Patent Publication Number: US-2016241884-A1

Title: Selective perceptual masking via scale separation in the spatial and temporal domains  for use in data compression with motion compensation

Description:
BACKGROUND OF THE INVENTION 
     Many significant and commercially important uses of modern computer technology relate to images and videos. These include image and video processing, image and video analysis and computer vision applications. In computer vision applications, such as, for example, object recognition and optical character recognition, it has been found that a separation of illumination and material aspects of an image can significantly improve the accuracy and speed of computer performance. Significant pioneer inventions related to the illumination and material aspects of an image are disclosed in U.S. Pat. No. 7,873,219 to Richard Mark Friedhoff, entitled Differentiation Of Illumination And Reflection Boundaries and U.S. Pat. No. 7,672,530 to Richard Mark Friedhoff et al., entitled Method And System For Identifying Illumination Flux In An Image (hereinafter the Friedhoff patents). 
     SUMMARY OF THE INVENTION 
     In an exemplary embodiment of the present invention, an automated, computerized method for processing a video is provided. The method includes providing a video file depicting a video, in a computer memory; providing a video file depicting a video, in a computer memory; scale separating the video file by applying an edge preserving blurring filter to generate a detail scale separated video and a level scale separated video corresponding to the video; temporally blurring the detail scale separated video and spatially blurring the level scale separated video; combining the filtered detailed scale separated video and the filtered level scale separated video to provide an output video; and outputting the output video for use in a data compression operation. An additional exemplary embodiment of the present invention provides a novel pyramid-based motion compensation technique to improve overall performance of the method according to the present invention. 
     In accordance with yet further embodiments of the present invention, computer systems are provided, which include one or more computers configured (e.g., programmed) to perform the methods described above. In accordance with other embodiments of the present invention, non-transitory computer readable media are provided which have stored thereon computer executable process steps operable to control a computer(s) to implement the embodiments described above. The present invention contemplates a computer readable media as any product that embodies information usable in a computer to execute the methods of the present invention, including instructions implemented as a hardware circuit, for example, as in an integrated circuit chip. The automated, computerized methods can be performed by a digital computer, analog computer, optical sensor, state machine, sequencer, integrated chip or any device or apparatus that can be designed or programmed to carry out the steps of the methods of the present invention. 
    
    
     
       BRIEF DESCRIPTION OF THE DRAWINGS 
         FIG. 1  is a block diagram of a computer system arranged and configured to perform operations related to videos. 
         FIG. 2  shows an n×m pixel array image file for a frame of a video stored in the computer system of  FIG. 1 . 
         FIG. 3 a    is a flow chart for identifying Type C token regions in the image file of  FIG. 2 , according to a feature of the present invention. 
         FIG. 3 b    is an original image used as an example in the identification of Type C tokens. 
         FIG. 3 c    shows Type C token regions in the image of  FIG. 3   b.    
         FIG. 3 d    shows Type B tokens, generated from the Type C tokens of  FIG. 3 c   , according to a feature of the present invention. 
         FIG. 4  is a flow chart for a routine to test Type C tokens identified by the routine of the flow chart of  FIG. 3 a   , according to a feature of the present invention. 
         FIG. 5  is a graphic representation of a log color space chromaticity plane according to a feature of the present invention. 
         FIG. 6  is a flow chart for determining a list of colors depicted in an input image. 
         FIG. 7  is a flow chart for determining an orientation for a log chromaticity space, according to a feature of the present invention. 
         FIG. 8  is a flow chart for determining log chromaticity coordinates for the colors of an input image, as determined through execution of the routine of  FIG. 6 , according to a feature of the present invention. 
         FIG. 9  is a flow chart for augmenting the log chromaticity coordinates, as determined through execution of the routine of  FIG. 8 , according to a feature of the present invention. 
         FIG. 10  is a flow chart for clustering the log chromaticity coordinates, according to a feature of the present invention. 
         FIG. 11  is a flow chart for assigning the log chromaticity coordinates to clusters determined through execution of the routine of  FIG. 10 , according to a feature of the present invention. 
         FIG. 12  is a flow chart for detecting regions of uniform reflectance based on the log chromaticity clustering according to a feature of the present invention. 
         FIG. 13  is a representation of an [A] [x]=[b] matrix relationship used to identify and separate illumination and material aspects of an image, according to a same-material constraint, for generation of intrinsic images. 
         FIG. 14  illustrates intrinsic images including an illumination image and a material image corresponding to the original image of  FIG. 3   b.    
         FIG. 15  shows a flow chart of a linear video stored in a video file being compressed in accordance with a conventional video compression method. 
         FIG. 16  shows a flow chart for processing a linear video, according to an embodiment of the present invention. 
         FIG. 17  shows an example of spatially subsampling an illumination video by spatially reducing each of the illumination video frames. 
         FIG. 18  shows an example of temporally subsampling a material video by reducing the number of material video frames. 
         FIG. 19  is a flow chart for decompressing and recombining the compressed recombined filtered intrinsic video stored or transmitted in  FIG. 18 , according to an embodiment of the present invention. 
         FIG. 20  shows a flow chart for processing a linear video, according to another embodiment of the present invention. 
         FIG. 21  is a flow chart for decompressing and recombining the compressed filtered illumination video and the compressed filtered material video from  FIG. 20 , according to an embodiment of the present invention. 
         FIG. 22  shows a flow chart for processing a linear video, according to another embodiment of the present invention. 
         FIG. 23  is a flow chart for decompressing and recombining the compressed filtered illumination video and the compressed filtered material video described with respect to  FIG. 22 , according to an embodiment of the present invention. 
         FIG. 24  shows a flow chart for processing a linear video, according to another embodiment of the present invention. 
         FIG. 25  is a flow chart for decompressing the compressed recombined filtered intrinsic video from  FIG. 24 , according to an embodiment of the present invention. 
         FIGS. 26 to 29  shows flow charts for scale separating and processing videos, according to different embodiments of the present invention. 
         FIG. 30  shows a flow chart for processing a gamma corrected video, according to another embodiment of the present invention. 
         FIG. 31  shows a graphic representation of an image pyramid for the exemplary image of  FIG. 3   b.    
         FIG. 32  is a flow chart for a block motion estimation used in a pyramid motion estimation implemented in connection with the image pyramid of  FIG. 31 , according to a feature of the present invention. 
         FIG. 33  is a flow chart for a pyramid block motion estimation according to a feature of the present invention. 
         FIGS. 34 a - e    show diagrams of an exemplary pyramid search center determined by execution of the routine of  FIG. 33 . 
         FIG. 35  is a flow chart for an expanded pyramid block motion estimation according to a feature of the present invention. 
         FIGS. 36 a - c    show diagrams of an exemplary pyramid search center determined by execution of the routine of  FIG. 35 . 
     
    
    
     DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS 
     Referring now to the drawings, and initially to  FIG. 1 , there is shown a block diagram of a computer system  10  arranged and configured to perform operations related to videos. A CPU  12  is coupled to a device such as, for example, a digital camera  14  via, for example, a USB port. The digital camera can comprise a video digital camera. The digital camera  14  operates to download videos stored locally on the camera  14 , to the CPU  12 . The CPU  12  stores the downloaded videos in a memory  16  as video files  18 . The video files  18  can be accessed by the CPU  12  for display on a monitor  20 . The memory  16  can comprise any temporary or permanent data storage device. 
