges of hue component values) based on the filtered distributions of hue component values of the target image and the source image. In some embodiments, a range of hue component values that has a relatively large distribution of pixels is identified as a dominant hue range.  FIG. 37 , which will be described in more detail below, conceptually illustrates a process of some embodiments for identifying dominant hue ranges. 
     Finally, the process  3200  matches (at  3250 ) dominant hue ranges in the source image with dominant hue ranges in the target image that are similar to the dominant hue ranges in the source image. Differently embodiments match dominant hue ranges in the images based on different factors. For instance, some embodiments match dominant hue ranges in the images based on similarities between the height of the hue ranges, the width of the hue ranges, and the amount of common hue component values that are included in the dominant hue ranges. Other embodiments use different and/or additional factors when matching dominant hue ranges in the source image with dominant hue ranges in the target image.  FIG. 40 , described in further detail below, conceptually illustrates a process of some embodiments for matching dominant hue ranges in the source image and the target image. 
     i. Filtering Distributions of Hue 
     In some instances, a distribution of hue component values of an image might have sharp transitions or unevenness along the distribution. As mentioned above, some embodiments filter the distributions of hue component values of the target image and the source image in order to reduce any unevenness and/or sharp transitions to smooth out the distributions. For instance, some of these embodiments use a neighbor-averaging technique to filter the distributions of the images.  FIG. 35  conceptually illustrates a process  3500  of some embodiments that performs such filtering. In some embodiments, the process  3500  is performed by the process  3200  (e.g., at the operation  3230 ), as mentioned above by reference to  FIG. 32 . For instance, some such embodiments perform the process  3500  on the distribution of hue component values of target image and on the distribution of hue component values of source image. The process  3500  starts by identifying (at  3510 ) a hue component value. As mentioned above, a hue component value represents a hue or color in some embodiments. 
     Next, the process  3500  identifies (at  3520 ) the number of pixels in the distribution that have the identified hue component value. The process  3500  also identifies (at  3520 ) the number of pixels in the distribution that have hue component values that neighbor the identified hue component value.  FIG. 36  illustrates an example of an identified hue component value and the hue component values that neighbor the identified hue component value. In particular,  FIG. 36  illustrates the histogram  3310  of the example distribution of hue component values illustrated in  FIG. 33 , but further illustrates an enlarged portion of the histogram  3310 . As shown, the enlarged portion includes the distributions of pixels along several hue component values. For this example, the identified hue component value is H. Thus, the neighbors of the identified hue component value are H−3, H−2, H−1, H+1, H+2, H+3, etc. 
     Different embodiments identify a different number of distributions of neighboring hue component values. For example, some embodiments identify two distributions of neighboring hue component values. Referring to  FIG. 36  for this example, these embodiments would identify the number of pixels in the distribution at hue component value H, the number of pixels in the distribution at hue component value H−1, and the number of pixels in the distribution at hue component value H+1. 
     The process  3500  then calculates (at  3530 ) the average number of identified numbers of pixels. Continuing with the example, the average of the number of pixels in the distribution at hue component values H−1, H, and H+1 is calculated. 
     Next, the process  3500  associates (at  3540 ) the calculated average number of pixels with the identified hue component value. That is, the number of pixels at the identified hue component value is now the calculated average number of pixels (e.g., instead of the actual number of pixels in the image that have the identified hue component value). 
     At  3550 , the process  3500  determines whether any hue component value in the distribution of pixels in the image is left to process. When the process  3500  determines that there is a hue component value to process, the process  3500  returns to the operation  3510  to process any remaining hue component values. When the process  3500  determines that there is not a hue component value left to process, the process  3500  ends. 
     After filtering the distribution of hue component values of the image, the distribution is smoother and less uneven. Some embodiments further smooth the distribution by repeating the process  3500  a predefined number of times. For instance, some such embodiments perform the process  3500  on the target image 32 times and on the source image 32 times. Other embodiments may repeat the process  3500  on the images any number of different times. 
     Although the example illustrated by  FIG. 36  and described with respect the process  3500  averages the number of pixels in the distribution that have the identified hue component value with the number of pixels in two distributions of neighboring hue component values, other embodiments average the number of pixels in the distribution that have the identified hue component value with a different number of distributions of neighboring hue component values. For example, some embodiment average the number of pixels in the distribution that have the identified hue component value with four distributions of neighboring hue component values. Referring to  FIG. 36  again, the number of pixels in the distributions at hue component values H−2, H−1, H, H+1, and H+2 are averaged. 
     ii. Identifying Dominant Hues 
     In order to match dominant hues of a target image to dominant hues of a source image, some embodiments identify dominant hue ranges of the images. Different embodiments identify dominant hue ranges in an image differently. For example, some embodiments identify dominant hue ranges in an image based on the distribution of hue component values of pixels in the image. The following  FIG. 37  illustrates an example of identifying dominant hue ranges in an image based on the distribution of hue component values of pixels in the image. In particular,  FIG. 37  conceptually illustrates a process  3700  of some embodiments for identifying dominant hue ranges in a distribution of hue component values in an image. In some embodiments, the process  3700  is performed by the process  3200  (e.g., at the operation  3240 ), as described above by reference to  FIG. 32 . The process  3700  will be described by reference to  FIG. 38 , which illustrates examples of different stages of identifying dominant hue ranges in the histogram  3410  of the filtered distribution illustrated in  FIG. 34 . 
     The process  3700  begins by identifying (at  3710 ) samples of distributions of hue component values of an image (e.g., the target image or the source image). In some embodiments, the process  3700  identifies two samples of distributions at two neighboring hue component values. Referring to  FIG. 36  as an example, process  3700  of such embodiments identifies the distributions at hue component values H and H+1. In other embodiments, the process  3700  identifies a different number of samples of distributions of neighboring hue component values (e.g., distributions at hue component values H, H+1, and H+2). 
     Next, the process  3700  determines (at  3720 ) the slope of a line formed by the distribution values associated with the identified samples. Referring to  FIG. 36  again, the slope of line formed by the distribution values associated with the distributions at hue component values H and H+1 has a negative slope. As another example, the slope of line formed by the distribution values associated with the distributions at hue component values H−3 and H−2 has a positive slope. 
     The process  3700  then determines (at  3730 ) whether the slope is positive. When the process  3700  determines that the slop is positive, the process continues to operation  3740 . Otherwise, the process proceeds to operation  3795 . 
     As shown,  FIG. 38  illustrates examples of identifying dominant hue ranges in an image based on the histogram  3410  at four different stages  3810 - 3840 . The first stage  3810  illustrates a portion of the filtered distribution of the histogram  3410  where samples have been identified, as indicated by an arrow. In this stage, a line formed based on the samples is determined to be positive. 
     At  3740 , the process  3700  identifies the boundary of a dominant hue range based on the samples. Some embodiments use the hue component value of one of the identified samples. Referring to  FIG. 36  as an example, when the identified samples being processed are the distributions at hue component values H and H+1, some embodiments use the hue component value H as the boundary of the dominant hue range while other embodiments use the hue component value H+1 as the boundary of the dominant hue range. Referring back to the first stage  3810 , the boundary of a dominant hue range (e.g., a hue component value) is identified near the location at which the arrow indicates. 
     Next, the process  3700  identifies (at  3750 ) samples of distributions of hue component values of an image. The operation  3750  is similar to the operation  3710  except the operation  3750  traverses the distribution of hue component values and identifies the next samples of distributions of hue component values. Referring to  FIG. 36 , when the operations  3710  identifies the distributions at hue component values H and H+1, then the operation  3750  of some embodiments identifies the distributions at hue component values H+1 and H+2. 
     The process  3700  then determines (at  3760 ) the slope of the line formed based on the values associated with the samples identified at operation  3750 . Operation  3760  is the same or similar to the operation  3720  described above. At this operation, the process  3700  determines the slope of a line formed by the distribution values associated with the samples identified at operation  3750 . 
     At  3770 , the process  3700  determines whether the slope is flat. In some embodiments, the slope of the line is flat when the line has a slope of zero. In other embodiments, the slope is flat when the slope of the line is within a predefined threshold (e.g., −0.1 to 0.1). When the process  3700  determines that the slope of the line is not flat, the process  3700  returns to the operation  3750  to continue traversing the distribution of hue component values and identifying samples of distributions of hue component values until the slope of the line of samples is determined to be flat. Referring to  FIG. 38 , the second stage  3820  shows a portion of the filtered distribution of the histogram  3410  where the line formed by samples, indicated by an arrow, is flat. 
     When the process  3700  determines that the slope of the line is flat, the process  3700  identifies (at  3780 ) the middle of the dominant hue range. Similar to operation  3740 , some embodiments of operation  3780  use the hue component value of one of the identified samples as the middle of the dominant hue range. Referring to  FIG. 38 , the second stage  3820  illustrates the middle of the dominant hue range (e.g., a hue component value) is identified near the location at which the arrow indicates. 
     After identifying the middle of the dominant hue range, the process  3700  determines (at  3790 ) the dominant hue range based on the determined boundary and middle. In some embodiments, the dominant hue range is determined by determining the distance between the middle and the identified boundary (e.g., the left length) and then identifying the hue component level that is the same distance from the middle in the opposite direction in order to identify the other boundary of the dominant hue range. Referring to  FIG. 38 , the third stage  3830  illustrates a dominant hue range determined based on the boundary of the dominant hue range determined in the first stage  3810  and the middle of the dominant hue range determined in the second stage  3820 . As shown, the third stage  3830  also illustrates a dashed triangle by sides extending from distribution of the middle of the hue region to the bottom of distributions of the boundaries a 1  and b 1 . As such, the distances from hue component value of the middle of the hue region to the hue component value of the boundary a 1  and the hue component value of the boundary b 1  are approximately the same. 
