Patent Publication Number: US-7899258-B2

Title: Systems and methods to convert images into high-quality compressed documents

Description:
CROSS-REFERENCE TO RELATED APPLICATIONS 
     This application is a continuation-in-part of, and claims the priority benefit of, co-pending, commonly-assigned U.S. patent application Ser. No. 11/202,720, filed on Aug. 12, 2005, entitled “LABEL AIDED COPY ENHANCEMENT,” by Anoop K. Bhattacharjya. The subject matter of the foregoing is incorporated herein by reference in its entirety. 
     This application is also related to co-pending, commonly-assigned U.S. patent application Ser. No. 11/691,339, filed on Mar. 26, 2007, entitled “SYSTEMS AND METHODS FOR GENERATING BACKGROUND AND FOREGROUND IMAGES FOR DOCUMENT COMPRESSION,” by Che-Bin Liu and Anoop K. Bhattacharjya. The subject matter of the foregoing is incorporated herein by reference in its entirety. 
    
    
     BACKGROUND 
     A. Technical Field 
     The present invention pertains generally to digital image processing, and relates more particularly to image processing that allows for enhanced quality of images while achieving high compression ratios. The present invention may be incorporated into systems and methods. 
     B. Background of the Invention 
     Whiteboards have been and are extensively used for exchanging ideas in many places, such as meeting rooms and classrooms. Notes on whiteboards are usually taken manually during or after discussions, which can be quite time consuming. Although whiteboard equipped with scanners are available, such systems are typically very costly solutions to reproduce what is on a whiteboard. Furthermore, a person at a meeting in which information is conveyed using a whiteboard may have no control over the type of whiteboard that is used. If a whiteboard scanner is not present, the participant may not have the ability to insist or insure that a whiteboard scanner be used or be made available for use. These problems or similar ones exist for information presented in other ways, such as posters, easels, chalkboards, signs, paper, cards, magazines, and the like. 
     Using a camera to capture images is economical, but the captured image may not be ready for reproduction, such as printing, due to factors such as unpredictable room lighting. Post-processing of the captured image using image editing software is typically a very tedious task, and it can be quite difficult to achieve satisfactory quality. In addition, such an image usually has a huge file size, making it difficult to store, process, and/or transmit. 
     Accordingly, what is needed are systems and method to convert images, such as whiteboard images, to a high-quality high-compression file in a compressed format, such as PDF format. 
     SUMMARY OF THE INVENTION 
     Aspects of the present invention provide label aided enhancement for subsequent high-quality copy generation or other rendering. In particular, regions within the scanned document are identified and labeled according to their particular content. This content may be text, image, background or other content known within the art and corresponding label information is leveraged to perform various enhancement and content refinement functions. For example, a region labeled as text may be enhanced using a different method as compared to a region labeled as image. 
     In one embodiment of the invention, regions within a scanned document are identified and labeled based on an analysis of pixel edge information within each region. Pixel gradient calculations are performed and analyzed in order to identify edge pixels within a particular region. The identified edge pixels may be further analyzed to determine an edge type for each of the edge pixels. Using this pixel edge and edge type information, regions may be tentatively identified within the scanned document according to content, such as text, image, and background regions. 
     In yet another embodiment of the invention, identified background regions may be used to identify the paper color of the original document and allow compensation for any page-color background manipulation operations. Other colors outside of the background region may also be remapped to maintain appropriate color relationships when the page background color is manipulated. For example, background colors, and if necessary other colors within the scanned document, may be mapped back to a preferred reference background color using various color translation and transformation methods. 
     In still yet another embodiment of the invention, multi-region enhancement may be performed on the scanned document. This multi-region enhancement may include a region adaptive descreening method during which unwanted halftone is removed from the scanned document, and a region dependent sharpening method in which regions are sharpened according to their content. For example, a text region may be sharpened using a standard unsharp masking algorithm. 
     The labeling of the scanned document may be further leveraged in both rendering and storing (e.g., region-dependent compression) the entire document or portions thereof. It is therefore an object of the present invention to provide systems and methods that allow for high compression ratios of an image but also maintain image quality. 
     Aspects of the present invention provide systems and methods for providing high compression ratios of an image but also maintain image quality. In embodiments, an input image may be compressed but maintain high-quality by separating certain features, such as text and other fine features, into a layer separate from other features that are not as compression sensitive. 
     In embodiments, a plurality of pixels selected from the input image may be labeled. In an embodiment, background text pixels, which may comprise small-size regions and thin features that fit within one of a plurality of bounding boxes, may be identified as being part of a foreground image layer and a background image layer may also be generated that comprising pixels from the input image that exclude the background text pixels. In embodiments, a mask layer may be generated that identifies from which of the background and foreground image layers a pixel should be selected when generating a representation of the input image. Having separating the input image into layers to minimize the adverse effects due to compression, a first compression algorithm may be used to compress the mask layer and at least one different compression algorithm may be used to compress the foreground and background image layers. Through the mask, items are identified where detail is preferably preserved when rendering the composite image and a lossless compression algorithm may be used. In embodiments, the foreground and background image layers may be compressed using a lossy algorithm. In an embodiment, the foreground and background image layers may also be downsampled. 
     In an embodiment, labeling may be performed using the systems or methods disclosed with respect to the label aided copy enhancement provided herein. One skilled in the art shall recognize that other labeling or segmentation systems or methods may also be employed. 
     In embodiments, as part of the label aided copy enhancement or performed separately or additionally, a background of the input image may be estimated. In an embodiment, responsive to estimating a background color, one or more pixels may be relabeled. 
     In an embodiment, the background text may be identified by using the pixel labels to generate a background mask comprising pixels with a background label, performing a connected component analysis on non-background pixels of the background mask, and responsive to a connected component group of non-background pixels being below a threshold size, selecting the connected component group as background text pixels. In an embodiment, the threshold size of the connect component group may refer to the number of pixels of the connect component group, may refer to the size of a region that bounds the connect component group, or both. 
     In an embodiment, systems and methods of the present invention may provide for selection of different modes, wherein different features may be included in the foreground and/or mask layers. In an embodiment, a photo mode may result in a foreground image layer comprising the background text. In embodiments, in a magazine or text mode, solid regions may also be included in the foreground image layer. 
     In embodiments, the solid regions may be obtained by generating an edge mask comprising pixels labeled as edge pixels and dilating the edge mask toward non-background regions of the edge mask. The labeled pixels and the dilated edge mask may be used to identify solid region pixels. In embodiments, line scans may be used to identify a non-edge-pixel segment bounded by a first edge pixel segment and a second edge pixel segment. A first color value may be computed for the first edge pixel segment. A second color value may be computed for the second edge pixel segment, and a non-edge-pixel segment color value may be computed for the non-edge-pixel segment. In embodiments, the color values may be the mean, median, or mode of a segment. In an embodiment, responsive to the difference between the non-edge-pixel segment color value and the average of the first and second color values being below a threshold value, the combined first edge pixel segment, second edge pixel segment, and non-edge-pixel segment may be identified as solid color region pixels and included in the foreground image layer. 
     In embodiments, the selection of a solid color region may include a size constraint. In an embodiment, one or more of the lengths of the combined length of the first edge pixel segment, second edge pixel segment, and non-edge-pixel segment; the combined length of the first edge pixel segment and non-edge-pixel segment; the combined length of the second edge pixel segment and non-edge-pixel segment; and the length of the non-edge-pixel segment, may be required to be below a threshold length or lengths. If the size constraint is met, the combined first edge pixel segment, second edge pixel segment, and non-edge-pixel segment may be identified as solid color region pixels and included in the foreground image layer. In an embodiment, additional or alternative constraints may be imposed upon a line scan in order for it to be classified as solid color pixels. In an embodiment, an additional or alternative constraint may be that at least a portion of the preceding line scan be identified as edge pixels or solid color pixels. 
     In embodiments, after separating the input image into two layers, color interpolation may be performed to fill blank areas in the image layers. In an embodiment, a filter or a set of filters may be applied to a foreground and/or background image layers to fill the image layer. In an embodiment, open areas in the background image layer corresponding to background text may be filled with the background color. 
     In embodiments, layer-based color enhancement may be performed to enhance one or more of the layers. In an embodiment, all background text on the foreground layer may be smoothed and color enhanced. In an embodiment, solid color regions may also be enhanced. In embodiments, the color enhancement may be conditionally performed based on local color statistics. 
     In embodiments, smoothing may be performed by applying a filtering operation to one or more of the layers. In one embodiment, the filtering operation may comprise setting a pixel value to the average value of the like-labeled pixels within a neighborhood of that pixel. 
     In embodiments, because the background color may have been estimated, different levels of background or paper color suppression may be applied. In an embodiment, the brightness of the mean background color in Hue-Saturation-Value (HSV) color space may be determined. If the background is not within a light color range, the background may not be suppressed in order to reflect the true background color. In an embodiment, a small background block may not be suppressed. 
     In an embodiment, in the text mode, large background sections may be replaced with white color. In an embodiment, background pixels may be completely replaced with white color if the true background color is bright and close to white color. In an embodiment, in the magazine mode, background color may be shifted toward white color. In an embodiment, background pixels may be shifted in color halfway toward white color. 
     In embodiments, a representation of the input image, or composite document, may be obtained from the foreground, background, and mask layers. In one embodiment, the composite document may use a portable document format. 
     Aspects of the present invention may also be applied to compressing images wherein the background color or colors of the image may be assumed, such as by way of example and not limitation, whiteboards, posters, chalkboards, paper, cards, and the like. In such embodiments, the foreground image layer and mask layer may be generated using one or more of the systems and/or methods disclosed herein. In embodiments, the file size of the compressed document may be further reduced by not including a background layer in the compressed document. 
     Aspects of the present invention may be incorporated or included in a system or systems. It should be noted that the teachings of the present invention may be implemented in any device or system that is capable of receiving and processing a digital image, included but not limited to computers, multimedia devices, scanners, printers, copiers, cameras, and the like. 
     Aspects of the present invention may be implemented in a wide variety of ways including software, hardware, firmware, or combinations thereof. For example, the functions to practice various aspects of the present invention may be performed by components that are implemented in a wide variety of ways, including discrete logic components, one or more application specific integrated circuits (ASICs), and/or a program or programs of instructions for execution by one or more program-controlled processors. It shall be noted that the manner in which the present invention is implemented is not critical. 
     Some features and advantages of the invention have been generally described in this summary section; however, additional features, advantages, and embodiments are presented herein or will be apparent to one of ordinary skill in the art in view of the drawings, specification, and claims hereof. Accordingly, it should be understood that the scope of the invention shall not be limited by the particular embodiments disclosed in this summary section. 
    