     Moreover, the computer system  10  includes an object database  24  storing information on various objects that can appear in the video files  18  stored in the memory  16 . The information includes information on the material make-up and material reflectance colors for each object stored in the database  24 . The object database is coupled to the CPU  12 , as shown in  FIG. 1 . The CPU  12  is also coupled to the Internet  26 , for access to websites  28 . The websites  28  include websites that contain information relevant to objects that can appear in the video files  18 , such as, for example, the material make-up and material reflectance colors for the objects, and provide another source for an object database. The websites  28  also include websites that are arranged to receive video file  18 , transmitted over the Internet  26 , from the CPU  12 . 
     Alternatively, the CPU  12  can be implemented as a microprocessor embedded in a device such as, for example, the digital camera  14  or a robot. The CPU  12  can also be equipped with a real time operating system for real time operations related to videos, in connection with, for example, a robotic operation or an interactive operation with a user. 
     As shown in  FIG. 2 , each video file  18  comprises a plurality of successive images, called frames, each comprising an n×m pixel array. Each pixel, p, is a picture element corresponding to a discrete portion of the overall image. All of the pixels together define each frame represented by the video file  18 . Each pixel comprises a digital value corresponding to a set of color bands, for example, red, green and blue color components (RGB) of the picture element. The present invention is applicable to any multi-band image, where each band corresponds to a piece of the electro-magnetic spectrum. The pixel array includes n rows of m columns each, starting with the pixel p (1,1) and ending with the pixel p(n, m). When displaying a video, the CPU  12  retrieves the corresponding video file  18  from the memory  16 , and operates the monitor  20  as a function of the digital values of the pixels in the frames of the video file  18 , as is generally known. 
     In an image operation, the CPU  12  operates to analyze the RGB values of the pixels of images of stored video file  18  to achieve various objectives, such as, for example, to identify regions of an image that correspond to a single material depicted in a scene recorded in the video file  18 . A fundamental observation underlying a basic discovery of the present invention, is that an image comprises two components, material and illumination. All changes in an image are caused by one or the other of these components. A method for detecting of one of these components, for example, material, provides a mechanism for distinguishing material or object geometry, such as object edges, from illumination and shadow boundaries. 
     Such a mechanism enables techniques that can be used to generate intrinsic images. Each of the intrinsic images corresponds to an original image, i.e., video frame, for example, an image depicted in an input video file  18 . The intrinsic images include, for example, an illumination image, to capture the intensity and color of light incident upon each point on the surfaces depicted in the image, and a material reflectance image, to capture reflectance properties of surfaces depicted in the image (the percentage of each wavelength of light a surface reflects). The separation of illumination from material in the intrinsic images provides the CPU  12  with images optimized for more effective and accurate and efficient further processing. 
     For example, according to a feature of the present invention, the intrinsic images are applied in a digital image signal compression algorithm, for improved results in data transmission and/or storage. Computer files that depict an image, particularly a color image, require a significant amount of information arranged as, for example, pixels represented by bytes. Thus, each video file requires a significant amount of storage space in a memory, and can consume a large amount of time in a data transmission of the image to a remote site or device. The amount of time that can be required to transmit a sequence of images, for example, as in a video stream, can render an operation, such as a streaming operation for realtime display of a video on a smartphone, Internet website or tablet, unfeasible. 
     Accordingly, mathematical techniques have been developed to compress the number of bytes representing the pixels of an image to a significantly smaller number of bytes. For example, standards for lossy video compression developed by organizations such as ISO MPEG, the Moving Picture Experts Group, enable compression of digital video files. A compressed video can be stored in a manner that requires much less storage capacity than the original video file, and transmitted to a remote site or device in a far more efficient and speedy transmission operation. The compressed video file is decompressed for further use, such as, for example, display on a screen. However, due to the rapidly increasing number of users of devices for reception and realtime display of digital videos, known compression techniques are being pressed to the limits of effective functionality. 
     According to a feature of the present invention, digital signal compression and decompression processing is improved by performing the compression and decompression processes on intrinsic images. 
     Pursuant to a feature of the present invention, processing is performed at a token level. A token is a connected region of an image wherein the pixels of the region are related to one another in a manner relevant to identification of image features and characteristics such as an identification of materials and illumination. The pixels of a token can be related in terms of either homogeneous factors, such as, for example, close correlation of color among the pixels, or inhomogeneous factors, such as, for example, differing color values related geometrically in a color space such as RGB space, commonly referred to as a texture. The present invention utilizes spatio-spectral information relevant to contiguous pixels of images depicted in a video file  18  to identify token regions. The spatio-spectral information includes spectral relationships among contiguous pixels, in terms of color bands, for example the RGB values of the pixels, and the spatial extent of the pixel spectral characteristics relevant to a single material. 
     According to one exemplary embodiment of the present invention, tokens are each classified as either a Type A token, a Type B token or a Type C token. A Type A token is a connected image region comprising contiguous pixels that represent the largest possible region of the image encompassing a single material in the scene (uniform reflectance). A Type B token is a connected image region comprising contiguous pixels that represent a region of the image encompassing a single material in the scene, though not necessarily the maximal region of uniform reflectance corresponding to that material. A Type B token can also be defined as a collection of one or more image regions or pixels, all of which have the same reflectance (material color) though not necessarily all pixels which correspond to that material color. A Type C token comprises a connected image region of similar image properties among the contiguous pixels of the token, where similarity is defined with respect to a noise model for the imaging system used to record the image. 
     Referring now to  FIG. 3 a   , there is shown a flow chart for identifying Type C token regions in the scene depicted in an image of video file  18  of  FIG. 2 , according to a feature of the present invention. Type C tokens can be readily identified in an image, utilizing the steps of  FIG. 3 a   , and then analyzed and processed to construct Type B tokens, according to a feature of the present invention. 
     A 1 st  order uniform, homogeneous Type C token comprises a single robust color measurement among contiguous pixels of the image. At the start of the identification routine, the CPU  12  sets up a region map in memory. In step  100 , the CPU  12  clears the region map and assigns a region ID, which is initially set at 1. An iteration for the routine, corresponding to a pixel number, is set at i=0, and a number for an N×N pixel array, for use as a seed to determine the token, is set an initial value, N=N start . N start  can be any integer&gt;0, for example it can be set at set at 11 or 15 pixels. 
     At step  102 , a seed test is begun. The CPU  12  selects a first pixel, i=1, pixel (1, 1) for example (see  FIG. 2 ), the pixel at the upper left corner of a first N×N sample of an image of video file  18 . The pixel is then tested in decision block  104  to determine if the selected pixel is part of a good seed. The test can comprise a comparison of the color value of the selected pixel to the color values of a preselected number of its neighboring pixels as the seed, for example, the N×N array. The color values comparison can be with respect to multiple color band values (RGB in our example) of the pixel. If the comparison does not result in approximately equal values (within the noise levels of the recording device) for the pixels in the seed, the CPU  12  increments the value of i (step  106 ), for example, i=2, pixel (1, 2), for a next N×N seed sample, and then tests to determine if i=i max  (decision block  108 ). 
     If the pixel value is at i max , a value selected as a threshold for deciding to reduce the seed size for improved results, the seed size, N, is reduced (step  110 ), for example, from N=15 to N=12. In an exemplary embodiment of the present invention, i max  can be set at a number of pixels in an image ending at pixel (n, m), as shown in  FIG. 2 . In this manner, the routine of  FIG. 3 a    parses the entire image at a first value of N before repeating the routine for a reduced value of N. 