     Finally, the process  3700  determines (at  3795 ) whether any samples are left to process. When the process  3700  determines that there are samples left to process, the process  3700  return to operation  37010  to traverses the distribution of hue component values and identify additional dominant hue ranges in the distribution of hue component values in the image. When the process  3700  determines that there are samples left to process, the process  3700  ends. 
     The fourth stage  380  illustrated in  FIG. 38  shows the histogram  3410  after the process  3800  has identified dominant hue ranges. As shown, the identified dominant hue ranges have boundaries of a 1  and b 1 , a 2  and b 2 , and a 3  and b 3 . A dashed triangle is illustrated in a similar fashion as described above by reference to the third stage  3830  for each of the dominant hue ranges. 
     While  FIG. 37  illustrates determining a dominant hue range by determining the distance from a boundary to the middle of the hue range (e.g., the left length), some embodiments determine the dominant hue range by further determining the distance from the middle of the hue range to the other boundary of the hue range (e.g., the right length). For example, in such embodiments, the process  3700  continues to process samples along the distribution of hue component values until the slope of line formed by the samples transitions from negative to flat. 
     Referring to  FIG. 38  as an example, the process  3700  in these embodiments processes the samples of the distribution of hue component values after the middle of the hue range is identified in the second stage  3820  to determine the distance from the middle of the hue range to the boundary on the right side of the hue range where the slope of line formed by the samples transitions from negative to flat. Some embodiments determine the hue range by determining the distance between the middle and the other identified boundary (e.g., the right length) and then identify the hue component level that is the same distance from the middle in the opposite direction in order to identify the other boundary of the dominant hue range. Other embodiments determine the hue range differently. For instance, the process  3700  of some of these embodiments averages the identified distances (e.g., the left length and the right length) while the process  3700  of other of these embodiments use a percentage of the averaged distances (e.g., 60 percent, 70 percent, 80 percent, etc.) 
     In some embodiments, the hue ranges identified by the process  3800  are further processed in order to filter out hue ranges that have a peak distribution of hue component values that is not greater than a predefined threshold.  FIG. 39  illustrates an example of such filtering of dominant hue ranges based on an example predefined threshold. This figure shows the histogram  3910  illustrated in the fourth stage  3840  of  FIG. 38  with a bolded line representing a predefined threshold in terms of a number of pixels. As shown, the peak (e.g., top) of the hue range with boundaries a 1  and b 1  is higher than the bolded line. As such, this hue range has a peak distribution of hue component values that is greater than the predefined threshold in this example. Similarly, the hue range with boundaries a 3  and b 3  has a peak distribution of hue component values that is greater than the predefined threshold. However, the peak of the hue range with boundaries a 2  and b 2  is not higher than the bolded line, as illustrated in  FIG. 39 . Thus, this hue range has a peak distribution of hue component values that is not greater than the predefined threshold and is filtered out. Histogram  3920  illustrates the histogram  3910  after filtering out hue ranges that have a peak distribution of hue component values that is not greater than the predefined threshold represented by the bolded line. While the example illustrated in  FIG. 39  illustrates one predefined threshold, different embodiments define different values (e.g., a threshold number of pixels) for the predefined threshold. 
     iii. Identifying Matching Dominant Hues 
     After dominant hue ranges in the target image and the source image are identified, some embodiments identify matching dominant hues in the images, as noted above. In some embodiment matching dominant hues are hues in the target image that are determined to be similar to dominant hues in the source image.  FIG. 40  conceptually illustrates a process  4000  of some embodiments for identifying dominant hue ranges in the source image that match dominant hue ranges in the target image. As mentioned above, the process  4000  is performed by the process  3200  of some embodiments (e.g., at the operation  3250 ). 
     The process  4000  will be described by reference to  FIGS. 41-44 , which illustrate several examples of identifying dominant hue ranges in the target image with dominant hue ranges in the source image.  FIG. 41  illustrates an example of dominant hue ranges in a target image and in a source image. In particular,  FIG. 41  illustrates four dominant hue ranges in a source image that are identified in some embodiments (e.g., by the processes illustrated by reference to  FIGS. 38 and 39 ). This figure also shows the dominant hue ranges illustrated in  FIG. 39  as dominant hue ranges of a target image.  FIG. 42  illustrates the most similar dominant hue range in a target image of each dominant hue range in a source image.  FIGS. 43 and 44  illustrate examples of identifying a dominant hue range in the source image that matches a dominant hue range in a target image.  FIG. 45  illustrates an example of matching dominant hues in the target image and the source image that are identified by the process  4000 . 
     The process  4000  begins by determining (at  4010 ) the most similar unmatched dominant hue range in the target image for each unmatched dominant hue range in the source image. In some embodiments, when the process  4000  first begins, all the dominant hue ranges in the target image and source image are unmatched hue ranges. As noted above, some embodiments determine the similarity between a dominant hue range in the target image with a dominant hue range in the source image based on several criteria. For instance, some of these embodiments consider the peak distributions of pixels of the hue ranges (e.g., the height of the hue ranges), the number of different hue component values in the hue ranges (e.g., the width of hue ranges), and the amount of hue component values that the dominant hue ranges share (i.e., the intersection of the hue ranges).  FIG. 46 , which will be described in more detail below, conceptually illustrates a process of some embodiments for determining the similarity between a hue range in the target image and a hue range in the source image based on such criteria. 
       FIG. 42  illustrates an example of hue ranges in the target image illustrated in  FIG. 41  that are determined (e.g., by the process described below by reference to  FIG. 46 ) to be the most similar hue range of each dominant hue range in the source image illustrated in  FIG. 41 . As indicated by an arrow, the hue range in the target image with the range a 1 -b 1  is determined as the most similar hue range of the hue range in the source image with the range c 1 -d 1 . In addition, the hue range in the target image with the range a 2 -b 2  is determined as the most similar hue range of both the hue range in the source image with the range c 3 -d 3  and the hue range in the source image with the range c 4 -d 4 , which are also indicated by arrows. 
     In some cases, none of the unmatched dominant hue ranges in the target image are determined to be similar to an unmatched dominant hue range in the source image. In such cases, the unmatched dominant hue range in the source image is not considered for the remainder of the process  4000 . Referring to  FIG. 42  as an example, none of the dominant hue ranges in the target image are determined (e.g., by the process described below by reference to  FIG. 46 ) to be similar to the dominant hue range in the source image with the range c 2 -d 2 . For this example, this dominant hue range in the source image is not considered for the remainder of the process  4000 . 
     Next, the process  4000  identifies (at  4020 ) an unmatched hue range in the target image. The process  4000  then determines (at  4030 ) whether the identified hue range in the target image is determined (at  4010 ) as the most similar hue range of any hue range in the source image. When the process  4000  determines that the identified hue range is not determined as the most similar hue range of any hue range in the source image, the process  4000  proceeds to operation  4070 . Otherwise, the process  4000  continues to operation  4040 . 
     At  4040 , the process  4000  determines whether the identified hue range in the target image is determined (e.g., at  4010 ) as the most similar hue range of more than one hue range in the source image. When the process  4000  determines that the identified hue range in the target image is determined as the most similar hue range of one hue range in the source image, the process  4000  then matches (at  4050 ) the identified hue range in the target image with the hue range in the source image. After the identified hue range in the target image and the hue range in the source image are matched, they are not considered for the remainder of the process  4000 . 
       FIG. 43  illustrates an example of an unmatched dominant hue range in the target image illustrated in  FIG. 41  that is determined as the most similar hue range of one unmatched dominant hue range in the source image illustrated in  FIG. 41 . In this example, the hue range in the target image with the range a 1 -b 1  is determined (e.g., by the process described below by reference to  FIG. 46 ) as the most similar hue range of just the hue range in the source image with the range c 1 -d 1 . Therefore, these hue ranges in the target image and the source image are matched, as indicated by a two-way arrow, and then no longer considered. 
     When the process  4000  determines that the identified hue range in the target image is determined (e.g., at  4010 ) as the most similar hue range of more than one hue range in the source image, the process  4000  proceeds to operation  4060  to resolve the conflict of the identified hue range in the target image being the most similar hue range of multiple unmatched hue ranges in the source image. 
     At  4060 , the process  4000  matches the identified hue range in the target with the hue range in the source image that is determined as the most similar to the identified hue range in the target image. In other words, while the identified hue range in the target image is determined as the most similar hue range of each of the hue ranges in the source image, the process  4000  determines (at operation  4060 ) the hue range in the source image that is the most similar to the identified hue range in the target image and matches that hue range in the source image with the identified hue range in the target image. Since the other hue ranges in the source image are not matched with the identified hue range in the target image, they are still unmatched hue ranges in the source image. 
       FIG. 44  illustrates an example of an unmatched dominant hue range in the target image illustrated in  FIG. 42  that is determined to be the most similar hue range of the two unmatched dominant hue ranges in the source image illustrated in  FIG. 42 . As shown in  FIG. 42 , the hue range in the target image with the range a 2 -b 2  is determined (e.g., by the process described below by reference to  FIG. 46 ) as the most similar hue range of the hue range in the source image with the range c 3 -d 3  and the hue range in the source image with the range c 4 -d 4 . For this example, the hue range in the source image with the range c 4 -d 4  is determined (e.g., by the process described below by reference to  FIG. 46 ) as the most similar (i.e., more similar than the hue range in the source image with the range c 3 -d 3 ) hue range of the hue range in the target image with the range a 2 -b 2 . Therefore, the hue range in the target image with the range a 2 -b 2  and the hue range in the source image with the range c 4 -d 4  are matched, as illustrated in  FIG. 44 , and then no longer considered. Since the hue range in the source image with the range c 3 -d 3  is not matched with the hue range in the target image with the range a 2 -b 2 , it is still an unmatched hue range in the source image. 