    
     
       BRIEF DESCRIPTION OF THE DRAWINGS 
       Reference will be made to embodiments of the invention, examples of which may be illustrated in the accompanying figures. These figures are intended to be illustrative, not limiting. Although the invention is generally described in the context of these embodiments, it should be understood that it is not intended to limit the scope of the invention to these particular embodiments. 
       FIG. (“FIG.”)  1  depicts, for purposes of illustration, an example of an image document  100  that may be processed according to an embodiment of the invention. 
         FIG. 2  is a general block diagram of a system for label aided document copy enhancement according to one embodiment of the invention. 
         FIG. 3  is a block diagram of an image labeling module according to one embodiment of the invention. 
         FIG. 4  is illustrations of examples of non-halftone edges prior to and after refinement according to one embodiment of the invention. 
         FIG. 5  is a flowchart illustrating a method for background estimation and label refinement according to one embodiment of the invention. 
         FIG. 6  illustrates documents prior to and after label aided background enhancement according to one embodiment of the invention. 
         FIG. 7  is a block diagram of a multi-region enhancement module according to one embodiment of the invention. 
         FIG. 8  is a flowchart illustrating a method for multi-region enhancement according to one embodiment of the invention. 
         FIG. 9  is a general block diagram of a system for document compression according to an embodiment of the invention. 
         FIG. 10  is a flowchart illustrating a method for refining background pixel labels according to an embodiment of the invention. 
         FIG. 11  is a flowchart illustrating a method for identifying text/solid color regions in an image according to an embodiment of the invention. 
         FIG. 12  depicts, for purposes of illustration, a portion of the image document  100  depicted in  FIG. 1  and example horizontal line scans, according to an embodiment of the invention. 
         FIG. 13  depicts a set of filters that may be applied to a foreground image layer and/or a background image layer to fill the image layer according to an embodiment of the invention. 
         FIG. 14  depicts an example of a smoothing filter that may be applied to an image layer according to an embodiment of the invention. 
         FIG. 15  is an example of an input image document that may be processed according to embodiments of the invention. 
         FIG. 16A  depicts an example of a background image layer that may be obtained from the input image according to an embodiment of the invention. 
         FIG. 16B  depicts an example of a foreground image layer that may be obtained from the input image according to an embodiment of the invention. 
         FIG. 17  depicts an example of a mask that may be obtained from the input image according to an embodiment of the invention. 
         FIG. 18  depicts an example of a composite image, which is a representation of the input image ( FIG. 15 ), that may be obtained from the foreground image layer ( FIG. 16A ), background image layer ( FIG. 16B ), and the mask ( FIG. 17 ) according to an embodiment of the invention. 
         FIG. 19  is a general block diagram of a system for document compression according to an embodiment of the invention. 
         FIG. 20  is a general block diagram of a system for document compression according to an embodiment of the invention. 
         FIG. 21  depicts an example of an input image according to an embodiment of the invention. 
         FIG. 22  depicts an example of a foreground image layer for the input image depicted in  FIG. 21  according to one embodiment of the invention. 
         FIG. 23  depicts an example of a mask layer for the input image depicted in  FIG. 21  according to one embodiment of the invention. 
         FIG. 24  depicts an example of a composite image of the input image depicted in  FIG. 21  generated from the foreground image layer and the mask layer according to one embodiment of the invention. 
     
    
    
     DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS 
     In the following description, for purpose of explanation, specific details are set forth in order to provide an understanding of the invention. It will be apparent, however, to one skilled in the art that the invention may be practiced without these details. One skilled in the art will recognize that embodiments of the present invention, some of which are described below, may be incorporated into a number of different systems and devices including camera, scanners, printers, computers, mobile devices, including those devices with a display or camera capabilities, multimedia devices, and the like. The embodiments of the present invention may be implemented in software, hardware, firmware, or combinations thereof. Components, or modules, shown in block diagrams are illustrative of exemplary embodiments of the invention and are meant to avoid obscuring the invention. It shall also be understood that throughout this discussion that components may be described as separate functional units, which may comprise sub-units, but those skilled in the art will recognize that the various components, or portions thereof, may be divided into separate components or may be integrated together, including integrating within a single system or component. 
     Furthermore, connections between components/modules within the figures are not intended to be limited to direct connections. Rather, data between these components may be modified, re-formatted, or otherwise changed by intermediary components. Also, additional or fewer connections may be used. It shall also be noted that the terms “coupled” or “communicatively coupled” shall be understood to include direct connections, indirect connections through one or more intermediary devices, and wireless connections. 
     Reference in the specification to “one embodiment” or “an embodiment” means that a particular feature, structure, characteristic, or function described in connection with the embodiment is included in at least one embodiment of the invention and may be in more than one embodiment. The appearances of the phrase “in one embodiment” or “in an embodiment” in various places in the specification are not necessarily all referring to the same embodiment. 
     As previously noted, some conventional image compression efforts to reduce file sizes of images have yielded unsatisfactory results. For example, when a high compression ratio is used, the compressed images show disturbing artifacts, and when a low compression ration is used, the resulting file size is not substantially reduced. Thus, neither approach is particularly satisfactory. 
     Consider, by way of example, the exemplary document  100 , having different content regions, which is depicted in  FIG. 1 . As shown, the document  100  has a first region  120  containing text, a second region  130  comprising a line art flow chart, a third region  140  containing text, a fourth region  150  containing text integrated within an image, and a fifth region  110  representing the background of the document  100 . Using a high-compression algorithm for the document may result in blurring of the text or line art; while using a low-compression algorithm may not satisfactorily reduce the file size. 
     Context-based image compression can alleviate the aforementioned problems. In context-based image compression, an image is separated into layers, which can be compressed independently using different compression strategies. If each of such image layers is generated so that it contains less sharp color changes in neighboring pixels, the compression ratio can be very high while maintaining very good image quality. A significant issue in context-based image compression is determining how to decompose the image into layers. Decomposition methods may have a significant effect on the compressibility of the layers and on the quality of the image that is reconstructed from the compressed layers. Disclosed herein are robust approaches to generate layered images, which are referred to herein as background and foreground images layers, for each input image with the aim to enhance quality of compressed images while achieving high compression ratio for applications using context-based image compression. 
       FIG. 2  illustrates a system for document copy enhancement and color compensation according to an embodiment of the invention. Each of the components within the system will be described in greater detail in the sections below. 
     A scanner  205  is used to create a digital version of a hard copy document, that is, a physical medium, e.g., paper, document. Other devices, such as a printer, copier, camera, or facsimile machine may also be used to generate a digital version of a physical document. The term “scanned document” is used herein to refer to a digital document generated from a physical document by any of these or other known means. A data converter  210 , in this embodiment an RGB to CIE Lab converter, is used to convert the scanned document, which is typically captured in RGB color space, into a device-independent perceptually-uniform color space. This conversion typically employs a color profile of the scanner  205  and a white point reference color. 
     The device independent data of the scanned document is provided to an image labeler  220  that tentatively identifies and labels content types, such as text, images and background, within the scanned document. In identifying content types, the image labeler  220  identifies and labels edge types in the scanned document. A subsequent analysis of the amount and types of edges within a particular region is performed to tentatively label corresponding content. This labeled content and edges may be subsequently refined to improve the classification of image regions using information about other labels in the neighborhood of each label within the scanned document. 
     A background estimator and label refiner  230  builds a three-dimensional histogram of colors that have been marked as tentative background pixels. Using this histogram, a background color is selected and the colors in the histogram mapped to a preferred background color (e.g., a white background). A background saturation threshold  245  is used to exclude strongly saturated colors from the histogram and improve the resolution of the histogram by limiting the range of examined colors. The identification of background pixels is refined so that colors from pixels labeled as halftone are not included as background pixels. 
     Once the background pixels have been identified and the pixels have been labeled, a multi-region enhancer  250  further enhances the labeled document. This enhancement may include a region adaptive de-screening process and a region dependent sharpening. After the multi-region enhancement, the labeled document can be used to perform differential enhancement for viewing or printing. 
     In one embodiment of the invention, the labeled and enhanced document  255  is sent to a printer  260 , which may require higher levels of sharpening for high-quality text as compared to, for example, text that is viewed on a computer screen. Refined label information  254  may also be used to switch between printer/screens color tables to improve the rendering of non-halftone edges that typically correspond to text on background regions. For example, lower frequency screens may be used over pixels labeled halftone edge or halftone to improve tone stability in devices such as color laser printers. 
     The labeled and enhanced document  256  may also be separated into text, line art, image layers and other content to facilitate high-compression storage by storage device  270  that may use specialized codecs for each type of content. The labeled and enhanced document may also be used to pre-process and present pertinent regions, such as text regions, to an optical character recognition system. 
     C. Image Labeling 
       FIG. 3  illustrates an image labeler  220  according to one embodiment of the invention. The image labeler  220  may include an edge location identifier  305 , an edge type identifier  320 , a tentative label assigner  330 , and a label refiner  340 . In one embodiment of the invention, the edge location identifier  305  comprises a first gradient module  310  and a second gradient module  315  that compute gradient values for each pixel, or at least a plurality of pixels, in the scanned document. These gradient calculations are used to identify edge pixels, which are pixels that relate to an edge of text, an image, or other content within the document copy. These edge pixels may be further characterized as fine edges, coarse edges, very coarse edges, or other types of edges and correspond to using different filter kernels for edge computation. One skilled in the art will recognize that various mathematical calculations may be performed in which edge pixels within a document copy may be identified; these various calculation methods are intended to be within the scope of this invention. 
     In one embodiment, the first gradient module  310  computes a first gradient value representing a horizontally-oriented edge element, for each pixel, according to:
 
 h   ij =(( L   i+1,j   ≧L   ij )?1:−1)×∥Lab i+1,j −Lab ij ∥
 
     The second gradient module  315  computes a second gradient value representing a vertically-oriented edge element, for each pixel, according to:
 
 v   ij =(( L   i,j+1   ≧L   ij )?1:−1)×∥Lab i,j+1 −Lab ij ∥
 
     where Lab ij  denotes the CIE Lab color at pixel (i,j), and ∥ ∥ denotes the L 2  norm. A threshold is defined to which a comparison is made in order to identify edge pixels. For example, consider an edge threshold of t e , a pixel (i,j) is identified an edge pixel if one of the following is true:
 
((abs( h   ij )&gt; t   e )^( h   ij &gt;0)),  (1)
 
((abs( h   i−1,j )&gt; t   e )^( h   i−1,j &lt;0)),  (2)
 
((abs( v   ij )&gt; t   e )^( v   ij &gt;0)), or  (3)
 
((abs( v   i,j−1 )&gt; t   e )^( v   i,j−1 &lt;0))  (4)
 