     After reduction of the seed size, the routine returns to step  102 , and continues to test for token seeds. An N stop  value (for example, N=2) is also checked in step  110  to determine if the analysis is complete. If the value of N is at N stop , the CPU  12  has completed a survey of the image pixel arrays and exits the routine. 
     If the value of i is less than i max , and N is greater than N stop , the routine returns to step  102 , and continues to test for token seeds. 
     When a good seed (an N×N array with approximately equal pixel values) is found (block  104 ), the token is grown from the seed. In step  112 , the CPU  12  pushes the pixels from the seed onto a queue. All of the pixels in the queue are marked with the current region ID in the region map. The CPU  12  then inquires as to whether the queue is empty (decision block  114 ). If the queue is not empty, the routine proceeds to step  116 . 
     In step  116 , the CPU  12  pops the front pixel off the queue and proceeds to step  118 . In step  118 , the CPU  12  marks “good’ neighbors around the subject pixel, that is neighbors approximately equal in color value to the subject pixel, with the current region ID. All of the marked good neighbors are placed in the region map and also pushed onto the queue. The CPU  12  then returns to the decision block  114 . The routine of steps  114 ,  116 ,  118  is repeated until the queue is empty. At that time, all of the pixels forming a token in the current region will have been identified and marked in the region map as a Type C token. 
     When the queue is empty, the CPU  12  proceeds to step  120 . At step  120 , the CPU  12  increments the region ID for use with identification of a next token. The CPU  12  then returns to step  106  to repeat the routine in respect of the new current token region. 
     Upon arrival at N=N stop , step  110  of the flow chart of  FIG. 3 a   , or completion of a region map that coincides with the image, the routine will have completed the token building task.  FIG. 3 b    is an original image used as an example in the identification of tokens. The image shows areas of the color blue and the blue in shadow, and of the color teal and the teal in shadow.  FIG. 3 c    shows token regions corresponding to the region map, for example, as identified through execution of the routine of  FIG. 3 a    (Type C tokens), in respect to the image of  FIG. 3 b   . The token regions are color coded to illustrate the token makeup of the image of  FIG. 3 b   , including penumbra regions between the full color blue and teal areas of the image and the shadow of the colored areas. 
     While each Type C token comprises a region of the image having a single robust color measurement among contiguous pixels of the image, the token may grow across material boundaries. Typically, different materials connect together in one Type C token via a neck region often located on shadow boundaries or in areas with varying illumination crossing different materials with similar hue but different intensities. A neck pixel can be identified by examining characteristics of adjacent pixels. When a pixel has two contiguous pixels on opposite sides that are not within the corresponding token, and two contiguous pixels on opposite sides that are within the corresponding token, the pixel is defined as a neck pixel. 
       FIG. 4  shows a flow chart for a neck test for Type C tokens. In step  122 , the CPU  12  examines each pixel of an identified token to determine whether any of the pixels under examination forms a neck. The routine of  FIG. 4  can be executed as a subroutine directly after a particular token is identified during execution of the routine of  FIG. 3 a   . All pixels identified as a neck are marked as “ungrowable.” In decision block  124 , the CPU  12  determines if any of the pixels were marked. 
     If no, the CPU  12  exits the routine of  FIG. 4  and returns to the routine of  FIG. 3 a    (step  126 ). 
     If yes, the CPU  12  proceeds to step  128  and operates to regrow the token from a seed location selected from among the unmarked pixels of the current token, as per the routine of  FIG. 3 a   , without changing the counts for seed size and region ID. During the regrowth process, the CPU  12  does not include any pixel previously marked as ungrowable. After the token is regrown, the previously marked pixels are unmarked so that other tokens may grow into them. 
     Subsequent to the regrowth of the token without the previously marked pixels, the CPU  12  returns to step  122  to test the newly regrown token. Neck testing identifies Type C tokens that cross material boundaries, and regrows the identified tokens to provide single material Type C tokens suitable for use in creating Type B tokens. 
       FIG. 3 d    shows Type B tokens generated from the Type C tokens of  FIG. 3 c   , according to a feature of the present invention. The present invention provides a novel exemplary technique using log chromaticity clustering, for constructing Type B tokens for images of video file  18 . Log chromaticity is a technique for developing an illumination invariant chromaticity space. 
     A method and system for separating illumination and reflectance using a log chromaticity representation is disclosed in U.S. Pat. No. 7,596,266, which is hereby expressly incorporated by reference. The techniques taught in U.S. Pat. No. 7,596,266 can be used to provide illumination invariant log chromaticity representation values for each color of an image, for example, as represented by Type C tokens. Logarithmic values of the color band values of the image pixels are plotted on a log-color space graph. The logarithmic values are then projected to a log-chromaticity projection plane oriented as a function of a bi-illuminant dichromatic reflection model (BIDR model), to provide a log chromaticity value for each pixel, as taught in U.S. Pat. No. 7,596,266. The BIDR Model predicts that differing color measurement values fall within a cylinder in RGB space, from a dark end (in shadow) to a bright end (lit end), along a positive slope, when the color change is due to an illumination change forming a shadow over a single material of a scene depicted in the image. 
       FIG. 5  is a graphic representation of a log color space, bi-illuminant chromaticity plane according to a feature of the invention disclosed in U.S. Pat. No. 7,596,266. The alignment of the chromaticity plane is determined by a vector N, normal to the chromaticity plane, and defined as N=log(Bright vector )−log(Dark vector )=log(1+1/S vector ). The co-ordinates of the plane, u, v can be defined by a projection of the green axis onto the chromaticity plane as the u axis, and the cross product of u and N being defined as the v axis. In our example, each log value for the materials A, B, C is projected onto the chromaticity plane, and will therefore have a corresponding u, v co-ordinate value in the plane that is a chromaticity value, as shown in  FIG. 5 . 
     Thus, according to the technique disclosed in U.S. Pat. No. 7,596,266, the RGB values of each pixel in each image of video file  18  can be mapped by the CPU  12  from the image file value p(n, m, R, G, B) to a log value, then, through a projection to the chromaticity plane, to the corresponding u, v value, as shown in  FIG. 5 . Each pixel p(n, m, R, G, B) in the corresponding image of video file  18  is then replaced by the CPU  12  by a two dimensional chromaticity value: p(n, m, u, v), to provide a chromaticity representation of the original RGB image. In general, for an N band image, the N color values are replaced by N−1 chromaticity values. The chromaticity representation is a truly accurate illumination invariant representation because the BIDR model upon which the representation is based, accurately and correctly represents the illumination flux that caused the original image. 
     According to a feature of the present invention, log chromaticity values are calculated for each color depicted in an image of video file  18  input to the CPU  12  for identification of regions of the uniform reflectance (Type B tokens). For example, each pixel of a Type C token will be of approximately the same color value, for example, in terms of RGB values, as all the other constituent pixels of the same Type C token, within the noise level of the equipment used to record the image. Thus, an average of the color values for the constituent pixels of each particular Type C token can be used to represent the color value for the respective Type C token in the log chromaticity analysis. 