     Next, the process  4000  determines (at  4070 ) whether any hue range in the target image is left to process. When the process  4000  determines that there is a hue range in the target image left to process, the process  4000  returns to the operation  4020  to identify matches for any remaining hue ranges in the target image. Otherwise, the process  4000  continues to operation  4080 . 
     Finally, the process  4000  determines (at  4080 ) whether both the target image and source image have unmatched hue ranges left to process. When the process  4000  determines that both the target image and source image have unmatched hue ranges left to process, the process  4000  returns to the operation  4010  to continue processing these unmatched hue ranges in the target image and the source image until there are no more unmatched hue ranges in the target image or there are no more unmatched hue ranges in the source image. When the process  4000  determines that both the target image and source image do not have unmatched hue ranges left to process, the process  4000  ends. 
       FIG. 45  illustrates an example of matching dominant hues in the target image and the source image illustrated in  FIG. 41  that are identified by the process  4000 . As shown, the hue range in the target image with the range a 1 -b 1  is identified as matching the hue range in the source image with the range c 1 -d 1 , and the hue range in the target image with the range a 2 -b 2  is identified as matching the hue range in the source image with the range c 4 -d 4 . 
     As described with respect to the process  4000 , some embodiments determine the similarity between hue ranges in a target image and hue ranges in a source image based on several criteria. The following  FIG. 46  conceptually illustrates a process  4600  of some embodiments for determining the similarity between a hue range in the target image and a hue range in the source image. As mentioned above, the process  4600  is performed by the process  4000  of some embodiment (e.g., at the operation  4010 ). 
     The process  4600  starts by identifying (at  4610 ) a dominant hue range in the source image. The process  4600  then identifies (at  4620 ) a dominant hue range in the target image. In some embodiments, the identified hue range in the source image and the identified hue range in the target image are unmatched hue ranges in a process that identifies matching hue ranges (e.g., the process described above by reference to  FIG. 40 ) based on the similarity between the hue ranges. 
     Next, the process  4600  determines (at  4630 ) the similarity between the identified hue ranges in the source image and the target image based on a set of criteria. For example, some embodiments take into account the peak distribution of pixels of the hue ranges. The peak distribution of pixels of a hue range is, in some embodiments, the largest distribution of pixels that have a particular hue component value in the hue range. In some embodiments, this is referred to as the height of the hue range. Referring to  FIG. 41  as an example, the peaks of the dominant hue ranges in the target image and the source image are indicated by the top of the respective dashed triangles. 
     The following is an equation that some such embodiments use to determine the similarity between the heights of two hue ranges: 
                     height   ⁢           ⁢   similarity     =     1   -              height   src     -     height   tar              height   largest                 (   4   )               
where the height src  is the height of the hue range in the source image, the height tar  is the height of the hue range in the target image, and the height largest  is the height of the greater of the two heights. Based on equation (4), the height similarity is equivalent to 1 when the height of the hue ranges are the same, and the height similarity decreases to 0 as the difference between the hue ranges&#39; heights increases.
 
     As another example, some embodiments consider the number of hue component values in the hue ranges when determining the similarity between hue ranges. That is, these embodiments consider the range of hue component values of the hue ranges. In some embodiments, this is referred to as the width of the hue range. Referring again to  FIG. 41  as an example, the range of hue component values of the dominant hue ranges in the target image and the source image are indicated by the vertical indicators and labels along the x-axes (e.g., c 1 -d 1 , c 2 -d 2 , a 1 -b 1 , etc.). 
     The following is an equation that some such embodiments use to determine the similarity between the widths of two hue ranges: 
                     width   ⁢           ⁢   similarity     =     1   -              width   src     -     width   tar              width   largest                 (   5   )               
where the width src  is the width of the hue range in the source image, the width tar  is the width of the hue range in the target image, and the width largest  is the width of the greater of the two widths. According to equation (5), the width similarity is equivalent to 1 when the width of the hue ranges are the same, and the width similarity decreases to 0 as the difference between the hue ranges&#39; widths increases.
 
     As yet another example, some embodiments base the determination of the similarity between hue ranges on the amount of hue component values that the dominant hue ranges share. In some embodiments, this is referred to as the intersection between the hue ranges.  FIG. 47  illustrates an example intersection between hue ranges. As illustrated in this figure, the intersection between the hue range in the source image with the range c 3 -d 3  and the hue range in the target image with the range a 2 -b 2  is the range of hue component values form a 2  to d 3  as indicated by the arrow. 
     The following equation is an equation that some such embodiments use to determine the similarity between the intersection of two hue ranges: 
                     intersection   ⁢           ⁢   similarity     =            intersection   ⁢           ⁢   width            width   smallest               (   6   )               
where the intersection width is the width of the intersection between the hue ranges, and the width smallest  is the width of the smaller of the hue ranges&#39; widths. Based on equation (6), the intersection similarity is equivalent to 1 when the intersection width of the hue ranges is the same as the width of the smaller of the hue ranges&#39; widths, and the intersection similarity decreases to 0 as the intersection width between the hue ranges&#39; widths decreases. In addition, some embodiments determine hue ranges as not similar when the intersection similarity is 0. That is, the hue ranges do not share any hue component values.
 
     In determining the similarity between hue ranges based on a set of criteria, some embodiments apply weights to the different criteria. Using the criteria described above (i.e., height, width, and intersection of hue ranges) as an example, some such embodiments may apply equal weighting to such criteria. As such, the height similarity has a weight of 1, the width similarity has a weight of 1, and the intersection similarity has a weight of 1. However, other such embodiments may apply different weight amounts to the different criteria. 
     Furthermore, some embodiments apply a predefined similarity threshold when determining the similarity between hue ranges. In such embodiments, hue ranges are determined as not similar when a similarity value that represents the similarity between hue ranges that does not pass the predefined similarity threshold. For example, using the equations (4)-(6) for the criteria described above and an equal weighting of 1 for each criteria, the similarity between hue ranges is represented by a value ranging from 0-3. When a value of 1.2 is defined as the similarity threshold, hue ranges that have a similarity value less than 1.2 are determined as not similar. Other embodiments may use different ranges of similarity values and different similarity thresholds. 
     Returning to  FIG. 46 , the process  4600  then determines (at  4640 ) whether the identified hue range in the target image is the most similar hue range of the hue range in the source image. When process  4600  determines that the hue range in the target image is the most similar hue range of the hue range in the source image, the process  4600  identifies (at  4650 ) the hue range in the target image as the most similar hue range of the hue range in the source image. Otherwise, the process  4600  proceeds to operation  4660 . 
     At  4660 , the process  4600  determines whether any hue range in the target image is left to process. When the process  4600  determines that there is a hue range in the target image left to process, the process  4600  returns to the operation  4620  to determine the hue range in the target image that is the most similar hue range of the hue range in the source image. When the process  4600  determines that there is no hue range in the target image left to process, the process  4600  ends. 
     In some cases, the process  4600  determines that none of the unmatched dominant hue ranges in the target image are similar to an unmatched dominant hue range in the source image.  FIG. 48  illustrates an example of such a dominant hue range in the source image. Specifically,  FIG. 48  illustrates a dominant hue range in the source image illustrated in  FIG. 41  that is not similar to any dominant hue range in the target image illustrated in  FIG. 41 . In this example, none of the dominant hue ranges in the target image are determined to be similar to the dominant hue range in the source image with the range c 2 -d 2  because this hue range in the source image does not intersect with any of the hue ranges in the target image. 
     While  FIG. 46  illustrates a process for determining the similarity between a hue range in the target image and a hue range in the source image based on the peak distributions of pixels of the hue ranges, the number of different hue component values in the hue ranges, and the amount of hue component values that the dominant hue ranges share, other embodiments may determine the similarity between the hue ranges based on different and/or additional criteria. For instance, some of these embodiments may determine the similarity between the hue ranges based on the width and height ratio of the hue ranges, the total of the distribution of pixels in the hue ranges (e.g., the integral of or the area under the distribution curve), etc. 
     iv. Determining Transforms 
     After matching hue ranges in the target image and the source image are identified, some embodiments determine a set of transforms for matching the colors of the target image to the colors of the source image based on the identified matching hue ranges in the images. For instance, some embodiments match the colors of pixels in the target image that are within a dominant hue to the colors of pixels in the source image that are within the corresponding matching dominant hue.  FIG. 49  conceptually illustrates a process  4900  of some embodiments for determining such transforms. 
     The process  4900  starts by determining (at  4910 ) hue shift operations based on the identified matching hue ranges in the target image and the source image. In some embodiments, the hue shift operations shift the hue ranges in the target image to align with the corresponding hue ranges in the source image. In some of these embodiments, for each pair of matching hue ranges in the target image and source image, a hue shift operation is determined that shifts the middle of a hue range in the target image to line up with the middle of the hue range in the source image. 
     Next, the process  4900  determines (at  4920 ) gain and lift operations to match the contrast of the target image to the contrast of the source image. In some embodiments, this operation is similar to the process  2000  described above by reference to  FIG. 20  except the boundary luma levels of the hue ranges are used to determine the gain and lift operations. That is, for the pixels in the target image that fall in a particular hue range, the lowest luma level and the highest luma level of such pixels are used. 
     The operation  4930  is similar to the operation  1620  described above by reference to  FIG. 16 , but the operation  4930  also applies the determine hue shift operations in addition to the determined gain and lift operations to the target image. 