     where abs(x) denotes the absolute value of x. 
     By comparing the calculated pixel gradients, within the scanned document, to each of the four criteria, edge pixels are identified. 
     The edge type identifier  320  identifies a type of edge for each of the identified edge pixels. According to one embodiment of the invention, two low-pass filters are used on the gradient pixels in order to identify different types of edges associated with the pixels. These low-pass filters process a particular region in order to identify edges within a region and ascertain a type of edge for each of the identified edges. By using multiple filters on the pixels, a relatively more complete capture of pixel information and edge information is provided. In one embodiment of the invention, edges may be defined at three different levels to be fine edges, coarse edges and very coarse edges. The fine edges provide well localized edges with relatively sharp boundaries. The coarse and very coarse edges are typically the result from halftones from true-image feature edges and generally provide poor information about edge location and fine-detail information about the edge itself. The edge type identification module  320  labels each of the edge pixels according to its identified type. 
     The tentative label assigner  330  estimates tentative halftone and background regions within the scanned document. According to one embodiment, this estimation is performed by analyzing the fine edges identified by the edge type identifier  320 . In particular, the number or quantity of identified fine edges is compared to a threshold value, and if the number of fine edges within a particular region is below the threshold, the region is tentatively labeled as background. These tentatively labeled background regions may be later refined based on a color histogram analysis of labeled background pixels, within the region, to identify which pixels actually correspond to the document background. 
     The tentative label assigner  330  may also identify tentative halftone pixels based on an analysis of identified fine edges. In one embodiment, the identified fine edges are compared to another threshold value, and if the number of fine edges within a region around an edge pixel exceeds the threshold, the edge pixel is tentatively labeled as a halftone edge. Content labeling may also include an analysis of coarse and very coarse edges. For example, if a coarse edge exists in a tentative background area, the edge is labeled as a non-halftone edge. If a very coarse edge exists in a tentative halftone area, the edge is labeled as a halftone edge. 
     The label and position refiner  340  refines the labels within the scanned document by analyzing tentatively labeled edges within a particular region. In one embodiment of the invention, tentative labels are refined according to the following parameters: 
     (a) if a connected set of non-halftone edge pixels is adjacent to a halftone edge pixel, all the pixels in the connected set are relabeled as halftone edges, or vice versa; 
     (b) non-halftone edges are refined to obtain edges of higher location accuracy; and 
     (c) halftone edge components that are below a particular size are relabeled as halftone pixels. 
     The label and position refiner  340  may also refine the position of non-halftone edges. According to one embodiment, non-halftone position is refined according to the following parameters: 
     (a) determine fine edge components that have a partial overlap with a non-halftone edge component; and 
     (b) replace the entire non-halftone edge component, which has a partial overlap with a fine edge component and do not overlap a halftone edge component, with a corresponding set of fine edge components. 
     These fine edge components are labeled as non-halftone edges. Because the fine edges have higher location accuracy, the location of the non-halftone edges is dramatically improved. 
       FIG. 4  illustrates an exemplary result of the above-described label and position refinement. Non-halftone edges, prior to refinement,  410  are shown and characters  430  therein appear thick and imprecise. Comparatively, after refinement, non-halftone edges  420  are shown and characters  440  therein are much more precise because of the superior location accuracy of the character fine edges that replaced the coarse non-halftone edges. 
     D. Background Estimation and Label Refinement 
       FIG. 5  is a flowchart illustrating a method for background estimation and label refinement according to one embodiment of the invention. As described above, an analysis of fine edges in a scanned document provides tentative labeling of background pixels. These tentative background pixels may be refined and their color appropriately modified by mapping the background color within the original document to a preferred color space for the scanned document, such as one related to a white background. 
     A three-dimensional color histogram in a device-independent format, such as CIE Lab, may be generated  510  for all of the colors that have been marked as tentative background pixels. One skilled in the art will recognize that various color histograms may be generated in accordance with the present invention. 
     In this embodiment, the lightest color peak within the three-dimensional color histogram is selected  520  as the color corresponding to the background of the document. This selected color peak may be analyzed to identify an appropriate method to map the remaining colors within the background color cluster and/or remaining colors within the scanned document to a color space corresponding to a preferred background color for the document copy. For example, the selected lightest color peak, or page color saturation, may be compared  530  to a particular threshold to identify the extent of the color distance between the background color cluster of the scanned document and the preferred color background of the document copy. 
     If the page color saturation is below the particular threshold, the image colors may be remapped by translating  540  all the colors within the page background color cluster. Furthermore, other colors, within the scanned document, may be translated  550  smoothly using a rapidly decaying translation vector that decays based on the distance of the color from the page background color. One skilled in the art will recognize that colors outside of the background color cluster may be translated using various techniques known within the art. 
     If the page color saturation is above the particular threshold, all of the colors should potentially be translated to compensate for the scanned document&#39;s strong color saturation level. In one embodiment, all colors within the scanned document are remapped  560  using the page color and the reference white point for the RGB—XYZ—Lab transformation. This transformation allows background removal of image content printed on strongly colored paper. This color transformation process may be accomplished by various methods known within the art. 
     The removal of background color allows for refinement of the tentatively identified background pixels within the scanned document. In one embodiment, all of the tentative background pixels within the background color cluster are labeled as true background pixels, and the remaining tentative background pixels are labeled as halftone pixels. These halftone pixels may be subsequently removed from the background. 
       FIG. 6  illustrates an exemplary result of the label-aided background removal described above. As shown, a scanned document on strongly colored paper  610  may result in a grayish background if printed or viewed in a black and white environment. However, by removing the strongly colored background, and to the extent appropriate, mapping other colors to the white background, a sharper image  620  is provided. In a color environment, all image colors are appropriately remapped so that consistent color relationships are maintained with respect to the page background color. 
     E. Multi-Region Enhancement 
       FIG. 7  illustrates a multi-region enhancer  250  according to one embodiment of the invention. The multi-region enhancer  250  includes a region adaptive descreener  730  and a region dependent sharpener  740 . The multi-region enhancer  250  uses the labeled pixels, such as the edge pixels and background pixels, to enhance the content and images in the scanned document. 
     In one embodiment of the invention, the enhancement occurs in two different stages. In the first stage, the region adaptive descreener  730  removes the halftones within the scanned document using a method such as the one described in  FIG. 8 . In the second stage, the region dependent sharpener  740  sharpens the image features using techniques known within the art, such as a standard unsharp masking algorithm. 
       FIG. 8  illustrates a method of multi-region enhancement, within a scanned document according to one embodiment of the invention. This enhancement method leverages the labeled regions within the scanned document, and provides region dependent descreening. 
     A low pass filter is applied  810  to all halftone and background pixels provided the support of the kernel does not contain a pixel labeled as a halftone edge or a non-halftone edge. The remaining unfiltered locations within the scanned document are filtered by one of two sets of oriented filter kernels. (Other sets of oriented filter kernels, having various characteristics, may also be used in other embodiments of the invention.) The first set, denoted as thick kernels, have larger support than the second set, denoted thin kernels. For each unfiltered location, if a thick kernel can be used for filtering, it is used. However, a thick filter may be used at a given location only if its support does not contain any non-halftone edge pixels. Otherwise, a thin filter kernel is used. 
     A first analysis of a region or image location within the scanned document may be performed to determine  820  whether a thick kernel may be applied. If any thick kernel can be applied, the filtered result may be selected  830  from the output of all the applicable thick kernels based on which output is closest to the color at the image location that is to be filtered. 
     Comparatively, if a thick kernel cannot be applied to the image location, a thin kernel is selected from all of the available thin kernels. Similar to the application of a thick kernel, if any thin kernel can be applied, the filtered result may be selected  840  from the output of all the applicable thin kernels based on which output is closest to the color at the image location that is to be filtered. 
     Analogous to sigma-filters, the weight of a given pixel color may be determined by a number of different factors including its spatial distance and its color distance to the pixel at the location to be filtered. Furthermore, instead of using a sharp color-distance cutoff, a decaying function of color distance may be used. One skilled in the art will recognize that other characteristics of a pixel may be considered in selecting an appropriate filter kernel. 
     In one embodiment of the invention, the input to a filter (e.g., thick or thin kernel) may be selected from the output array of previous filtered pixels whenever it is available. This output array dependent selection allows the use of a filtered estimate of pixel color for subsequent filtering whenever such an estimate is available. Additionally, this approach reduces the risk of inadequate suppression of halftones near halftone edge pixels. Furthermore, this approach may reduce the variance near halftone edge pixels resulting in a more smooth tone appearance. 
     Once the pixel image regions are filtered, a region dependent sharpening tool is applied  850 . In one embodiment, a fast unsharp masking algorithm is used for this sharpening, which may, for example, only sharpen the L channel of the Lab image. Halftone edges and non-halftone edges may also be sharpened by similar sharpening factors to minimize visual artifacts. 
     The application of these image sharpening methods may result in noise in color regions and/or thickening of text regions within the scanned document. According to one embodiment of the invention, asymmetrical clipping may be performed on the sharpened L channel to minimize this noise and thickening of text regions. The actual level of clipping may vary and be determined using various methods. For example, the level of clipping may be determined by the saturation of the pixel itself. The saturation of a particular pixel may be defined by:
 