       FIG. 6  is a flow chart for determining a list of colors depicted in an input image, for example, an image of video file  18 . In step  200 , an input video file  18  is input to the CPU  12  for processing. In steps  202  and  204 , the CPU  12  determines the colors depicted in the input image of video file  18 . In step  202 , the CPU  12  calculates an average color for each Type C token determined by the CPU  12  through execution of the routine of  FIG. 3 a   , as described above, for a list of colors. The CPU  12  can be operated to optionally require a minimum token size, in terms of the number of constituent pixels of the token, or a minimum seed size (the N×N array) used to determine Type C tokens according to the routine of  FIG. 3 a   , for the analysis. The minimum size requirements are implemented to assure that color measurements in the list of colors for the image are an accurate depiction of color in a scene depicted in the input image, and not an artifact of blend pixels. 
     Blend pixels are pixels between two differently colored regions of an image. If the colors between the two regions are plotted in RGB space, there is a linear transition between the colors, with each blend pixel, moving from one region to the next, being a weighted average of the colors of the two regions. Thus, each blend pixel does not represent a true color of the image. If blend pixels are present, relatively small Type C tokens, consisting of blend pixels, can be identified for areas of an image between two differently colored regions. By requiring a size minimum, the CPU  12  can eliminate tokens consisting of blend pixel from the analysis. 
     In step  204 , the CPU  12  can alternatively collect colors at the pixel level, that is, the RGB values of the pixels of the input image of video file  18 , as shown in  FIG. 2 . The CPU  12  can be operated to optionally require each pixel of the image of video file  18  used in the analysis to have a minimum stability or local standard deviation via a filter output, for a more accurate list of colors. For example, second derivative energy can be used to indicate the stability of pixels of an image. 
     In this approach, the CPU  12  calculates a second derivative at each pixel, or a subset of pixels disbursed across the image to cover all illumination conditions of the image depicted in an input video file  18 , using a Difference of Gaussians, Laplacian of Gaussian, or similar filter. The second derivative energy for each pixel examined can then be calculated by the CPU  12  as the average of the absolute value of the second derivative in each color band (or the absolute value of the single value in a grayscale image), the sum of squares of the values of the second derivatives in each color band (or the square of the single value in a grayscale image), the maximum squared second derivative value across the color bands (or the square of the single value in a grayscale image), or any similar method. Upon the calculation of the second derivative energy for each of the pixels, the CPU  12  analyzes the energy values of the pixels. There is an inverse relationship between second derivative energy and pixel stability, the higher the energy, the less stable the corresponding pixel. 
     In step  206 , the CPU  12  outputs a list or lists of color (after executing one or both of steps  202  and/or  204 ). According to a feature of the present invention, all of the further processing can be executed using the list from either step  202  or  204 , or vary the list used (one or the other of the lists from steps  202  or  204 ) at each subsequent step. 
       FIG. 7  is a flow chart for determining an orientation for a log chromaticity representation, according to a feature of the present invention. For example, the CPU  12  determines an orientation for the normal N, for a log chromaticity plane, as shown in  FIG. 5 . In step  210 , the CPU  12  receives a list of colors for an input file  18 , such as a list output in step  206  of the routine of  FIG. 6 . In step  212 , the CPU  12  determines an orientation for a log chromaticity space. 
     As taught in U.S. Pat. No. 7,596,266, and as noted above, alignment of the chromaticity plane is represented by N, N being a vector normal to the chromaticity representation, for example, the chromaticity plane of  FIG. 5 . The orientation is estimated by the CPU  12  thorough execution of any one of several techniques. For example, the CPU  12  can determine estimates based upon entropy minimization, manual selection by a user or the use of a characteristic spectral ratio for an image of an input video file  18 , as fully disclosed in U.S. Pat. No. 7,596,266. 
     For a higher dimensional set of colors, for example, an RYGB space (red, yellow, green, blue), the log chromaticity normal, N, defines a sub-space with one less dimension than the input space. Thus, in the four dimensional RYGB space, the normal N defines a three dimensional log chromaticity space. When the four dimensional RYGB values are projected into the three dimensional log chromaticity space, the projected values within the log chromaticity space are unaffected by illumination variation. 
     In step  214 , the CPU  12  outputs an orientation for the normal N. As illustrated in the example of  FIG. 5 , the normal N defines an orientation for a u, v plane in a three dimensional RGB space. 
       FIG. 8  is a flow chart for determining log chromaticity coordinates for the colors of an input image, as identified in steps  202  or  204  of the routine of  FIG. 6 , according to a feature of the present invention. In step  220 , a list of colors is input to the CPU  12 . The list of colors can comprise either the list generated through execution of step  202  of the routine of  FIG. 6 , or the list generated through execution of step  204 . In step  222 , the log chromaticity orientation for the normal, N, determined through execution of the routine of  FIG. 7 , is also input to the CPU  12 . 
     In step  224 , the CPU  12  operates to calculate a log value for each color in the list of colors and plots the log values in a three dimensional log space at respective (log R, log G, log B) coordinates, as illustrated in  FIG. 5 . Materials A, B and C denote log values for specific colors from the list of colors input to the CPU  12  in step  220 . A log chromaticity plane is also calculated by the CPU  12 , in the three dimensional log space, with u, v coordinates and an orientation set by N, input to the CPU  12  in step  222 . Each u, v coordinate in the log chromaticity plane can also be designated by a corresponding (log R, log G, log B) coordinate in the three dimensional log space. 
     According to a feature of the present invention, the CPU  12  then projects the log values for the colors A, B and C onto the log chromaticity plane to determine a u, v log chromaticity coordinate for each color. Each u, v log chromaticity coordinate can be expressed by the corresponding (log R, log G, log B) coordinate in the three dimensional log space. The CPU  12  outputs a list of the log chromaticity coordinates in step  226 . The list cross-references each color to a u, v log chromaticity coordinate and to the pixels (or a Type C tokens) having the respective color (depending upon the list of colors used in the analysis (either step  202  (tokens) or  204  (pixels))). 
       FIG. 9  is a flow chart for optionally augmenting the log chromaticity coordinates for pixels or Type C tokens with extra dimensions, according to a feature of the present invention. In step  230 , the list of log chromaticity coordinates, determined for the colors of the input image through execution of the routine of  FIG. 8 , is input to the CPU  12 . In step  232 , the CPU  12  accesses the input video file  18 , for use in the augmentation. 
     In step  234 , the CPU  12  optionally operates to augment each log chromaticity coordinate with a tone mapping intensity for each corresponding pixel (or Type C token). The tone mapping intensity is determined using any known tone mapping technique. An augmentation with tone mapping intensity information provides a basis for clustering pixels or tokens that are grouped according to both similar log chromaticity coordinates and similar tone mapping intensities. This improves the accuracy of a clustering step. 
     In step  236 , the CPU  12  optionally operates to augment each log chromaticity coordinate with x, y coordinates for the corresponding pixel (or an average of the x, y coordinates for the constituent pixels of a Type C token) (see  FIG. 2  showing a P (1,1) to P (N, M) pixel arrangement). Thus, a clustering step with x, y coordinate information will provide groups in a spatially limited arrangement, when that characteristic is desired. 
     In each of steps  234  and  236 , the augmented information can, in each case, be weighted by a factor w 1  and w 2 , w 3  respectively, to specify the relative importance and scale of the different dimensions in the augmented coordinates. The weight factors w 1  and w 2 , w 3  are user-specified. Accordingly, the (log R, log G, log B) coordinates for a pixel or Type C token is augmented to (log R, log G, log B, T*w 1 , x*w 2 , y*w 3 ) where T, x and y are the tone mapped intensity, the x coordinate and the y coordinate, respectively. 