     The process  4900  next determines (at  4940 ) black balance and white balance operations to match the colors of the target image to the colors of the source image. The operation  4940  is similar to the process  2300  described above by reference to  FIG. 23 , but the operation  4940  determines the black balance and white balance operations based on the CbCr distributions of pixels at the lowest luma level and highest luma levels of each hue range in the target image and the corresponding hue ranges in the source image. In other words, the black balance and white balance operations match the average CbCr component values of the distributions at the lowest luma level of each hue range in the target image to the average CbCr component values of the distributions at the lowest luma level of the corresponding hue range in the source image. In addition, the average CbCr component values of the distributions at the highest luma level of each hue range in the target image are matched to the average CbCr component values of the distributions at the highest luma level of the corresponding hue range in the source image. 
     After determining the black balance and white balance operations, the process  4900  performs operation  4950 . This operation is similar to the operation  1640  described above by reference to  FIG. 16 , except the determined hue shift operations are applied to the target image as well as the determined gain and lift operations and the determined black balance and white balance operations. 
     The process  4900  determines (at  4960 ) the saturation operations in the same manner as the process  2600  described above by reference to  FIG. 26 , but instead of determining the saturation operations based on luma ranges, the process  4900  of some embodiments determines the saturation operations based on the matching hue ranges in the target image and the source image. Therefore, these embodiments match the saturation of each hue range in the target image to the saturation of the corresponding hue range in the source image. 
     Finally, the process  4900  determines (at  4970 ) transforms to match colors of target image to colors of source image based on the determined hue shift operations, gain and lift operations, black balance and white balance operations, and saturation operation. Some embodiments of the process  4900  determine a set of transforms for each matching pair of hue ranges in the target image and the source image. Thus, the colors of each hue range in the target image are matched to the colors of the corresponding hue range in the source image. 
     Although the  FIG. 49  illustrates a process that determines black balance and white balance operations to match the colors of the target image to the colors of the source image and determines transforms based on the black balance and white balance operations, some embodiments do not determine the transforms based on the black balance and white balance operations. In such embodiments, the process  4900  does not perform operation  4940  and accordingly does not apply black balance and white balance operations to the target image at operation  4950 . 
     Some embodiments use a transformation matrix to represent the set of transforms (e.g., the determined operations) determined for each hue range in the target image. Thus, the determined operations for each hue range are incorporated into a transformation matrix to match the color of pixels in the hue ranges in the target image to the colors of pixels in the corresponding hue ranges in the source image. Furthermore, different embodiments define different transformation matrices with different dimensions to represent the set of transforms for a matching pair of hue range. For instance, some such embodiments define a 3×4 transformation matrix to represent the set of transforms for a luma range. Other such embodiments define a transformation matrix with different dimensions to represent the set of transforms for a luma range. 
     Furthermore, some embodiments associate a transformation matrix with each hue level of the target image. For instance, some of these embodiments define the pixels in the target image (and the source image) to represent 256 different hue levels. Therefore, these embodiments associated a transformation matrix with each of the 256 hue levels. As mentioned, some embodiments determine a set of transforms for each hue range in the target image. In some such embodiments, the transformation matrices associated with hue levels in a hue range are the same (i.e., have the same values). 
     To smooth out any sharp transitions among the transformation matrices, some embodiments of the process  4900  also perform a blending operation on the transformation matrices similar to the one describe above by reference to  FIG. 28 . In these embodiments, the process  2300  blends the transformation matrices on a hue level basis. For instance, in the embodiments that define pixels in the target image to represent 256 different hue levels, the process  2300  averages the values of the transformation matrix of each hue level with the values of the transformation matrices of its neighboring hue levels. 
     B. Matching Colors of Images 
     After determining transforms for matching the colors of a target image to the colors of a source image, some embodiments apply the transforms to the target image in order to match the colors of the target image to the colors of the source image.  FIG. 50  conceptually illustrates a process  5000  of some embodiments for applying transforms to a target image to match the colors of the target image to the colors of the source image. As noted above, the process  5000  is performed by the process  600  of some embodiment (e.g., at the operation  650 ). The process  5000  is similar to the process  3000  describe above by reference to  FIG. 30 , but the process  5000  applies transforms to only pixels in the target image that have a hue component value within a dominant hue range in the target image. 
     The process  5000  starts the same way as the process  3000 . Operation  5010  is the same as the operation  3010  described above. At this operation, the process  5000  identifies a pixel in the target image. The process  5000  then determines (at  5020 ) the hue component value of the pixel. As mentioned above, some embodiments determine the hue component value of the pixel by converting the target image to a color space that uses a hue component to represent pixels (e.g., a HSL, HLS, or HSB color space) while other embodiments determine the pixel&#39;s hue component value by applying a transform or equation to the pixel. 
     Next, the process  5000  determines (at  5030 ) whether the hue component value of the pixel is a value within a dominant hue range in the target image (e.g., identified by the process  3200  described above). When the process  5000  determines that the hue component value of the pixel is a value within a dominant hue range in the target image, the process  5000  proceeds to operation  5040 . 
     Operation  5040  is similar to the operation  3030  described above by reference to  FIG. 30 , but instead of identifying a transformation matrix associated with the pixel&#39;s luma component value, the process  5000  identifies a transformation matrix associated with the pixels&#39; hue component value. 
     Operations  5050  and  5060  are the same as the corresponding operations  3040  and  3050  as described above by reference to  FIG. 30 . At these operations, the process  5000  applies the identified transformation matrix to the pixel and determines whether any pixel in the target image is left to process. When the process  5000  determines that there is a pixel in the target image left to process, the process  5000  returns to the operation  5010  to continue processing the remaining pixels in the target image. Otherwise, the process  5000  ends. After the process  5000  ends, the dominant hues of the target image are matched to corresponding dominant hues of the source image. 
     III. Color Matching Using Color Segmentation 
     In addition to matching colors of a target image to colors of a source image based on the images&#39; luma or the images&#39; hues, some embodiments match the colors of the images by using a color segmentation technique. As noted above, the color segmentation technique of some embodiments identifies a set of color ranges (e.g., color segments or segmented colors) in the target image and identifies the set of color ranges in the source image. Based on the segmented colors, the set of colors of the target image are matched to the set of colors of the source image in some embodiments. 
       FIG. 51  conceptually illustrates a process  5100  of some embodiments for matching colors of a target image to colors of a source image by segmenting the colors of the images. The process  5100  is similar in many ways to the process  700 , described above by reference to  FIG. 7 , but the process  1600  includes additional operations. The process  5100  starts in the same manner as the process  700 . Operation  5110  is the same as described above for operation  710 . At this operation, the process  5100  determines a set of transforms to match characteristics of the target image to the set of characteristics of the source image. As mentioned above, different embodiments of these transforms match different combinations of characteristics of the images, such as the average color of the image, the average color of dark portions of the image, the average color of bright portions of the image, the average saturation of the image, the contrast of the image. In some embodiments, these transforms are represented by a transformation matrix. An example of a process for determining such a set of transforms is described below by reference to  FIG. 52 . 
     Next, the process  5100  applies (at  5120 ) the transforms to the target image to match the set of characteristics of the target image to the set of characteristics of the source image. As mentioned, the transforms are represented by a transformation matrix in some embodiments. In some such embodiments, the process  5100  applies the transforms to the target image by applying the transformation matrix to the each of the pixels in the target image. 
     After the set of characteristics of the target image have been matched to the set of characteristics of the source image, the process  5100  segments (at  5130 ) the colors of the target image and the colors of the source image. Some embodiments segment colors of an image by converting the image to a color space that is optimized for identifying colors in the images.  FIG. 54 , which is described in more detail below, conceptually illustrates a process of some embodiments for segmenting colors of an image by converting the image to such a color space. 
     Next, the process  5100  determines (at  5140 ) a set of transforms to match characteristics of a segmented color of the target image to characteristics of the segmented color in the source image. As mentioned above, different embodiments of these transforms match different combinations of characteristics of the segmented color of the images, such as the average color of the segmented color in the image, the average color of dark portions of the segmented color in the image, the average color of bright portions of the segmented color in the image, the average saturation of the segmented color in the image, the contrast of the segmented color in the image. Some embodiments use a transformation matrix to represent the set of transforms. In some embodiments, the process described below by reference to  FIG. 52  is used to determine the set of transforms. 
     The process  5100  then determines (at  5150 ) whether any segmented color is left to process. When the process  5100  determines that there is a segmented color left to process, the process  5100  returns to the operation  5140  to continue processing any remaining segmented colors. 
     When the process  5100  determines that there is no segmented color left to process, the process  5100  applies (at  5160 ) the transforms to the target image to match the colors of the target image to the colors of the source image. As noted above, the transforms are represented by a transformation matrix. Some such embodiments apply the transforms to the target image by applying the transforms to each pixel in the target image. As described in more detail below,  FIG. 59  conceptually illustrates a process of some embodiments for applying the transforms to the target image in such manner. After applying the transforms to the target image, the process  5100  ends. 
     A. Determining Transforms 
     As mentioned above, some embodiments determine transforms to match characteristics of an image (e.g., a target image) to characteristics another image (e.g., a source image). For instance, the process  5100  illustrated in  FIG. 51  describes determining transforms for matching a set of characteristics of a target image to the set of characteristics of a source image and transforms for matching characteristics of a segment color in the target image to characteristics of the corresponding segmented color in the source image. The following  FIG. 52  illustrates an example of a process that determines such transforms. In particular,  FIG. 52  conceptually illustrates a process  5200  of some embodiments for determining a set of transforms to match characteristics of a target image to characteristics of a source image. As noted above, the process  5200  is performed by the process  5100  of some embodiment (e.g., at the operation  5110  and/or the operation  5140 ). 