 s   Lab =√{square root over (( a   2   +b   2 ))}
 
     Let f min  denotes the clip factor and f s  be a smooth function of s Lab  and where: 
     (1) f s  is zero when s Lab  is zero; and 
     (2) f s  is 1 for a sufficiently large s Lab . 
     The pixel saturation is clipped to the range [Lf min f s , 100] by sharpening a pixel with this L-value. One skilled in the art will recognize that various clipping techniques may be applied to a saturated pixel. 
     The processed scanned document may be provided to various applications and devices for enhanced rendering. These devices include but are not limited to various types of printers and display devices. Additionally, the labeled image may be used to switch between screens and color tables to improve the rendering of both halftone and non-halftone edges. As discussed in more detail below, it shall be noted that the labeled image may used to parse a document into various components, such as text and images, to facilitate compression or other processing of the document. 
     F. Compression System Implementations 
     As noted above, system  200 , or portions thereof, may be used to help generate high-compression documents. A compression system may comprise system  200 , or portions thereof, or may receive an output from one or more of the components of system  200  to generate a high-quality, high-compression document. A “document” shall be construed to mean a digital file, the data that comprises the document (or a portion thereof), including without limitation pixel values, or the display of the digital file, which may be displayed electronically (such as on a computer screen) or displayed on a physical medium (such as a printed paper). As used herein, the terms “image” and “document” may be used interchangeably. 
     In embodiments, the document compression system comprises high-quality high-compression document generation technology that builds an image layer description for an input image using pixel labeling and image segmentation techniques. This layer description is generated based on document properties to support optimized strategies to enhance and compress images. The compressed document may represent a composite of compressed image layers and may have enhanced visual quality and much smaller file size compared to conventional approaches. In an embodiment, the resulting high-quality high-compression document may be in a portable document format (“PDF”), but one skilled in the art shall recognize that other viewers and formats may be employed. 
     1. Exemplary Embodiment of a Document Compression System 
       FIG. 9  illustrates an exemplary system  900  for generating a high-quality high-compression document according to an embodiment of the invention. As depicted in  FIG. 9 , a digital image  905  is provided to the compression system  900 . Because the present invention may operate on a portion of an input image, it shall be understood that input image  905  may be construed to be a portion of an input image. In embodiments, the input image  905  may be an image obtained from a scanner, camera, copier, facsimile machine, or copier. In embodiments, the input image may be a digitally generated image. The components of the embodiment of the compression system  900  are described below. 
     a) Pixel Labeler 
     The compression system  900  comprises a pixel labeler  910  that receives an input image  905  and labels the image  905  at the pixel level. In an embodiment, each pixel of the image  905  may be assigned a label, such as background, halftone edge, non-halftone edge, or halftone pixel. This labeling may be referred to as X-labeling because, in embodiments, each pixel may be classified with a label selected from a group. In an embodiment, the group may comprise, but is not limited to, a background label, a halftone edge label, a non-halftone edge label, or a halftone label. It shall be noted the number, type, and classification of labels may vary. It shall also be noted that in embodiments in which the input image is a digitally generated image (such as from a computer or a digital camera) as opposed to a scanned image, halftone may not exist within the image and halftone labels may not be used. 
     The pixel labeler  910  may comprise all of system  200  or a portion of system  200 , such as image labeler  220 . In an embodiment, the pixel labeler  910  may represent that portion of system  200  that outputs enhanced image  255 ,  256  or refined image  254 . 
     b) Color Matcher 
     In an embodiment, given a scanner, printer, copier, or display color profile  970 , color matcher  915  may adjust the image  905 , or one or more layers derived from the image, so that the image colors match the original document. It should be noted that color matching may be beneficial when the image information is being communicated between devices, such as, for example from one copier to another copier or to a computer screen. Accordingly, the color matcher may provide adjustments given the intended output display. In embodiments, the labeled image may be used to perform differential enhancement for viewing or printing of the compressed image. For example, printing often requires higher levels of sharpening for high-quality text than what would be required for viewing on a computer screen. The label information may also be used to switch between printer/screen color tables to improve the rendering of the image or portions thereof, such as non-halftone edges that typically correspond to text on background regions. For example, lower frequency screens may be used over pixels labeled halftone edge or halftone to improve tone stability in devices such as color laser printers and may also provide a greater number of tones. 
     c) Background Label Refiner and Paper Color Estimator 
     In an embodiment, a background label refiner and paper color estimator  920  may be used to further refine the labeling of background pixels and, in embodiments, to estimate the color of the background of the document  905 . In an embodiment, background label refiner and paper color estimator  920  may be the background estimator and label refiner  230  of system  200  and may also include the RGB to CIE Lab component  240 . Alternatively, background label refiner and paper color estimator  920  may be a component capable of performing functionality described herein. 
     In an embodiment, background pixels labeled by component  910  may have no texture patterns in the pixel neighborhood. These pixels may not correspond to paper color of a scanned document. To find true background pixels, histogram-like statistics may be used to identify a dominant background color. In an embodiment, background pixels not corresponding to true background color may be labeled as halftone pixels. 
       FIG. 10  depicts an embodiment of a method for determining a background color according to an embodiment of the present invention, which may be performed by background label refiner and paper color estimator  920 . The depicted embodiment begins by computing ( 1005 ) the mean and variance of the pixels with a “background” label. If the mean color indicates ( 1010 ) an overall bright background, all or some of the background pixels with darker colors may be relabeled ( 1015 ) as halftone pixels. If the mean color is not bright and the color variance is below a threshold level ( 1020 ), which indicates that the background color is consistent, no background pixels are relabeled ( 1025 ). However, if the color variance of the background pixels exceeds a threshold value ( 1030 ), a determination is made ( 1030 ) if there are a sufficient number of very bright background pixels, such as for example, if their number exceeds a threshold value. If a sufficient number of bright background pixels exists, all or some of the darker background pixels may be relabeled ( 1035 ) as halftone pixels. If a sufficient number of bright background pixels do not exist, the image border may be examined. In an embodiment, the image border may comprise the outer region of the image  905 . If the image border does not have a sufficient number of background pixels ( 1040 ), that is, their number does not exceed a threshold value, then no background pixels are relabeled ( 1055 ). If the image border has a sufficient number of background pixels ( 1040 ), the mean color of the background pixels in the image border may be set ( 1045 ) as the mean background color. Background pixels with color values that are more than a threshold value ( 1050 ) from the mean background color may be relabeled as halftone pixels. In an embodiment, a pixel with an unknown label that has a color close to the background color, for example a yellow pixel when the background color is white or close to white, may be labeled as a background pixel if any of its neighbor pixels are background pixels; otherwise, it may be labeled as a halftone pixel. In an embodiment, the mean background color may be regarded as the paper color of the scanned document. For the above presented embodiment, the threshold values/sufficient numbers may the same or different and may be preset or user selected. 
     d) Background Mask and Edge Mask Generator 
     In an embodiment, after refining the background labels, high-frequency image noise, such as Moire pattern artifacts, may be removed or reduced by applying a median filter on all pixels except those pixels labeled as edge pixels. In an embodiment, the median filter may be applied to all pixels except those labeled as edge pixels and all pixels within one pixel of an edge pixel. 