     In step  238 , the CPU  12  outputs a list of the augmented coordinates. The augmented log chromaticity coordinates provide accurate illumination invariant representations of the pixels, or for a specified regional arrangement of an input image, such as, for example, Type C tokens. According to a feature of the present invention, the illumination invariant characteristic of the log chromaticity coordinates is relied upon as a basis to identify regions of an image of a single material or reflectance, such as, for example, Type B tokens. 
       FIG. 10  is a flow chart for clustering the log chromaticity coordinates, according to a feature of the present invention. In step  240 , the list of augmented log chromaticity coordinates is input the CPU  12 . In step  242 , the CPU  12  operates to cluster the log chromaticity coordinates. The clustering step can be implemented via, for example, a known k-means clustering. Any known clustering technique can be used to cluster the log chromaticity coordinates to determine groups of similar log chromaticity coordinate values. The CPU  12  correlates each log chromaticity coordinate to the group to which the respective coordinate belongs. The CPU  12  also operates to calculate a center for each group identified in the clustering step. For example, the CPU  12  can determine a center for each group relative to a (log R, log G, log B, log T) space. 
     In step  244 , the CPU  12  outputs a list of the cluster group memberships for the log chromaticity coordinates (cross referenced to either the corresponding pixels or Type C tokens) and/or a list of cluster group centers. 
     As noted above, in the execution of the clustering method, the CPU  12  can use the list of colors from either the list generated through execution of step  202  of the routine of  FIG. 6 , or the list generated through execution of step  204 . In applying the identified cluster groups to an input image, the CPU  12  can be operated to use the same set of colors as used in the clustering method (one of the list of colors corresponding to step  202  or to the list of colors corresponding to step  204 ), or apply a different set of colors (the other of the list of colors corresponding to step  202  or the list of colors corresponding to step  204 ). If a different set of colors is used, the CPU  12  proceeds to execute the routine of  FIG. 11 . 
       FIG. 11  is a flow chart for assigning the log chromaticity coordinates to clusters determined through execution of the routine of  FIG. 10 , when a different list of colors is used after the identification of the cluster groups, according to a feature of the present invention. In step  250 , the CPU  12  once again executes the routine of  FIG. 8 , this time in respect to the new list of colors. For example, if the list of colors generated in step  202  (colors based upon Type C tokens) was used to identify the cluster groups, and the CPU  12  then operates to classify log chromaticity coordinates relative to cluster groups based upon the list of colors generated in step  204  (colors based upon pixels), step  250  of the routine of  FIG. 11  is executed to determine the log chromaticity coordinates for the colors of the pixels in the corresponding image of the input video file  18 . 
     In step  252 , the list of cluster centers is input to the CPU  12 . In step  254 , the CPU  12  operates to classify each of the log chromaticity coordinates identified in step  250 , according to the nearest cluster group center. In step  256 , the CPU  12  outputs a list of the cluster group memberships for the log chromaticity coordinates based upon the new list of colors, with a cross reference to either corresponding pixels or Type C tokens, depending upon the list of colors used in step  250  (the list of colors generated in step  202  or the list of colors generated in step  204 ). 
       FIG. 12  is a flow chart for detecting regions of uniform reflectance based on the log chromaticity clustering according to a feature of the present invention. In step  260 , the corresponding image of input video file  18  is once again provided to the CPU  12 . In step  262 , one of the pixels or Type C tokens, depending upon the list of colors used in step  250 , is input to the CPU  12 . In step  264 , the cluster membership information, form either steps  244  or  256 , is input to the CPU  12 . 
     In step  266 , the CPU  12  operates to merge each of the pixels, or specified regions of an input image, such as, for example, Type C tokens, having a same cluster group membership into a single region of the image to represent a region of uniform reflectance (Type B token). The CPU  12  performs such a merge operation for all of the pixels or tokens, as the case may be, for the corresponding image of input video file  18 . In step  268 , the CPU  12  outputs a list of all regions of uniform reflectance (and also of similar tone mapping intensities and x, y coordinates, if the log chromaticity coordinates were augmented in steps  234  and/or  236 ). It should be noted that each region of uniform reflectance (Type B token) determined according to the features of the present invention, potentially has significant illumination variation across the region. 
     U. S. Patent Publication No. US 2010/0142825 teaches a constraint/solver model for segregating illumination and material in an image, including an optimized solution based upon a same material constraint. A same material constraint, as taught in U. S. Patent Publication No. US 2010/0142825, utilizes Type C tokens and Type B tokens, as can be determined according to the teachings of the present invention. The constraining relationship is that all Type C tokens that are part of the same Type B token are constrained to be of the same material. This constraint enforces the definition of a Type B token, that is, a connected image region comprising contiguous pixels that represent a region of the image encompassing a single material (same reflectance) in the scene, though not necessarily the maximal region corresponding to that material. Thus, all Type C tokens that lie within the same Type B token are by the definition imposed upon Type B tokens, of the same material, though not necessarily of the same illumination. The Type C tokens are therefore constrained to correspond to observed differences in appearance that are caused by varying illumination. 
       FIG. 13  is a representation of an [A] [x]=[b] matrix relationship used to identify and separate illumination and material aspects of an image, according to a same-material constraint, as taught in U. S. Patent Publication No. US 2010/0142825. Based upon the basic equation I=ML (I=the recorded image value, as stored in a video file  18 , M=material reflectance, and L=illumination), log(I)=log(ML)=log(M)+log(L). This can be restated as i=m+1, wherein i represents log(I), m represents log(M) and l represents log(L). In the constraining relationship of a same material, in an example where three Type C tokens, a, b and c, (as shown in  FIG. 13 ) are within a region of single reflectance, as defined by a corresponding Type B token defined by a, b and c, then m a =m b =m c . For the purpose of this example, the I value for each Type C token is the average color value for the recorded color values of the constituent pixels of the token. The a, b and c, Type C tokens of the example can correspond to the blue Type B token illustrated in  FIG. 3   d.    
     Since: m a =i a −l a , m b =i b −l b , and m c =i c −l c , these mathematical relationships can be expressed, in a same material constraint, as (1)l a +(−1)l b +(0)l c =(i a −i b ), (1)l a +(0)l b +(−1)l c =(i a −i c ) and (0)l a +(1)l b +(−1)l c =(i b −i c ). 
     Thus, in the matrix equation of  FIG. 13 , the various values for the log (I) (i a , i b , i c ), in the [b] matrix, are known from the average recorded pixel color values for the constituent pixels of the adjacent Type C tokens a, b and c. The [A] matrix of 0&#39;s, 1&#39;s and −1&#39;s, is defined by the set of equations expressing the same material constraint, as described above. The number of rows in the [A] matrix, from top to bottom, corresponds to the number of actual constraints imposed on the tokens, in this case three, the same material constraint between the three adjacent Type C tokens a, b and c. The number of columns in the [A] matrix, from left to right, corresponds to the number of unknowns to be solved for, again, in this case, the three illumination values for the three tokens. Therefore, the values for the illumination components of each Type C token a, b and c, in the [x] matrix, can be solved for in the matrix equation, by the CPU  12 . It should be noted that each value is either a vector of three values corresponding to the color bands (such as red, green, and blue) of our example or can be a single value, such as in a grayscale image. 