     The process  5200  begins by analyzing (at  5210 ) the target image. In some embodiments, the process  5200  analyzes the target image by identifying the values of components that define the pixels in the target image. For example, when the target image is defined in an RGB color space, the process  5200  identifies the red component value, the green component value, and the blue component value of the pixels. As another example, when the target image is defined in an HSL color space, the process  5200  identifies the hue component value, the saturation component value, and the luma (or lightness in some embodiments) component value of the pixels of the target image. 
     Next, the process  5200  determines (at  5220 ) a set of values that represent a set of characteristics of the target image. Different embodiments use different sets of characteristics of the target image. For example, the process  5200  of some embodiments determines a value that represents the average color of the pixels in the target image, the average color of dark pixels (e.g., shadows) in the target image, the average color of bright pixels (e.g., highlights) in the target image, the average saturation of pixels in the target image, the average contrast of pixels in the target image, and whether the target image is monochrome. In some embodiments, the target image is monochrome when each of the pixels in the target image has no saturation. 
     In some embodiments, multiple values are used to represent the average color of the pixels in the target image, the average color of dark pixels (e.g., shadows) in the target image, and the average color of bright pixels (e.g., highlights) in the target image. For instance, when the target image is defined in an RGB color space, three values are used to represents these characteristics: a value for the red component, a value for the green component, and a value for the blue component. As such, these embodiments use twelve values to represent the set of characteristics described above. 
     Some of these embodiments determine whether a pixel is a bright pixel and a dark pixel based on the value of the pixel&#39;s luminance. For instance, some of these embodiments determine that a pixel is a bright pixel when the value of the pixels&#39; luminance passes a threshold luminance value. Similarly, some of these embodiments determine that a pixel is a dark pixel when the value of the pixels&#39; luminance passes a threshold luminance value. In some embodiments, the threshold luminance values are different while, in other embodiments, they are the same. 
     In some embodiments, the process  5200  determines the contrast of the target image based on the distribution of luma component values of the pixels in the target image. Some such embodiments use a linear regression analysis to determine a line based on distribution of luma component values. The slope of this determined line represents the contrast in some embodiments. 
     As mentioned above, some embodiments of the process  5200  analyze the target image by identifying the values of the components of the pixels in the target image. In some of these embodiments, the process  5200  determines the set of values that represent the set of characteristics of the target image based on the values of the components of the pixels in the target image. For example, the process  5200  of some embodiments determines the value that represents the average saturation of the pixels in the target image by averaging the saturation value of each pixel in the target image. In some embodiments, the process  5200  applies transforms to the values of the components of the pixels in order to identify the saturation value of the pixels. The values for the other characteristics in the set of characteristics described above are determined in a similar way (e.g., averaging each of the red, green, and blue component values of the pixels in the target image, averaging each of the red, green, and blue component values of dark pixels in the target image, averaging each of the red, green, and blue component values of bright pixels in the target image, etc.). 
     As noted above, the process  5200  of some embodiments is performed when determining transforms for matching characteristics of a segmented color in the target image to characteristics of the corresponding segmented color in the source image (e.g., the operation  5140 ). In these embodiments, the same set of twelve characteristics of the target image is determined, but the values are weighted based on the amount of the segmented color in target image. As such, similar techniques described above are used to determine these twelve values for the segmented color, but the values are weighted based on the amount of the segmented color in the target image. For example, if 30 percent of the target image contains a particular segmented color, each value in the set of values is multiplied by 0.3 (except the value that represents whether the image is monochrome, in some embodiments). The value representing whether the target image is monochrome is set to indicate that the target image is monochrome for the transforms for the segmented color in some embodiments. 
     The operations  5230  and  5240  are the similar to that described for the operations  5210  and  5220 , respectively, but the operations  5230  and  5240  are instead performed on a source image. At these operations  5230  and  5240 , the process  5200  analyzes the source image and determines a set of values that represent a set of characteristics of the source image. 
     Finally, the process  5200  determines (at  5250 ) a set of transforms to match the colors of the target image to the colors of the source image based on the set of values. Some embodiments of the process  5200  determine the set of transforms by identifying the difference between the set of values of the target image and the set of values of the source image. The twelve differences are the set of transforms in some of these embodiments. In this fashion, the set of transforms can be used to match the colors of the target image to the colors of the source image. 
     B. Color Segmentation 
     As described with respect to the process  700  and the process  5100 , the color segmentation technique of some embodiments segments the colors of the target image and the colors of the source image in order to match the colors of the target image to the colors of the source image. Different embodiments define a set of colors into which colors of an image are segmented differently. For instance, some embodiments define a set of colors by analyzing the multiple images in order to “learn” the colors that should be segmented into each color in the set of colors. The following  FIG. 53  illustrates an example process for defining color ranges, which are used to segment the colors of an image in some embodiments. 
     i. Defining Color Ranges 
       FIG. 53  illustrates a process  5300  of some embodiments for defining color ranges (e.g., segmented colors or color segments). In some embodiments, the defined color ranges are used (e.g., by the process described below by reference to  FIG. 54 ) to segment the colors of an image. 
     The process  5300  starts by identifying (at  5310 ) a definition of a set color ranges. In some embodiments, the definition of each color range in the set of color ranges is defined by sets of color values with each set of color values representing a color in the color range. A set of color values corresponds to color component values that are used to define colors in a color space in some of these embodiments. For instance, for an RGB color space, a definition of a color range is defined by sets of color values that each includes a red component value, a green component value, and a blue component value. The definition of the set of color ranges is defined differently for different color spaces in different embodiments. In addition, in some embodiments, the identified definition of the set of color ranges is a default set of color ranges while, in other embodiments, the identified definition of the set of color ranges is a set of color ranges that was previously modified (e.g., by the process  5300 ). 
     The process  5300  then receives (at  5320 ) an image that includes colors that are defined to be in the set of color ranges. Some of the identified colors might already be defined in the set of color ranges while some of the identified colors might not yet be defined in the set of color ranges. In some embodiments, the received image includes information that identifies colors in the image (e.g., pixels) that are defined to be in the set of color ranges. Some embodiments also include information that identifies colors that are defined to not to be in the set of color ranges. Furthermore, the colors that are defined to be in the set of color ranges are identified by a user in some embodiments. 
     As mentioned above, some embodiments segment an image to identify different subject types in the image based on the color of the subject types, such as white and blue colors to identify sky, green colors to identify foliage, and red and brown colors to identify earth or terrain. Continuing with this example, the received image may include information that identifies white and blue colored pixels that are defined to be in one color range (e.g., segmented color or color segment), green pixels that are defined to be in another color range, and red and brown pixels that are defined to be in yet another color range. 
     Next, the process  5300  determines (at  5330 ) the values of the colors in the image (e.g., pixels) that are defined to be in a color range of the set of color ranges. As noted above, the definition of a color in a color range is defined by a set of color values that represents a color in a color space in some embodiments. As such, the process  5300  of some embodiments determines the values of the colors in the image that are defined to be in the color range by converting the values of the colors to the color space used to define colors in the definition of the set of color ranges when the color space used to define the values of the colors in the image is not the same as the color space used to define the colors in the definition of the set of color ranges. In some of these embodiments, the values are converted by applying a transform or equation to the values. 
     After determining the values of the colors, the process  5300  modifies (at  5340 ) the definition of the color range based on the determined values. As mentioned, some of the identified colors in the image might already be defined in the set of color ranges and some of the identified colors in the image might not yet be defined in the set of color ranges. For the values of the colors that are not yet defined in the color range, the process  5300  modifies the definition to include the values of such colors in the color range (e.g., by adding the values of the colors to the definition of the color range). Continuing with the example above, if the color range being processed is the white and blue color range, the values of blue and white colors in the image that are not defined in the color range for these colors is modified to include these values. As described above, some embodiments include information that identifies colors that are defined not to be in the set of color ranges. For these colors, the process  5300  modifies the definition to not include the values of such colors in the color range (e.g., by removing the values of the colors from the definition of the color range). 
     The process  5300  then determines (at  5350 ) whether there is any color range left to process. When the process  5300  determines that there is a color range left to process, the process  5300  returns to the operation  5330  to continue processing the remaining color ranges in the set of color ranges. 
     When the process  5300  determines that there is not a color range left to process, the process  5300  determines (at  5360 ) whether there is any image left to process. When the process  5300  determines that there is an image left to process, the process  5300  returns to the operation  5320  to continue processing image. By processing numerous images that include variations of colors defined to be in the set of color ranges, the definition of the set of color ranges is refined by including (or removing) such variations of colors in a color range. 
     ii. Segmenting Colors of an Image 
     Once a set of colors is defined, some embodiments segment the target image and the source image based on the defined set of colors. As mentioned above, some embodiments segment the colors of an image by converting the color space of the image to a color space that is optimized for identifying colors in the image.  FIG. 54  conceptually illustrates an example of a process that segments an image in such a manner. Specifically,  FIG. 54  illustrates a process  5400  of some embodiments for segmenting the colors of an image. In some embodiments, the process  5400  is performed by the process  5100  (e.g., to segment the colors of a target image and a source image at the operation  5130 ). 
     The process  5400  starts by identifying (at  5410 ) a set of transforms that is for converting an image from a color space of the image to an XYZ color space. As noted above, the XYZ color space of some embodiments is a device-independent color space (e.g., the International Commission on Illumination (CIE) 1931 XYZ color space). 