     Based on revised labels, background mask and edge mask generator  925  may generate a background mask and an edge mask of the input image. In an embodiment, the background mask may comprise all revised labeled pixels with a background designation. In an embodiment, the edge mask may comprise all revised labeled pixels that are labeled as halftone edge and non-halftone edge pixels. The edge mask may be dilated in order to perform a reliable color consistency analysis. In an embodiment, the edge mask may be dilated inward to the halftone regions but not into background regions. In embodiments, dilated pixels may be examined to determine whether the pixel should be dilated to an edge pixel. In an embodiment, if a dilated edge pixel is brighter than its original edge pixel, the dilated edge label may be removed and the pixel may be returned to its prior label. By limiting the edge mask dilation to the inward regions, the text in the rendered compressed document is not made thicker than in the original document. The background mask and edge mask may be utilized in subsequent processing. 
     e) Text-in-Background Extractor 
     In an embodiment, a text-in-background extractor  930  may classify the pixels of the edge mask into different types according to the types of image blocks or regions with which they are associated. If an edge pixel is on a small image block or on a thin image block, it may be treated as a potential text pixel on background, which may be extracted by the text-in-background extractor  930 . Otherwise, in an embodiment, it may be treated as an edge pixel on a potential color-consistent region, such as for example the letter “A” of the “ABC” letters  120  depicted in  FIG. 1 , which may be extracted by the solid region extractor  940 . References herein to classifying pixels as “text,” “text in background,” “background text,” or the like shall be construed to mean pixels in the input image that would benefit from having a higher quality compression in order to preserve a desired level of detail of the pixels, regardless of whether those pixels form legible symbols or any form of lexical mark. Examples of such items that may be classified herein as “text” may include, but are not limited to, alphanumeric characters, lexical marks, symbols, graphics, line art, charts, fine detail items, and the like. The above-mentioned terms may also be used interchangeably with “non-background region” or “non-background region pixels,” meaning those pixels that are preferably included in the foreground image layer. 
     In an embodiment, text-in-background extractor  925  may identify two types of non-background regions by examining the non-background pixels of the background mask. The first type of non-background regions may comprise small-size regions, and the second type of non-background regions may comprise thin lines. In an embodiment, all or a substantial number of surrounding pixels of both of these types of non-background regions may be required to be background pixels. 
     In an embodiment, small-size regions may be extracted by performing a connected component analysis on non-background pixels, which may include edge pixels and halftone pixels. In an embodiment, a connected component analysis may be performed on the non-background pixels of the background mask. In an embodiment, if the number of pixels in a connected component group of pixels is below a threshold size value and the group of pixels is surrounded or substantially surrounded by background pixels, the group of pixels corresponding to the connected component group may be labeled as a non-background region. 
     In an embodiment, thin lines may be extracted by examining the pixels labeled as either edge pixels (non-halftone edge or halftone edge) or halftone pixels that may be surrounded or substantially surrounded by background pixels and that meet a threshold size limitation, for example, by fitting within at least one bounding region. Bounding regions may be used to set one or more threshold size values for the connected component groups. A bounding region&#39;s size may refer to its pixel area or may be defined by one or more pixel dimensions that form the bounding region (e.g., x pixels by y pixels). The threshold size limitation of a bounding region may also be expressed or measured in units other than pixels. 
     In an embodiment, thin line regions may be identified by performing a connected component analysis on the non-background pixels of the background mask. In an embodiment, one or more vertical bounding rectangular boxes and one or more horizontal bounding rectangular boxes may be used to identify thin lines that may be labeled as non-background regions. For example, horizontal and vertical sections of the line art  130  in  FIG. 1  may be identified as non-background regions using horizontal and vertical bounding boxes. In alternative embodiments, different or additional bounding region configurations may be used. 
     f) Mode Selection 
     Embodiments of the document compression system  900  may support one or more modes of processing. In embodiments, the following three different operation modes may be utilized to enhance and compress images based on different document properties. One mode may be referred to as “Photo Mode,” which may be utilized for documents dominated with images that are best preserved in high quality. A second mode may be referred to as “Magazine Mode,” which may be utilized for documents that mix images and text. A third mode may be referred to as “Text Mode,” which may be utilized for documents where text is the most important feature. 
     In embodiments, a difference between these three modes may lie on the extraction of text or sharp color boundaries on the halftone images. The photo mode may be configured to assume that no text lies on halftone image. The magazine mode may be configured to preserve text lying on halftone image but assumes such text is not highly significant. The text mode may be configured to extract all text regions in the document and to ensure high readability of the extracted text. In embodiments, text on background may be extracted for all three modes. 
     In the embodiment of a compression system depicted in  FIG. 9 , system  900  may allow for mode selection  935 . In an embodiment, the mode section may be made by a user. Alternatively, the mode selection may be made by system  900  based upon one or more factors about the input image  905 , including but not limited to, pixel labels, the amount of text that has been identified, background color, amount of background, color distributions, and the like. 
     In the depicted embodiment, if the text or magazine modes are selected, the system proceeds to the solid region extractor  940 , which may be used to extract halftone text lying on image regions within the image  905 . If neither the text nor the magazine modes are selected, the system  900  proceeds to the image layer separator  945 . The operation of the solid region extractor  940  and the image layer separator  945  are discussed in more detail below. 
     g) Solid Regions Extractor 
     In an embodiment, the solid region extractor  940  may be used to extract solid regions, which may include text lying on regions within an image. In an embodiment, the extraction may be done by finding solid color region in the image document  905 . The solid regions may be classified as non-background regions and placed in the foreground image layer. 
     In an embodiment, solid color regions, such as background text or halftone text, may be determined by a color consistency analysis using edge pixels, where text shall be understood to have the broader meaning as set forth previously. In an embodiment, the color consistency analysis finds color-consistent regions at least partially surrounded by edge pixels using vertical scan lines, horizontal scan lines, or both. 
       FIG. 11  depicts an embodiment of a method for performing a color consistency analysis to extract regions, such as text items lying on image regions, in a document  905 . A line-by-line scan may be performed ( 1105 ) using the edge mask, or a portion thereof. For each line scan, the line segments or regions, if any, are identified ( 1110 ) by pixel label type. It shall be noted that a line segment or region may be one or many pixels in size. 
     Consider, by way of illustration, a portion of document  905  depicted in  FIG. 12 . Illustrated in  FIG. 12  are two horizontal line scans  1220 ,  1225  of a portion of document  905  containing the letter “A.” The border  1205  of the letter  120 -A represents the dilated edge pixels obtained from the edge mask. The interior pixels  1210  represent halftone or non-halftone pixels. The letter  120 -A resides on an image region  1215  that may be background, halftone, or non-halftone pixels. Because line scan  1220  does not intersect any of letter A  120 -A, it is a single line segment. Note, however, that line scan  1225  intersects letter A  120 -A at several places. The first segment, a, comprises the pixels from the start of the line scan until the start of the edge pixels  1205  of the letter A  120 -A. Segments b and d comprise edge pixels of the edge of the letter A  120 -A; segment c comprises interior pixels of the letter A  120 -A between the two edge pixel boundary segments, b and d; and so forth. A comparison is made ( 1115 ) of the segments to identify regions, such as text regions. In an embodiment, the color of segment c may be compared ( 1115 ) against the color of segment b with the color of segment d. If the difference in color between segment c and the edge segments b and d is below some threshold level, then the segments may be considered as part of a single solid region and may be classified ( 1130 ) as such. If the difference in color between segment c and the edge segments b and d exceeds the threshold level, then the segments are not considered classified ( 1125 ) as a solid color region. In an embodiment, the color of the segment bounded by edge pixels, in this case segment c, may be compared against an average of the colors of the two bounding edge pixels segments, segments b and d. If the difference between the values is below a threshold level, as shown in the following formula: 
     