     Once the illumination values are known, the material color can be calculated by the CPU  12  using the I=ML equation. Intrinsic illumination and material images can be now be generated for the region defined by tokens a, b and c, by replacing each pixel in the original image by the calculated illumination values and material values, respectively. An example of an illumination image and material image, corresponding to the original image shown in  FIG. 3 b   , is illustrated in  FIG. 14 . 
     According to a feature of a further exemplary embodiment of the present invention, the CPU  12  is coupled to an object database  24 . As noted above, the object database  24  stores a list of objects that can appear in the video files  18 , and information on the material make-up and material reflectance colors for each object stored in the database  24 . In connection with the above-described techniques for segregating an image into corresponding material reflectance and illumination intrinsic images, the CPU  12  is operated to perform a known object recognition task, such as, for example, a SIFT technique, to identify objects in an image being processed. 
     Upon the identification of an object in a scene depicted in an image being processed, the CPU  12  accesses the object database  24  for the material reflectance color information relevant to the identified object. The CPU  12  is then operated to correlate, for example, any Type C tokens in the image being processed that constitute the identified object. The material reflectance color information for the identified object can then be used to specify, for example, a fixed material color anchor value added to the matrix equation shown in  FIG. 13 , to constrain the Type C tokens constituting the identified object, to thereby segregate the tokens constituting the identified object in an image being processed, into the corresponding intrinsic material reflectance and illumination aspects of the object. 
     According to yet another feature of the exemplary embodiment, the CPU  12  is coupled to the Internet  26 . In this manner, the CPU  12  can access websites  28  on the Internet  26 . The websites  28  provide another source for an object database. For example, the CPU  12  can search the Internet  26  via, for example, a text-based search, to obtain information at an accessed website  28 , relevant to the material characteristics of an object identified in an image being processed. The material characteristics are used to determine the fixed anchor value described above. 
     Implementation of the constraint/solver model according to the techniques and teachings of U. S. Patent Publication No. US 2010/0142825, utilizing, for example, the Type C tokens and Type B tokens obtained, for example, via a log chromaticity clustering technique according to the present invention, and information from an object database  26 , provides a highly effective and efficient method for generating intrinsic images corresponding to an original input image. The intrinsic images can be used to enhance the accuracy, speed and efficiency of image processing, image analysis and computer vision applications. 
     According to yet another feature of the present invention, advantage is made of a correspondence between inherent characteristics of each of the intrinsic material reflectance and illumination images with observations of human visual perception. As observed, human perception of details of objects depicted in a scene recorded in an video file  18  is aligned with the details depicted in the intrinsic images for the material reflectance aspects of the scene. Moreover, human perception of motion depicted in a sequence of images for the scene is aligned with motion displayed in a sequence of intrinsic images for the illumination aspects of the scene. 
     Humans tend to perceive fine spatial detail with more clarity in static or slow-moving regions of a video and tend to perceive fast motion more clearly in larger spatial objects or regions of a video. In order to allow perception of both the fine details and the fast motion, conventional video compression techniques maintain high frame rates to allow for perception of smooth motion and high spatial resolution for perception of fine detail. 
       FIG. 15  shows a flow chart of a linear video stored in a video file  18  being compressed in accordance with a conventional video compression method for filtering, compression or other processing. A linear video is formed by a stream of video frames that are in an ordered sequence. For example, a first frame F 1  is followed by a second frame F 2 , which is followed by a third frame F 3 , etc. . . . . In step  400 , a video file is received at a computer. In a step  402 , gamma correction and/or tone adjustment are performed on the linear video. In a step  404 , the linear video is compressed or encoded for transmission or storage. An encoder proceeds to compress or encode the linear video according to a known compression format such as H.264/AVC, HEVC or another format. Then, in a step  406 , the compressed video is stored by the computer and/or transmitted, for example, via the Internet, to a remote device. In this conventional method, the compressed video at step  406  has the same number of frames as the linear video at step  400 . 
     Embodiments of the present invention allow the material component and the illumination component of a video to be separated from each other in a precompression technique into an independent material video and an independent illumination video for filtering. Such separation of the material and illumination videos allows adjustments to be made to the material and illumination video frames making up the video independently of each other for further reduction in video file size, yet maintaining aspects of the original video frames that are most important for human perception of videos. Because videos are formed of sequential images, it is possible to alter or remove individual video frames of the video without affecting the quality of the video from a human perception standpoint. Due to the importance of material reflectance of an image for fine details and object boundaries in a video, but not necessarily the shape and movement, it is possible to reduce the frame rate of the material images for storage or transmission without affecting the quality of the video from a human perception standpoint. Also, due to the importance of illumination of an image for the shape and movement in a video, but not necessarily the fine details and object boundaries, it is possible to reduce the detail of the illumination images storage or transmission without affecting the quality of the video from a human perception standpoint. 
       FIG. 16  shows a flow chart for processing a linear video, according to an embodiment of the present invention. The video processing method shown in  FIG. 16  reduces the material reflectance component of the linear video temporally and reduces the illumination component of the linear video spatially to further reduce the size of the linear video for transmission and/or storage, as compared with the conventional method described with respect to  FIG. 15 , but essentially maintaining the quality of the video from a human perception standpoint. Such further reduction in file size allows for more efficient storage and faster data transmission. In one alternative embodiment, the material reflectance component of the linear video may be reduced temporally, without reducing the illumination component of the linear video spatially. In another alternative embodiment, the illumination component of the linear video may be reduced spatially, with reducing the material reflectance component of the linear video temporally. These alternative embodiments still beneficially reduce the size of the video file. 
     In step  500 , the CPU  12  receives an original video file, for example, a video file  18  from the memory  16 . In step  502 , the CPU  12  operates to generate intrinsic images from the each of the video frames of the original video file, for example, according to the techniques described in detail above, to output illumination maps (illumination video frames forming an illumination video) (step  504 ) and reflectance maps (material video frames forming a material video) (step  506 ). 
     In step  508 , the CPU  12  operates to separately perform, either in a parallel operation, or in a sequence, an illumination component filtering on the illumination video frames in step  510  and a material component filtering on the material video frames in step  512 . In this embodiment, the illumination component filtering in step  510  includes spatially subsampling the illumination video and the material component filtering in step  512  includes temporally subsampling the material video. The spatial subsampling of the illumination video may include reducing the spatial resolution of each of the illumination video frames of the illumination video. For example, the spatial resolution of illumination video frames may reduced both horizontally and vertically by a factor of two, such that a spatial resolution W×H of the illumination video frames is reduced to W/2×H/2 while not affecting the frame rate F. The spatial resolution of the illumination video frames of the illumination video may also be decreased during the spatial subsampling by other amounts in other examples. The temporal subsampling of the material video may include removing j material video frame(s) out of every k material video frames of the material video in a repeating pattern. For example, where j=1 and k=2, every other material video frame is removed from the material video, in a repeating pattern of removing the first video frame of each group of two video frames and leaving the second video frame of the group of two video frames or in a repeating pattern of removing the second video frame of each group of two video frames and leaving the first video frame of the group of two video frames. 
     Also, for example, where j=2 and k=3, two out of every three material video frames may be removed from the material video during the temporal subsampling in a first repeating pattern where the first and second material video frames of each group of three material video frames are removed and the third material video frame of the group of three material video frames is not removed, a second repeating pattern where the first and third material video frames of each group of three material video frames are removed and the second material video frame of the group of three material video frames is not removed, or a third repeating pattern where the second and third material video frames of each group of three material video frames are removed and the first material video frame of the group of three material video frames is not removed. 