     In some embodiments, an image may be defined by device-dependent color spaces (e.g., an RGB color space), some embodiments of the process  5400  identify the set of transforms that is for converting the image from a device-dependent color space of a particular device to the XYZ color space. In other words, the set of transforms for converting the image from a device-dependent color space of the image to an XYZ color space is unique to the device on which the image is being used or viewed. In some embodiments, the set of transforms identified by the process  5400  is a transformation matrix that represents the set of transforms. 
     Next, the process  5400  modifies (at  5420 ) the identified set of transforms to segment the colors of the image into a set of colors. As noted above, the set of transforms can be represented by a transformation matrix in some embodiments. In such embodiments, the process  5400  modifies the transformation matrix so that the transformation matrix segments the colors of the image into the set of colors. 
     In some embodiments, the process  5400  modifies the set of transforms by modifying the mapping of colors in the color space of the image to the XYZ color space so that the set of transforms shift colors in the color space of the image towards the set of colors in the XYZ color space. For instance, using the example described above by reference to  FIG. 53 , the set of transforms is modified so that blue and white colors are shifted towards (e.g., mapped to) blue colors so that they are identified as blue colors in the modified color space and red and brown colors are mapped to/shifted towards red colors. 
     In some embodiments, the set of colors is a set of color ranges that are defined by the process  5300 , which is described above, while, in other embodiments, the set of colors is a set of predefined colors (e.g., by a user of an application that provides a color matching tool that uses the process  5400  to segment colors of images). 
     Finally, the process  5400  applies (at  5430 ) the modified set of transforms to the image to segment the colors of the image into the set of colors. As mentioned above, a transformation matrix is used to represent the set of transforms in some embodiments. In such embodiments, the process  5400  applies the set of transforms to the image on a pixel-by-pixel basis. As such, the transformation matrix is applied to each pixel of the image to segment the colors of the image into the set of colors. 
     Although the process  5400  illustrates modifying a transform for converting a color space of an image to an XYZ color space, other transforms that convert the color space of the image to a device-independent color space may be used and similarly modified in other embodiments. 
     The following  FIGS. 55-58  illustrates several examples of segmenting colors of an image into a set of colors.  FIG. 55  illustrates an example of applying an unmodified XYZ transform  5520  to an image  5510  to convert a color space of the image (e.g., an RGB color space) to an XYZ color space. As illustrated in this figure, the image  5510  in this example shows a mountain range against a blue sky and white clouds. After the XYZ transform  5520  for converting the color space of the image  5510  to the XYZ color space is applied, the colors of the converted image  5530  remain the same. As shown, the color of the sky in the image  5530  is still blue and the color of the clouds in the image  5530  is still white. 
       FIG. 56  illustrates an example of segmenting colors of the image  5510  into a set of colors when applying a modified XYZ transform  5620  to the image  5510 . In this example, the blue colors and the highlights (e.g., white colors) of the image  5510  are segmented to blue colors. As such, the XYZ transform  5520  is modified to identify blue colors and highlights in the image  5510  and shift them towards blue colors. After applying the modified XYZ transform  5620  to the image  5510 , the blue colors and the highlights (e.g., white colors) of the converted image  5630  are segmented to blue colors. As shown, the color of the sky in the image  5630  is blue and the color of the clouds in the image  5630  is also blue. 
       FIG. 57  illustrates another example of applying an unmodified XYZ transform  5520  to an image  5710  to convert a color space of the image (e.g., an RGB color space) to an XYZ color space. In this example, the image  5710  shows a brown mountain range against a sky and clouds and a gray shadow on the ground that is cast by the clouds. After the XYZ transform  5520  for converting the color space of the image  5710  to the XYZ color space is applied, the colors of the converted image  5730  are unchanged. As shown, the color of the mountain range in the image  5730  is still brown and the color of the shadows in the image  5530  is still gray. 
       FIG. 58  illustrates an example of segmenting colors of the image  5710  into a set of colors when applying a modified XYZ transform  5820  to the image  5710 . The brown colors and the shadows (e.g., gray colors and black colors) of the image  5710  are segmented to brown colors. Thus, the XYZ transform  5520  is modified to identify brown colors and shadows in the image  5710  and shift them towards brown colors. After applying the modified XYZ transform  5820  to the image  5710 , the brown colors and the shadows (e.g., gray colors and black colors) of the converted image  5830  are segmented to brown colors. As illustrated in  FIG. 58 , the color of the mountain range in the image  5830  is brown and the color of the shadow in the image  5830  is also brown. 
     The  FIGS. 55-58  illustrated above show several examples of segmenting colors of an image into a set of colors. However, additional and/or other colors can be segmented into different sets of colors in different embodiments. For example, some embodiments segment red colors, brown colors, and shadow-like colors to a certain color (e.g., red colors or brown colors). 
     C. Matching Colors of Images 
     As mentioned above, after determining transforms for matching the colors of a target image to the colors of a source image, some embodiments apply the determined transforms to the target image (e.g., at the operations  5120  and  5140 ).  FIG. 59  conceptually illustrates a process  5900  of some embodiments for applying transforms to a target image to match the colors of the target image to the colors of a source image. In some embodiments, the process  5900  is performed by the process  5100  (e.g., to apply transforms to a target image at the operation  5160 ). In this example, the transforms that are applied to the target image are transforms determined by the process  5100  (e.g., transforms for a set of characteristics and transforms for segmented colors), as described above. 
     The process  5900  starts by identifying (at  5910 ) a pixel in the target image. The process  5900  then applies (at  5920 ) the set of transforms for matching a set of characteristics of the target image to the set of characteristics of the source image. As noted above, some embodiments represent the transforms using a transformation matrix. In such embodiments, the transformation matrix is applied to the pixel. 
     Next, the process  5900  identifies (at  5930 ) a set of transforms for matching characteristics of a segmented color of the target image to characteristics of the segmented color in the source image. After identifying the set of transforms, the process  5900  applies (at  5940 ) the set of transforms to the pixel. As mentioned above, some embodiments use a transformation matrix to represent this set of transforms. In such embodiments, the process  5900  applies the transformation matrix to the pixel. 
     The process  5900  then determines (at  5950 ) whether any set of transforms for a segmented color is left to process. When the process  5900  determines that there is a set of transforms for a segmented color left to process, the process  5900  returns to the operation  5930  to continue identifying and applying sets of transforms for any remaining segmented colors. When the process  5900  determines that there is not a set of transforms for a segmented color left to process, the process  5900  proceeds to operation  5960 . 
     At  5960 , the process  5900  determines whether any pixel in the target image is left to process. When the process  5900  determines that there is a pixel in the target image left to process, the process  5900  returns to the operation  5910  to continue processing any remaining pixels in the target image. When the process  5900  determines that there is not a pixel in the target image left to process, the process  5900  ends. 
     Many of the figures illustrated above show examples of matching the colors of an image to the colors of another image. However, in some embodiments, the colors of frames in a target video clip are matched to the colors of a frame in a source video clip (or a source image). For instance, some embodiments identify a frame in the source video clip to which to match all the frames in the target video clip. The follow  FIG. 60  illustrates an example of a process that matches the frames of a target video clip to a frame of a source video clip. Specifically,  FIG. 60  illustrates a process  6000  of some embodiments for matching the colors of each frame of a target video clip to the colors of a frame of a source video clip. 
     The process  6000  begins by identifying (at  6010 ) a frame in a source video clip. Different embodiments identify a frame in the source video clip in different ways. For example, one way of identifying a frame in a source video clip is described in further detail below by reference to  FIG. 61 . 
     Next, the process  6000  identifies (at  6020 ) a frame in the target video clip. When identifying the initial frame in the target video clip, different embodiments identify the frame differently. For instance, some embodiments may identify the first frame in the target video clip (e.g., the frame that is displayed first when the video clip is played back) as the initial frame while other embodiments may identify the last frame in the target video clip (e.g., the frame that is displayed last when the video clip is played back) as the initial frame. The process  6000  of other embodiments identifies other frames of the target video clip as the initial frame. 
     The process  6000  then matches (at  6030 ) the colors of the identified frame in the target video clip to the colors of the identified frame in the source image. Some embodiments of the process  6000  performs the process  5100 , which is previously described by reference to  FIG. 51 , to match the colors of the identified frame of the target video clip to the colors of the identified frame of the source video clip. In some such embodiments, the process  6000  uses the same transforms that are determined (e.g., at the operations  5110  and  5140 ) for the initial identified frame of the target video clip to match the colors of subsequent identified frames of the target video clip to the colors of the identified frame of the source video clip. In contrast, the process  6000  of other such embodiments determines transforms (e.g., at the operations  5110  and  5140 ) for the identified frame of the target video clip each time the process  6000  matches the colors of the identified frame of the target video clip to the colors of the identified frame of the source video clip. 
     Finally, the process  6000  determines (at  6040 ) whether any frame in the target video clip is left to process. When the process  6000  determines that there is a frame in the target video clip left to process, the process  6000  returns to the operation  6020  to continue processing frames in the target video clip. Otherwise, the process  6000  ends. 
     While the process  6000  illustrates matching the colors of a target video clip to a frame of a source video clip, the process  6000  of some embodiments can be used for matching the colors of a target video clip to a source image (instead of a source video clip) as well. In some of these embodiments, instead of identifying a frame in a source video clip at the operation  6010 , the process  6000  identifies a source image at the operation  6010 . 
     As described above, different embodiments identifying a frame in a source video clip for matching a frame in a target video clip against in different ways. Some embodiments identify the frame in the source video clip that is “closest” to the frame in the target video clip.  FIG. 61  illustrates an example of such a process. Specifically,  FIG. 61  conceptually illustrates a process  6100  of some embodiments for identifying a frame in a source video clip against which to match a frame of a target video clip. Although the process  6100  illustrates identifying a frame in a source video clip against which to match a frame of a target video clip, in some embodiments the process  6100  can be used to identifying a frame in a source video clip against which to match a target image. In some embodiments, the process  6100  is performed by the process  6000  (e.g., to identify a frame in a source video clip at the operation  6010 ). 