       
         
           
             
               
                  
                 
                   c 
                   - 
                   
                     
                       b 
                       + 
                       d 
                     
                     2 
                   
                 
                  
               
               &lt; 
               
                 T 
                 1 
               
             
             , 
           
         
       
     
     the segments may be classified as a solid color region ( 1130 ). In an embodiment, the average (mean, median, or mode) color of the segments may be used in the comparison process. In an embodiment, any background pixels that reside along a line segment that has been classified as a solid region may not be classified as solid region but may remain as background pixels. 
     In an embodiment, the edge labels may be added to all or a portion of the image to avoid artifacts when items, such as text, appears at the image border. Consider, by way of illustration, the region  1230  appearing in  FIG. 12 , wherein edge pixels  1235  along the image border have been added. In the depiction, the darker exterior indicates pixels labeled as edge pixels and the lighter interior pixels represent halftone pixels. When performing the color consistency analysis, a line scan will identify a line segment through region  1230  as being a solid region, assuming the other conditions are met, because it will be bounded by two edge pixels  1235  and  1240 . 
     In embodiments, additional conditions may be applied ( 1120 ) as part of the color consistency analysis. In an embodiment, an additional condition may include examining the neighboring pixels of the immediately preceding line segment. If the neighboring pixels are not substantially edge pixels or are not substantially identified as a solid region during its color consistency analysis, then the line segment may not be identified ( 1125 ) as a solid region. 
     Consider, for purposes of illustration, the following example. Assuming that the segments b, c, and d met the conditions that the difference in color between segment c and the edge segments b and d was below a threshold level, the neighboring pixels to combined line segment b+c+d that were previously examined may be checked to determine if a number exceeding a threshold value of those pixels were also identified as a solid region. For example, assuming the line-by-line scan proceeded from top to bottom, the line segment of pixel neighboring line segment b+c+d would be the pixels directly above, as identified by line  1245  in  FIG. 12 . In an embodiment, if a threshold number of pixels on line  1245  had been identified as a solid region, line segment b+c+d would also be identified as a solid region; otherwise, line segment b+c+d would not be identified as a solid region. 
     In an embodiment, the color consistency analysis may also include one or more threshold size restrictions or limitations. For example, if combined line segment b+c+d exceeds a threshold number of pixels, it may also be excluded from being classed as a solid color region. One skilled in the art shall recognize that a threshold size limit may be placed on any combination of the segments or any portion thereof. 
     In an embodiment, a restriction may be placed on the brightness of pixels that may be classified as a solid region. For example, a set range of light colors may be excluded from being classified as a solid region. 
     One skilled in the art will recognize that the threshold levels and restrictions, including but not limited to, the maximum color variation, the maximum width, and the brightness of colors of such regions may be configured differently according to a number of factors, including but not limited to the operation mode (text, magazine, or photo) and the type of desired text (background or halftone). 
     h) Image Layer Separator 
     In embodiments, the definitions of the background and foreground image layers may differ depending upon the operation mode. In an embodiment, for photo mode, the foreground layer may be defined to comprise all background text and the background layer may be defined to comprise all other pixels in the image  905 ; and for magazine or text mode, the foreground layer may comprise all background text and solid color regions, and the background layer may comprise all other pixels. It shall be noted that all pixels in the foreground image layer may be referred to as non-background regions, non-background pixels, and/or non-background region pixels. In the embodiment depicted in  FIG. 9 , image layer separator  945  generates a foreground image layer and a background image layer of the image  905  using the definitions for the selected mode. In an embodiment, a mask is also generated by image layer separator  945 . 
     After separating an entire image document  905  into two layers, the document compression system  900  may perform color interpolation to fill in blank areas in the image layers. Because compression is better if the image layer has consistent color,  FIG. 13  depicts an embodiment of a set  1300  of two filters  1310 ,  1320  that may be applied to a foreground and/or background image layers to fill the image layer. Filter  1310  may be applied to the image layer starting at the top left of the image layer and moving to the bottom right corner. Filter  1320  may be applied to the image layer starting in the bottom right of the image layer and moving to the top left corner. In the depicted embodiment, a filtered pixel value is the average of the selected neighboring pixel values. In an embodiment, for both filters  1310 ,  1320 , if a selected pixel value in the image is unknown (meaning that the pixel value is in the other image layer), the filters  1310 ,  1320  may ignore those values. In an embodiment, open areas in the background image layer corresponding to background text may be filled with the background color. 
     i) Layer-based Color Enhancer 
     In embodiments, layer-based color enhancer  950  may enhance one or more of the layers. In an embodiment, all background text on the foreground layer may be smoothed and color enhanced. In the text mode, solid color regions may also be enhanced. 
     In embodiments, the color enhancement may be conditionally performed based on local color statistics so that the composite of the two layers has good text readability and also avoids aggressive enhancement for misclassified text pixels. For example, in an embodiment, color enhancement may be performed by darkening text color in order to improve text readability. In one embodiment, for each pixel on the foreground layer, a pixel&#39;s color may be replaced with the color of the darkest pixel within a defined neighborhood of the pixel, if the color difference, or intensity gradient, between the pixel and the average of the pixels within the defined neighborhood exceeds a threshold value. In an alternative embodiment, a pixel&#39;s color may be replaced with the average color of the pixels in a defined neighborhood if the color difference, or intensity gradient, between the pixel and the average of the pixels within the defined neighborhood exceeds a threshold value. In an embodiment, the defined neighborhood may be a 3×3 kernel. 
     In embodiments, smoothing may be performed by applying a filtering operation to one or more of the layers. In one embodiment, the filtering operation may comprise setting a pixel value to the average value of the like-labeled pixels within a neighborhood of that pixel. Consider, by way of illustration, an exemplary 3×3 portion of the image mask depicted in  FIG. 14 , wherein a “0” identifies a pixel labeled as background and a “1” identifies a pixel as having been labeled as foreground. In an embodiment, the center pixel color value, which is a foreground pixel, may be set as the average color value of all foreground pixels within the region of support of the 3×3 filter. In an embodiment, the average value calculation may include the value of the center pixel. 
     j) Background Color Suppressor 
     Because the true background color may have been estimated, different levels of background or paper color suppression may be applied by the background color suppressor  955 . 
     In an embodiment, the brightness of the mean background color in Hue-Saturation-Value (HSV) color space may be determined. If the background is not within a light color range, the background may not be suppressed in order to reflect the true background color. In an embodiment, a small background block may not be suppressed. 
     In an embodiment, in the text mode, big background block may be replaced with white color. In an embodiment, background pixels may be completely replaced with white color if the true background color is bright and close to white color. Such operation removes yellowish color appearance in scanned or camera-captured documents. 
     In an embodiment, in the magazine mode, background color may be shifted toward white color. In an embodiment, background pixels may be shifted in color halfway toward white color. 
     k) Layer-based Compression and Composite Generator 
     In an embodiment, a composite file, such as a portable document format (PDF) file, may be generated as a representation of an input image by stacking the background and foreground layer images using a mask. One skilled in the art will recognize that other composites generators/viewers, other than PDF, may be used to create or view a composite output file  975 . 
     In an embodiment, layer-based compression  960  may compress the background and foreground image layers separately using a compression algorithm. In an embodiment, a JPEG compression with the same or different compression ratios may be used to compress the background and foreground image layers. Because, in embodiments, the two image layers are generated by separating text from background and separating neighboring regions of sharp color changes, both layers can be highly compressed. In particular, since the foreground image layer may consist of smooth and solid color regions, the JPEG quality may be set very low (e.g. 25), but a high-quality composite image is still able to be obtained as an output  975 . 
     Composite generator  965  may used the layers and mask to form a composite document  975 . In an embodiment, the high-quality composite of two compressed JPEG image layers (foreground and background) may be achieved by maintaining an image mask that identifies foreground pixels. In embodiments, this mask may be kept intact with lossless compression, such as JBIG2, to minimize file size. 
     In an embodiment, the foreground and background image layers may be downsampled, such as by a ratio of 3:1, and the mask may be maintained at a ratio of 1:1. In embodiments, system  900 , system  1900 , system  2400 , or each of them may include a downsampler to downsample the foreground and/or background image layers. 
     For purposes of illustration,  FIGS. 15-18  depict an example of an input image  1500  and the resulting layers used for compressing the input image.  FIG. 15  depicts the input image  1500 .  FIG. 16A  depicts the background image layer  1600  for the input image  1500 , and  FIG. 16B  depicts the foreground image layer  1610 .  FIG. 17  depicts the mask layer  1700 . The mask layer indicates which pixels should be selected from the foreground image and which pixels should be selected from the background pixels to generate a composite image. An example of a composite image  1800  is depicted in  FIG. 18 . 
     2. Alternative Embodiments of Document Compression 
     Described below are various approaches to create a compressed document from a captured image, which may be captured by a camera, scanner, copier, or the like. Examples depicted herein may refer to whiteboard images; however, it shall be noted that the present invention is not limited to whiteboards, but may be used with images captured from papers, cards, magazines, easels, signs, chalkboards, etc. An application of the present invention includes, but is not limited, creating a compressed document (such as a PDF document) from an image of whiteboards, papers, cards, magazines, easels, chalkboards, signs, etc. Camera-captured images typically have significant illumination problems and have huge file sizes; however, using the teachings presented herein ameliorates or removes disturbing illuminated background problems and creates files with reduced file sizes. 
       FIG. 19  depicts an embodiment of a compression system. As noted previously, segmentation is an important factor that impacts the effectiveness of compression for scanned document, as well as pictures of documents taken by a camera.  FIG. 19  presents a flowchart of a hybrid compression scheme with document segmentation for compressing an input image  1905 . As depicted in  FIG. 19 , an input image  1905 , such as a whiteboard image, is separated into a foreground layer. In an embodiment, the pixels of the foreground layer may be color-enhanced to improve the appearance of the resulting composite image. The resulting composite image  1950 , which may be a PDF document, may be obtained from a masked, compressed foreground layer image. One skilled in the art shall recognize that system  900  may be used or may be readily adapted into system  1900  and that the functions of the components depicted in  FIG. 19  may be the same as or similar to the functions described with respect to system  900 . For purposes of illustration, additional or alternative embodiments with respect to certain components of system  1900  and their associated functions are provided below. 
     a) Pixel Classification and Labeling 