     The foregoing examples are merely illustrative and the number of material video frames of the material video removed and/or the pattern of removal may also be varied during the temporal subsampling by other amounts in other examples. 
     The spatial subsampling and the temporal subsampling reduce the sizes of the illumination video and the material video, reducing the size of the video file storing the illumination and material videos. In step  510 , the CPU  12  may perform one or more alternative or additional filtering processes on each of the illumination video frames and in step  512 , the CPU  12  may perform one or more alternative or additional filtering processes on each of the material video frames. 
       FIG. 17  shows an example of spatially subsampling an illumination video by reducing each of the illumination video frames by a factor of two horizontally and vertically from a spatial resolution W×H to W/2×H/2. Five exemplary illumination video frames, frames IF  12  to IF  16 , of a illumination video are shown. The spatial resolution of each of the illumination video frames IF 12  to IF 16  is reduced by a factor of two horizontally and vertically from a spatial resolution W×H to W/2×H/2, without altering the frame rate F of the illumination video. 
       FIG. 18  shows an example of temporally subsampling a material video by reducing the number of material video frames by a factor of two from a frame rate F to a frame rate F/2. Five sequential exemplary material video frames, frames MF 12  to MF 16 , of a material video are shown. The frame rate F of the material video is reduced by a factor of two from a frame rate F to a frame rate F/2 by removing material video frame MF 13  and material video frame MF  15 , without altering the spatial resolution W×H of frames MF 12 , MF 14 , MF 16 . 
     In a step  514 , the CPU  12  operates to separately interpolate the filtered illumination video and the filtered material video and then re-mix the interpolated illumination video and the interpolated material video according to a pixel-by-pixel or sample-by-sample operation to form a recombined intrinsic video. CPU  12  or the remote device operates to perform, either in a parallel operation, or in a sequence, separate interpolation processes on the filtered illumination video and the filtered reflectance video. In this embodiment, the file size of the interpolated illumination video and the interpolated material video are reduced compared the corresponding illumination video and material video created in step  508 . 
     The interpolating may include creating interpolated illumination frames from the filtered illumination frames created in the illumination component subsampling in step  508 . The interpolated illumination frames may be formed by interpolating spatially between pairs of horizontally and vertically adjacent pixels of each of the filtered illumination frames created in step  510  to output an interpolated illumination video (step  532 ). For example, referring to  FIG. 17 , illumination frames IF 12  to IF 16  formed by spatial subsampling may each be up-sampled by interpolating pixels spatially between pair of horizontally and vertically adjacent pixels. As mentioned in the example of  FIG. 17 , the filtered illumination frames IF 12  to IF 16  have the spatial dimensions W/2×H/2 and the frame rate F. For this example, the interpolating results in a video including a sequence of interpolated illumination frames at the original spatial resolution w×h and frame rate F. 
     The interpolating may also include creating interpolated material frames to replace the material frames removed in the material component subsampling in step  512 . The interpolated material frames may be formed by interpolating each pixel position of a material frame directly preceding the corresponding removed material frame and a material frame directly following the corresponding removed material frame to output an interpolated material video. For example, referring to  FIG. 18 , material frame MF 13  removed during the temporal subsampling may be replaced by an interpolated material frame created by interpolating each pixel position of material frames MF 12  and MF 14 ; and material frame MF 15  removed during the temporal subsampling may be replaced by an interpolated material frame created by interpolating each pixel position of material frames MF 14  and MF 16 . As mentioned in the example of  FIG. 18 , the filtered material video has the spatial dimensions W×H and the frame rate F/2. For this example, the interpolating results in a video including a sequence of material frames at the original resolution W×H and frame rate F. 
     In alternative embodiments, other known methods of interpolation, for example linear interpolation, bilinear interpolation, cubic interpolation or bicubic interpolation can be used in step  514 . 
     In a step  516 , gamma correction and/or tone adjustment may be performed on the recombined intrinsic video. In a step  518 , the recombined intrinsic video is compressed or encoded for transmission or storage. An encoder (or CPU carrying out the process) proceeds to compress or encode the recombined intrinsic video according to a known compression format such as H.264/AVC, HEVC or another format. 
     According to a feature of the present invention, in step  520 , the compressed recombined intrinsic video (video formed of filtered and interpolated intrinsic images) is stored by the CPU  12  in the memory  16  and/or transmitted, for example, via the Internet  26 , to a remote device configured, for example, as a website  28  (see  FIG. 1 ). The remote device comprises, for example, a PC, a smartphone, a tablet computer, or a device in a TV broadcast operation. 
       FIG. 19  is a flow chart for decompressing and recombining the compressed recombined intrinsic video stored or transmitted in step  520  of  FIG. 18 , according to an embodiment of the present invention. In step  522 , depending on whether the compressed recombined intrinsic video is stored or transmitted in step  520 , the compressed recombined intrinsic video is retrieved by CPU  12  or received by the remote device as a website  28  via the Internet  26 . 
     In a step  524 , a decoder of the CPU  12  or the remote device operates to decompress or decode the compressed recombined intrinsic video. 
     Each of steps  522  and  524  are implemented using known techniques for compression or decompression of digital video material, such as techniques compatible with one of ISO/MPEG-2 Visual, ITU-T H.264/AVC, HEVC or other known formats for compressed video material. 
     In step  526 , the CPU  12  or the remote device operates to output a video appearing to the human visual system to be of essentially the same video quality as the original video, for example, the video depicted in the video file  18  initially processed by the CPU  12  according to the routine of  FIG. 16 . 
       FIG. 20  shows a flow chart for processing a linear video, from video file  18 , according to another embodiment of the present invention. The video processing method shown in  FIG. 20  reduces the material reflectance component of the linear video temporally and reduces the illumination component of the linear video spatially to further reduce the size of the linear video, as compared with the conventional method described with respect to  FIG. 15 , but maintaining the quality of the video from a human perception standpoint. Such further reduction in the size of the video file allows for more efficient storage and faster data transmission. 
     Steps  600 ,  602 ,  604 ,  606  of  FIG. 20  are the same as steps  500 ,  502 ,  504 ,  506  of  FIG. 16 . In step  600 , the CPU  12  receives an original video file, for example, a video file  18  from the memory  16 . In step  602 , the CPU  12  operates to generate intrinsic images from the each of the video frames of the original video file, for example, according to the techniques described in detail above, to output illumination maps (illumination video frames forming an illumination video) (step  604 ) and reflectance maps (material video frames forming a material video) (step  606 ). 
     Steps  608 ,  610 ,  612  of  FIG. 20  are the same as steps  508 ,  510 ,  512  of  FIG. 16 . In step  608 , the CPU  12  operates to separately perform, either in a parallel operation, or in a sequence, an illumination component filtering on the illumination video frames in step  610  and a material component filtering on the material video frames in step  612 . In this embodiment, as described above with respect to  FIG. 16 , the illumination component filtering includes spatially subsampling the illumination video and the material component filtering includes temporally subsampling the material video. The spatial subsampling of the illumination video may include reducing the spatial resolution of each of the illumination video frames of the illumination video. The temporal subsampling of the material video may include reducing the frame rate of the material video by removing j material video frame(s) out of every k material video frames of the material video in a repeating pattern. Steps  610  and  612  may also include additional or alternative filtering operations. 