     The process  6100  starts by identifying (at  6110 ) a frame in the target video clip. In some embodiments, the identified frame is a frame in the target video clip selected by a user through a GUI of an application (e.g., GUI  100 ) that provides the color matching tool. In other embodiments, the identified frame is a default frame (e.g., the first frame, middle frame, last frame, etc.) in the target video clip. 
     Next, the process  6100  identifies (at  6120 ) a set of characteristics of the identified frame of the target video clip. In some embodiments, the process  6100  identifies the set of characteristics by determining a set of values that represent a set of characteristics of the frame in the same or similar fashion as described above by reference to operations  5220  and  5240  of  FIG. 52 . 
     The process  6100  then identifies (at  6130 ) a frame in the source video clip. When identifying the initial frame in the source video clip, some embodiments of the process  6100  identify the first frame (e.g., the frame that is displayed first when the video clip is played back) as the initial frame while other embodiments of the process  6100  identify the last frame (e.g., the frame that is displayed last when the video clip is played back). Still, some embodiments identify the middle frame (e.g., the frame that is displayed midway through playback of the video clip). Other ways of identifying the initial frame are possible in other embodiments. 
     After identifying a frame in the source video clip, the process  6100  identifies (at  6140 ) a set of characteristics of the identified frame of the source. Similar to the operation  6120 , the process  6100  of some embodiments identifies the set of characteristics by determining a set of values that represents a set of characteristics of the frame in the same or similar fashion as described above by reference to operations  5220  and  5240  of  FIG. 52 . 
     Next, the process  6100  determines (at  6150 ) the closeness between the frame in the target video clip and the frame in the source video clip based on the sets of characteristics of the frames. In some embodiments, the closeness between the frames is determined by the Euclidean distance between the sets of values. 
       FIG. 62  illustrates an example of determining closeness between two images. In particular, this example illustrates determining the Euclidean distance between a matrix of a set of twelve values that are determined in the similar manner described above by reference to operations  5220  and  5240  of  FIG. 52 . 
     To determine the Euclidean distance between the two 3×4 matrices of values, the following equation is used: 
             distance   =                       (       T   ⁢           ⁢   1     -     S   ⁢           ⁢   1       )     2     +       (       T   ⁢           ⁢   2     -     S   ⁢           ⁢   2       )     2     +       (       T   ⁢           ⁢   3     -     S   ⁢           ⁢   3       )     2     +       (       T   ⁢           ⁢   4     -     S   ⁢           ⁢   4       )     2     +                   (       T   ⁢           ⁢   5     -     S   ⁢           ⁢   5       )     2     +       (       T   ⁢           ⁢   6     -     S   ⁢           ⁢   6       )     2     +       (       T   ⁢           ⁢   7     -     S   ⁢           ⁢   7       )     2     +       (     T8   -     S   ⁢           ⁢   8       )     2     +                         (       T   ⁢           ⁢   9     +     S   ⁢           ⁢   9       )     2     +       (       T   ⁢           ⁢   10     -     S   ⁢           ⁢   10       )     2     +       (       T   ⁢           ⁢   11     +     S   ⁢           ⁢   11       )     2     +       (       T   ⁢           ⁢   12     -     S   ⁢           ⁢   12       )     2                     
where T 1 -T 12  are the values of the matrix of the target frame and S 1 -S 12  are the values of the matrix of the source frame. For this example, the values D 1 -D 12  are used for the matrix of the target frame and the values E 1 -E 12  are used for the matrix of the source frame. As shown in  FIG. 62 , the above equation is used to determine the Euclidean distance between the matrix of the target frame and the matrix of the source frame.
 
     Some embodiments apply different weights to different values. In this manner, values representing characteristics of the frames that are determined to be more important than others in determining the closeness between the matrices of the frames are given more weight than those values that are determined to be less important. 
     Returning to  FIG. 61 , the process  6100  then determines (at  6160 ) whether the frame of the source video clip is closest to the frame of the target video clip. In some embodiments that use the above equation to determine closeness, the frame in the source video clip that is closest to the frame in the target video clip is the frame with the smallest Euclidean distance to the frame in the target video clip. When the process  6100  determines that the frame of the source video clip is closest to the frame of the target video clip, the process identifies (at  6170 ) the frame in the source video clip as the closest frame to the frame in the target video clip. Otherwise, the process  6100  proceeds to operation  6180 . 
     At  6180 , the process  6100  determines whether any frame in the source video clip is left to process. When the process  6100  determines that there is a frame in the source video clip left to process, the process  6100  returns to operation  6130  to continue processing any remaining frames in the source video clip in order to identify the frame in the source video clip that the closest to the frame in the target video clip. When the process  6100  determines that there is not a frame in the source video clip left to process, the process  6100  ends. 
     IV. Software Architecture 
     In some embodiments, the processes described above are implemented as software running on a particular machine, such as a computer or a handheld device, or stored in a computer readable medium.  FIG. 63  conceptually illustrates the software architecture of a media editing application  6300  of some embodiments. In some embodiments, the media editing application is a stand-alone application or is integrated into another application, while in other embodiments the application might be implemented within an operating system. Furthermore, in some embodiments, the application is provided as part of a server-based solution. In some such embodiments, the application is provided via a thin client. That is, the application runs on a server while a user interacts with the application via a separate machine remote from the server. In other such embodiments, the application is provided via a thick client. That is, the application is distributed from the server to the client machine and runs on the client machine. 
     As shown, the media editing application  6300  includes a user interface (UI) interaction module  6305 , a set of editing modules  6310 , a preview generator  6315 , and a color matcher  6320 . The media editing application  6300  also includes media data  6330  and transform data  6335 . In some embodiments, the transform data  6335  stores numerous transform that the media-editing application may apply to images before color matching images (e.g., transforms for converting an image to a particular color space). In some embodiments, the transform data  6335  also stores transforms determined by the color matcher  6320  during color matching images, which the color matcher  6320  might later use for color matching images (e.g., gain and lift transforms, black balance and white balance transforms, saturation transforms, etc.). The media data  6330  stores media content (e.g., text, audio, image, and video content) data of media clips. In some embodiments, storages  6330  and  6335  are all stored in one physical storage. In other embodiments, the storages are in separate physical storages. In some cases, for example, the media data  6330  may be stored across multiple hard drives, network drives, etc. 
       FIG. 63  also illustrates an operating system  6350  that includes input device driver(s)  6355  and display module  6360 . In some embodiments, as illustrated, the input device drivers  6355  and display module  6360  are part of the operating system  6350  even when the media editing application is an application separate from the operating system  6350 . 
     The input device drivers  6355  may include drivers for translating signals from a keyboard, mouse, touchpad, tablet, touch screen, etc. A user interacts with one or more of these input devices, which send signals to their corresponding device driver. The device driver then translates the signals into user input data that is provided to the UI interaction module  6305 . 
     The present application describes a graphical user interface that provides users with numerous ways to perform different sets of operations and functionalities. In some embodiments, these operations and functionalities are performed based on different commands that are received from users through different input devices (e.g., keyboard, track pad, touchpad, mouse, etc.). For example, the present application describes the use of a cursor in the graphical user interface to control (e.g., select, move) objects in the graphical user interface. However, in some embodiments, objects in the graphical user interface can also be controlled or manipulated through other controls, such as touch control. In some embodiments, touch control is implemented through an input device that can detect the presence and location of touch on a display of the device. An example of such a device is a touch-screen device. In some embodiments, with touch control, a user can directly manipulate objects by interacting with the graphical user interface that is displayed on the display of the touch-screen device. For instance, a user can select a particular object in the graphical user interface by simply touching that particular object on the display of the touch-screen device. As such, when touch control is utilized, a cursor may not even be provided for enabling selection of an object of a graphical user interface in some embodiments. However, when a cursor is provided in a graphical user interface, touch control can be used to control the cursor in some embodiments. 
     The display module  6360  translates the output of a user interface for a display device. That is, the display module  6360  receives signals (e.g., from the UI interaction module  6305 ) describing what should be displayed and translates these signals into pixel information that is sent to the display device. The display device may be an LCD, plasma screen, CRT monitor, touch screen, etc. 
     The UI interaction module  6305  of the media editing application  6300  interprets the user input data received from the input device drivers  6355  and passes it to various modules, including the editing modules  6310 , and the preview generator  6315 . The UI interaction module also manages the display of the UI and outputs this display information to the display module  6360 . This UI display information may be based on information from the color matcher  6320  or directly from input data (e.g., when a user moves an item in the UI that does not affect any of the other modules of the media editing application  6300 ). 
     The color matcher  6320  matches the colors of an image to the colors of another image based on user inputs received from the UI interaction module  6305 . The color matcher  6320  includes a luma-based matcher  6325 , a hue-based matcher  6340 , a color segmentation engine  6345 , and a color transform engine  6365 . 
     The luma-based matcher  6325  matches images based on the images&#39; luma. In some embodiments, the luma-based matcher  6325  includes the transform generator  220 , as described above. In these embodiments, the luma-based matcher  6325  might identify luma ranges for images being color matched, determine transforms based on the identified luma ranges, and blend the determined transforms. The luma-based matcher  6325  sends transforms to the color transform engine  6365  to apply the transforms to the image being matched. 