     In an embodiment, each pixel of the input image  1905  (or a portion thereof) may be labeled using the pixel classifier and labeler  1910 . The pixel classifier and labeler  1910  may comprise pixel classifier and labeler  910 , all of system  200 , or a portion of system  200 , such as image labeler  220 . In an embodiment, the pixel classifier and labeler  1910  may be that portion of system  200  that outputs enhanced image  255 ,  256  or refined image  254 . This labeling may be referred to as X-labeling because, in embodiments, each pixel is classified with one label from a possible plurality of labels, such as for example, an edge label, a background label, a halftone label, or an other label. It shall be noted the number, type, and classification of labels may vary. 
     Because a whiteboard will rarely, if ever, appear as white in the captured image  1905  due to such factors as environmental illumination, true background pixels (i.e. white area on the whiteboard) may be identified by performing histogram-like statistics to identify dominant background color. In embodiments, background label refiner  1920  may identify true background pixels using or adapting one or more of the methods disclosed with respect to background estimator and label refiner  230  in system  200  or background label refiner and paper color estimator  920  in system  900 . Pixels initially labeled as background pixels but that do not correspond to the true background color may be relabeled, such as relabeled as halftone pixels. 
     b) Color Transform 
     Because the captured image is illuminated, in an embodiment, pixel-based color transform  1915  may transform image color pixel-by-pixel by matching a given color table  1970 . Similar to what was described with respect to system  200  or system  900 , a color table  1970  may be designed for matching original document colors when printing or displaying a scanned image, and it may also be used to generate vivid colors for captured images. 
     c) Mask Generator 
     In embodiments, mask generator  1922  may perform the same or similar functions as background mask and edge mask generator  925  of system  900 . In an embodiment, based on the labels, mask generator  1922  may generate a background mask and an edge mask. The background mask and edge mask may be utilized in subsequent processing. In an embodiment, edge mask may be dilated. 
     d) Foreground Layer Generation 
     The foreground layer of a captured image typically comprises text, drawings, or both, which may be in different colors. In an embodiment, to extract the text and drawings, pixels labeled as edges are preserved. These pixels, which may be referred to as non-background region pixels (or foreground pixels), may be marked as a bi-level mask image (foreground mask). In an embodiment, pixels lying on solid color regions between edge pixels may also be selected as foreground pixels. Solid region extractor  1925  may be used to identify text and other solid regions in the input image  1905  using one or more methods as described with respect to text-in-background extractor  930  and/or solid region extractor  940  in system  900 . Foreground layer generator  1930  may generate a foreground image layer by starting with a blank image and then including non-background/foreground pixels. 
     In embodiments, system  1900  may include foreground layer color enhancer  1935 . In an embodiment, the colors of foreground pixels may be locally smoothed and enhanced to improve readability. In an embodiment, smoothing and enhancement may be performed in like manner as described previously with respect to embodiments of system  900 . 
     In an embodiment, foreground layer color enhancer  1935  may dilate the foreground area so that colors may be recovered from its downsampled image. Colors may then be interpolated in the remaining blank areas for the foreground image layer. By performing color interpolation, the foreground image has gradual color changes (small color gradients) and may be downsampled and highly compressed without sacrificing much image quality. One skilled in the art shall recognize that the functions of foreground layer color enhancer  1935  may be performed or may be part of the foreground layer generator  1930 . 
     e) Foreground Layer Compression and Composite Generation 
     In an embodiment, foreground layer compressor  1940  may downsample the foreground image to improve compression. In one embodiment, the foreground image layer may be downsampled by a factor of 3. In an embodiment, the downsampled foreground image may be compressed using a lossy compression algorithm, such as JPEG compression with quality set at 30. 
     In an embodiment, mask-based composite generator  1945  may embed this compressed foreground image in a container, such as a PDF container, with a foreground mask, which may be TIFF-encoded. In an embodiment, the foreground mask may be compressed using a lossless compression algorithm. It should be noted that the background layer is not required because the composite container may assume a background color, such as by way of example and not limitation, white, black, or another color or colors. By not including a background image layer, the file size is even further reduced. Even though the JPEG quality may be set very low, the viewing experience of the masked foreground image may be superior to the original camera-captured image. It shall be noted that various compression algorithms, encoding formats, quality levels, and final composite types may be employed. 
     3. Alternative Embodiments of Document Compression 
     In the previous embodiments for image compression, systems and methods for converting images into high-quality high-compression image in composite format were built upon the earlier presented embodiments of background and foreground image generation for document compression. In alternative embodiments presented below, additional systems and methods may be used to extract contents on images, including simple techniques in background color estimate and edge detection. Such methods may be performed independent of other labeling methodologies previously depicted herein and are also computationally efficient. 
       FIG. 20  depicts an embodiment of a system  2000  that may be used to convert an image, such as a whiteboard image, into a high-quality high-compression composite document. Differences between this system and previous embodiments include the procedures that estimate background and edge pixels in input images. Like the previously presented systems, after labeling background and edge pixels, the input image may be separated into a foreground layer. In an embodiment, the content on the foreground layer may be represented as a masked JPEG-compressed image and may be stored in a PDF container. 
     a) Background Pixel and Color Estimate 
     System  2000  may be designed with knowledge or an estimate of the background color. For example, a whiteboard background area always shows dominant light color in the input images. Therefore, in an embodiment, background pixel and color estimator  2010  labels background pixels by identifying and thresholding the dominant light color in the input image  2005 . 
     In an embodiment, background pixel and color estimator  2010  may convert the red-green-blue (RGB) color value of each image pixel to an 8-bit grayscale intensity and build an intensity histogram for the input image  2005 . The intensity with the peak histogram value may be taken as the mean (μ) of the background intensity, and a pixel may be defined as a background pixel if its intensity is larger than μ−σ, where σ is a predefined threshold. In an embodiment, σ may function like or be related to the variance of background intensity. In an embodiment, the mean of the colors of background pixels may be set as the background color. 
     b) Edge Pixel Detection 
     One skilled in the art shall recognize that there are several ways to detect edges in images. In an embodiment, edge pixel detector  2015  may employ a Sobel edge detector, which may use two 3-by-3 convolution masks to detect both horizontal and vertical edges. The two 3-by-3 masks may be first convolved with the grayscale intensity image to compute the gradient values in both horizontal (G x ) and vertical (G y ) directions for each pixel. An approximate gradient magnitude may then be computed as |G|=|G x |+|G y | for each pixel. Pixels with a gradient magnitude larger than a predefined threshold may be classified as edge pixels. 
     c) Foreground Layer and Composite Generation 
     With the pixels labeled in the previous step, mask generator  2020  may generate a background mask and an edge mask. In an embodiment, edge mask may be dilated. Alternatively, mask generator  2020  may generate an edge mask. As noted previously, mask generator  2020  may function the same as or similar to background and mask generator  925  and/or mask generator  1922 . 
     In embodiment, solid region extractor  2025  may identify non-background pixel regions for the foreground image layer. In an embodiment, solid region extractor  2025  may find pixels lying on solid color regions between edge pixels. Note that, in an embodiment, background pixels may not be included in the process. In an alternative embodiment, solid region extractor  2025  may be used to identify non-background regions in the input image  2005  using one or more methods as described with respect to text-in-background extractor  930  and/or solid region extractor  940  in system  900 . 
     Foreground layer generator  2030  may use the locations of edge pixels and detected non-background region pixels, which may be referred to as foreground pixels, to mark the pixels as a bi-level mask image (foreground mask). In an embodiment, foreground layer color enhancer  2035  may locally smooth and enhance the colors of all foreground pixels to improve readability using any one or more of the methods previously described herein or otherwise known to those skilled in the art. Foreground layer compressor  2040  and mask-based composite generator  2045  may perform the remaining steps that convert the foreground pixels into a composite image  2050 , such as a PDF document, in the same or similar manners as described previously. As noted previously, the present invention is not limited to utilizing PDF composite images but that other composite generators or composite viewers may be employed. 
     For purpose of illustration,  FIG. 21  depicts an example of an input image  2100  taken of a whiteboard.  FIG. 22  depicts an example of a foreground image layer  2200  that may be obtained using the teachings of the present invention.  FIG. 23  depicts an example of a foreground mask  2300  that may be obtained using the teachings of the present invention. And,  FIG. 24  depicts an example of a composite image  2400  that may be obtained using the teachings of the present invention. It shall be noted that no background image layer is required for the composite, which helps reduce the file size, as the composite image may assume a background color, in this case, white. Note that in  FIG. 24 , with respect to the background color, issues related to environmental illumination have been eliminate or mitigated by displaying a white background  2405 . Also note that the text is presented with good readability, and the resultant composite image  2400  possesses a smaller image file size. 
     One skilled in the art shall recognize that the above-described embodiments may be used or adapted for use with images captured from easel-style papers, poster boards, or the like. One skilled in the art shall recognize that the above-described embodiments may be used or adapted for use with images captured from chalkboards, poster boards, or the like, where the background will have a dark color and foreground text will be lighter in color. 
     Aspects of the present invention may be implemented in any device or system capable of processing the image data, including without limitation, a general-purpose computer and a specific computer intended for graphics processing. The present invention may also be implemented into other devices and systems, including without limitation, a digital camera, a printer, a scanner, a printer, a facsimile machine, a multimedia device, and any other device that processes, captures, transmits, or stores an image. Furthermore, within any of the devices, aspects of the present invention may be implemented in a wide variety of ways including software, hardware, firmware, or combinations thereof. For example, the functions to practice various aspects of the present invention may be performed by components that are implemented in a wide variety of ways including discrete logic components, one or more application specific integrated circuits (ASICs), and/or program-controlled processors. It shall be noted that the manner in which these items are implemented is not critical to the present invention. 
     It shall be noted that embodiments of the present invention may further relate to computer products with a computer-readable medium that have computer code thereon for performing various computer-implemented operations. The media and computer code may be those specially designed and constructed for the purposes of the present invention, or they may be of the kind known or available to those having skill in the relevant arts. Examples of computer-readable media include, but are not limited to: magnetic media such as hard disks, floppy disks, and magnetic tape; optical media such as CD-ROMs and holographic devices; magneto-optical media; and hardware devices that are specially configured to store or to store and execute program code, such as application specific integrated circuits (ASICs), programmable logic devices (PLDs), flash memory devices, and ROM and RAM devices. Examples of computer code include machine code, such as produced by a compiler, and files containing higher level code that are executed by a computer using an interpreter. 
     While the invention is susceptible to various modifications and alternative forms, specific examples thereof have been shown in the drawings and are herein described in detail. It should be understood, however, that the inventions are not to be limited to the particular forms disclosed, but to the contrary, the inventions are to cover all modifications, equivalents, and alternatives falling within the scope of the appended claims.