     The hue-based matcher  6340  matches images based on the images&#39; hues. In some embodiments, the hue-based matcher  6340  includes the hue engine  520 , as described above. In these embodiments, the hue-based matcher  6340  may identify dominant hue ranges in the images being color matched, match the identified hue ranges, and perform hue shifts on the matched hue ranges. The hue-based matched  6340  sends transforms to the color transform engine  6365  to apply the transform to the image being matched. 
     The color segmentation engine  6345  segments images being matched and matches the segmented colors of the images. The color segmentation engine  6345  might identify a frame in a video clip to which to match another image or a frame or frames of another video clip. The color segmentation engine  6345  sends transforms to the color transform engine  6365  to apply the transform to the image being matched. 
     The color transform engine  6365  receives transforms from the luma-based matcher  6325 , the hue-based matcher  6340 , and the color segmentation engine  6345  along with an image to which to apply the transforms. In some embodiments, different color matching options are provided by the media-editing application. However, in some embodiments, a combination of different color matching options is provided as one color matching option. In such embodiments, after applying transform to an image, the color transform engine  6365  might send the image to the luma-based matcher  6325 , the hue-based matcher  6340 , and/or the color segmentation engine  6345  for further color matching. 
     The preview generator  6315  enables the output of audio and video from the media editing application so that a user can preview images or clips. The preview generator  6315  uses the media data to send display instructions to the UI interaction module  6305 , which incorporates the preview into the user interface. In some embodiments, the preview generator  6315  sends a preview of a color matching operation to an image or frame in a video clip to the UI interaction module  6305  before the color matcher  6320  actually performs the color matching operation(s). 
     While many of the features have been described as being performed by one module (e.g., the color segmentation engine 6345  or the preview generator  6315 ), one of ordinary skill would recognize that the functions might be split up into multiple modules. Similarly, the functions described as being performed by multiple different modules might be performed by a single module in some embodiments (e.g., the color transform engine  6365  might be included in each of the luma-based matcher  6325 , the hue-based matcher  6340 , and the color segmentation engine  6345 ). 
     V. Computer System 
     Many of the above-described features and applications are implemented as software processes that are specified as a set of instructions recorded on a computer readable storage medium (also referred to as computer readable medium). When these instructions are executed by one or more processing unit(s) (e.g., one or more processors, cores of processors, or other processing units), they cause the processing unit(s) to perform the actions indicated in the instructions. Examples of computer readable media include, but are not limited to, CD-ROMs, flash drives, RAM chips, hard drives, EPROMs, etc. The computer readable media does not include carrier waves and electronic signals passing wirelessly or over wired connections. 
     In this specification, the term “software” is meant to include firmware residing in read-only memory or applications stored in magnetic storage which can be read into memory for processing by a processor. Also, in some embodiments, multiple software inventions can be implemented as sub-parts of a larger program while remaining distinct software inventions. In some embodiments, multiple software inventions can also be implemented as separate programs. Finally, any combination of separate programs that together implement a software invention described here is within the scope of the invention. In some embodiments, the software programs, when installed to operate on one or more electronic systems, define one or more specific machine implementations that execute and perform the operations of the software programs. 
       FIG. 64  conceptually illustrates a computer system  6400  with which some embodiments of the invention are implemented. The electronic system  6400  may be a computer, phone, PDA, or any other sort of electronic device. Such an electronic system includes various types of computer readable media and interfaces for various other types of computer readable media. Electronic system  6400  includes a bus  6405 , processing unit(s)  6410 , a graphics processing unit (GPU)  6420 , a system memory  6425 , a read-only memory  6430 , a permanent storage device  6435 , input devices  6440 , and output devices  6445 . 
     The bus  6405  collectively represents all system, peripheral, and chipset buses that communicatively connect the numerous internal devices of the electronic system  6400 . For instance, the bus  6405  communicatively connects the processing unit(s)  6410  with the read-only memory  6430 , the GPU  6420 , the system memory  6425 , and the permanent storage device  6435 . 
     From these various memory units, the processing unit(s)  6410  retrieve instructions to execute and data to process in order to execute the processes of the invention. The processing unit(s) may be a single processor or a multi-core processor in different embodiments. Some instructions are passed to and executed by the GPU  6420 . The GPU  6420  can offload various computations or complement the image processing provided by the processing unit(s)  6410 . In some embodiments, such functionality can be provided using Corelmage&#39;s kernel shading language. 
     The read-only-memory (ROM)  6430  stores static data and instructions that are needed by the processing unit(s)  6410  and other modules of the electronic system. The permanent storage device  6435 , on the other hand, is a read-and-write memory device. This device is a non-volatile memory unit that stores instructions and data even when the electronic system  6400  is off. Some embodiments of the invention use a mass-storage device (such as a magnetic or optical disk and its corresponding disk drive) as the permanent storage device  6435 . 
     Other embodiments use a removable storage device (such as a floppy disk, flash drive, or ZIP® disk, and its corresponding disk drive) as the permanent storage device. Like the permanent storage device  6435 , the system memory  6425  is a read-and-write memory device. However, unlike storage device  6435 , the system memory is a volatile read-and-write memory, such a random access memory. The system memory stores some of the instructions and data that the processor needs at runtime. In some embodiments, the invention&#39;s processes are stored in the system memory  6425 , the permanent storage device  6435 , and/or the read-only memory  6430 . For example, the various memory units include instructions for processing multimedia clips in accordance with some embodiments. From these various memory units, the processing unit(s)  6410  retrieve instructions to execute and data to process in order to execute the processes of some embodiments. 
     The bus  6405  also connects to the input and output devices  6440  and  6445 . The input devices enable the user to communicate information and select commands to the electronic system. The input devices  6440  include alphanumeric keyboards and pointing devices (also called “cursor control devices”). The output devices  6445  display images generated by the electronic system. The output devices include printers and display devices, such as cathode ray tubes (CRT) or liquid crystal displays (LCD). Some embodiments include devices such as a touchscreen that function as both input and output devices. 
     Finally, as shown in  FIG. 64 , bus  6405  also couples electronic system  6400  to a network  6465  through a network adapter (not shown). In this manner, the computer can be a part of a network of computers (such as a local area network (“LAN”), a wide area network (“WAN”), or an Intranet, or a network of networks, such as the Internet. Any or all components of electronic system  6400  may be used in conjunction with the invention. 
     Some embodiments include electronic components, such as microprocessors, storage and memory that store computer program instructions in a machine-readable or computer-readable medium (alternatively referred to as computer-readable storage media, machine-readable media, or machine-readable storage media). Some examples of such computer-readable media include RAM, ROM, read-only compact discs (CD-ROM), recordable compact discs (CD-R), rewritable compact discs (CD-RW), read-only digital versatile discs (e.g., DVD-ROM, dual-layer DVD-ROM), a variety of recordable/rewritable DVDs (e.g., DVD-RAM, DVD-RW, DVD+RW, etc.), flash memory (e.g., SD cards, mini-SD cards, micro-SD cards, etc.), magnetic and/or solid state hard drives, read-only and recordable Blu-Ray® discs, ultra density optical discs, any other optical or magnetic media, and floppy disks. The computer-readable media may store a computer program that is executable by at least one processing unit and includes sets of instructions for performing various operations. Examples of computer programs or computer code include machine code, such as is produced by a compiler, and files including higher-level code that are executed by a computer, an electronic component, or a microprocessor using an interpreter. 
     While the above discussion primarily refers to microprocessor or multi-core processors that execute software, some embodiments are performed by one or more integrated circuits, such as application specific integrated circuits (ASICs) or field programmable gate arrays (FPGAs). In some embodiments, such integrated circuits execute instructions that are stored on the circuit itself. 
     As used in this specification and any claims of this application, the terms “computer”, “server”, “processor”, and “memory” all refer to electronic or other technological devices. These terms exclude people or groups of people. For the purposes of the specification, the terms display or displaying means displaying on an electronic device. As used in this specification and any claims of this application, the terms “computer readable medium” and “computer readable media” are entirely restricted to tangible, physical objects that store information in a form that is readable by a computer. These terms exclude any wireless signals, wired download signals, and any other ephemeral signals. 
     While the invention has been described with reference to numerous specific details, one of ordinary skill in the art will recognize that the invention can be embodied in other specific forms without departing from the spirit of the invention. In addition, a number of the figures (including  FIGS. 3 ,  6 ,  7 ,  10 ,  12 ,  14 ,  15 ,  16 ,  20 ,  23 ,  26 ,  28 ,  30 ,  32 ,  35 ,  37 ,  40 ,  46 ,  49 ,  50 ,  51 ,  52 ,  53 ,  54 ,  59 ,  60 , and  61 ) conceptually illustrate processes. The specific operations of these processes may not be performed in the exact order shown and described. The specific operations may not be performed in one continuous series of operations, and different specific operations may be performed in different embodiments. Furthermore, the process could be implemented using several sub-processes, or as part of a larger macro process.

Metadata:
Filing Date: 20110204
Publication Date: 20131217
Grant Date: 20131217
Priority Date: 20110204
Inventors: BRYANT ANDREW
PETTIGREW DANIEL
Assignee: APPLE INC
CPC Classifications: [{"code": "H04N1/62", "inventive": true, "first": false, "tree": "[]"}, {"code": "H04N1/60", "inventive": true, "first": true, "tree": "[]"}, {"code": "H04N1/6075", "inventive": true, "first": false, "tree": "[]"}, {"code": "H04N1/60", "inventive": true, "first": true, "tree": "[]"}, {"code": "H04N1/62", "inventive": true, "first": false, "tree": "[]"}, {"code": "H04N1/6075", "inventive": true, "first": false, "tree": "[]"}]
Family ID: 46600657