     Starting at step  614 , the method of  FIG. 20  begins to vary from the method of  FIG. 16 . In a step  614 , in contrast to the method of  FIG. 16 , in which the filtered material and illumination videos are first recombined, the CPU  12  may operate to separately perform either in a parallel operation, or in a sequence, gamma correction and/or tone adjustment on the filtered illumination video (step  616 ) and the filtered material video (step  618 ). 
     In a step  620 , the CPU  12  operates to separately compress or encode, either in a parallel operation, or in a sequence, filtered illumination video and the filtered material video, which are performed by separate encoders  620   a ,  620   b , respectively, of CPU  12 . For example, the CPU  12  operates to convert the illumination maps to a known sampling format such as RGB, YCrCb or YUV. The CPU  12  then proceeds to compress the converted illumination maps and reflectance maps according to a known compression format such as H.264/AVC, HEVC or another format. The individual encoders  620   a ,  620   b  may optionally communicate with each other while compressing the filtered illumination video and the filtered material video, respectively. In one embodiment, steps  610 ,  612  and/or steps  616 ,  618  may also be performed by encoders  620   a ,  620   b.    
     According to a feature of the present invention, in step  622 , the compressed filtered illumination video (video formed of filtered and compressed illumination images) and the compressed filtered material video (video formed of filtered and compressed material images), either in a parallel operation, or in a sequence, are stored by the CPU  12  in the memory  16  and/or transmitted, for example, via the Internet  26 , to a remote device configured, for example, as a website  28  (see  FIG. 1 ) in the form of two video streams, a stream of the compressed filtered illumination video and a stream of the compressed filtered material video, separately or together. The remote device comprises, for example, a PC, a smartphone, a tablet computer, or a device in a TV broadcast operation. 
       FIG. 21  is a flow chart for decompressing and recombining the compressed filtered illumination video and the compressed filtered material video from  FIG. 20 , according to an embodiment of the present invention. In step  624 , depending on whether the compressed recombined filtered intrinsic video is stored or transmitted in step  622 , the compressed recombined filtered intrinsic video is retrieved by CPU  12  or received by the remote device as a website  28  via the Internet  26  in the form of two video streams, a stream of the compressed filtered illumination video and a stream of the compressed filtered material video, separately or together. 
     In steps  626  and  628 , in contrast to step  524  of  FIG. 19 , in which the filtered material video and illumination are decompressed together, separate decoders  620   a ,  620   b  of the CPU  12  or the remote device operate to perform, either in a parallel operation, or in a sequence, a decompression or decoding processes. 
     In decompression process of step  626 , decoder  620   a  performs a decompression process on the compressed version of the illumination video to output the decompressed filtered illumination video. 
     In decompression process of step  628 , decoder  620   b  performs a decompression process on the compressed version of the material video to output the decompressed filtered reflectance video. 
     Each of steps  624 ,  626  and  628  are implemented using known techniques for compression or decompression of digital video material, such as techniques compatible with one of ISO/MPEG-2 Visual, ITU-T H.264/AVC, HEVC or other known formats for compressed video material. 
     In steps  630 ,  632 , in a similar same manner as in step  514  of figure, CPU  12  or the remote device operates to perform, either in a parallel operation, or in a sequence, a spatial interpolation process on the filtered illumination video and temporal interpolation process on the filtered reflectance video. 
     Step  630  may include creating interpolated illumination frames from the filtered illumination frames created in the illumination component subsampling in step  610 . The interpolated illumination frames by interpolating spatially between pairs of horizontally and vertically adjacent pixels of each of the filtered illumination frames created in step  610  to output an interpolated illumination video (step  634 ). Step  630  results in an illumination video including a sequence of illumination frames at the original resolution, frame rate F and spatial dimensions W×H. 
     Step  632  may include creating interpolated material frames to replace the material frames removed in the material component subsampling in step  612 . The interpolated material frames may be formed by interpolating each pixel position of a material frame directly preceding the corresponding removed material frame and a material frame directly following the corresponding removed material frame to output an interpolated material video (step  636 ). Step  632  results in a material video including a sequence of material frames at the original resolution W×H and frame rate F. 
     In step  638 , the CPU  12  or the remote device operates to recombine the illumination video output at step  634  and the material video output at step  636  to output a video appearing to the human visual system to be of essentially the same video quality as the original video (step  640 ), for example, the video depicted in the video file  18  initially processed by the CPU  12  according to the routine of  FIG. 20 . The recombined video can be created by the CPU  12  or the remote device using by calculating each of the video frames using the I=ML equation, as fully described above. 
       FIG. 22  shows a flow chart for processing a linear video, according to another embodiment of the present invention. The video processing method shown in  FIG. 22  reduces the material reflectance component of the linear video temporally and reduces the illumination component of the linear video spatially to further reduce the size of the linear video, as compared with the conventional method described with respect to  FIG. 15 , but essentially maintaining the quality of the video from a human perception standpoint. Such further reduction in the size of the video file allows for more efficient storage and faster data transmission. 
     Steps  700 ,  702 ,  704 ,  706  of  FIG. 22  are the same as steps  500 ,  502 ,  504 ,  506  of  FIG. 16 . In step  700 , the CPU  12  receives an original video file, for example, a video file  18  from the memory  16 . In step  702 , the CPU  12  operates to generate intrinsic images from the each of the video frames of the original video file, for example, according to the techniques described in detail above, to output illumination maps (illumination video frames forming an illumination video) (step  704 ) and reflectance maps (material video frames forming a material video) (step  706 ). 
     In step  708 , the CPU  12  operates to separately perform, either in a parallel operation, or in a sequence, an illumination component filtering on the illumination video frames in a step  710  and a material component filtering on the material video frames in a step  712 . In this embodiment, in contrast with the methods of  FIGS. 16 and 20 , the illumination component filtering includes spatially or other type of filtering the illumination video frames, to reduce the information content, without actually reducing the spatial resolution W×h of the illumination video frames. The material component filtering includes temporally or other type of filtering the material video frames, to reduce the information content, without actually reducing the frame rate F. 
     The filtering reduce the sizes of the illumination video and the material video. Filters may be properly chosen such that the size reduction and quality performance is adjusted to be essentially identical to the method described with respect to  FIGS. 16 and 19  and the method described with respect to  FIGS. 20 and 21 . The filtering may be performed by any appropriate filtering technique or techniques, including for example motion compensating filters, spatio-temporal filters, wavelet filters, subband filters. 
     Steps  714 ,  716 ,  718 ,  720 ,  722  of  FIG. 22  are the same as steps  614 ,  616 ,  618 ,  620 ,  622  of  FIG. 20 . In a step  714 , the CPU  12  may operate to separately perform, either in a parallel operation, or in a sequence, gamma correction and/or tone adjustment on the filtered illumination video (step  716 ) and the filtered material video (step  718 ). 
     In a step  720 , the CPU  12  operates to separately compress or encode, either in a parallel operation, or in a sequence, filtered illumination video and the filtered material video, which are performed by separate encoders  720   a ,  720   b , respectively, or CPU  12 . For example, the CPU  12  operates to convert the illumination maps to a known sampling format such as RGB, YCrCb or YUV. The CPU  12  then proceeds to compress the converted illumination maps and reflectance maps according to a known compression format such as H.264/AVC, HEVC or another format. The individual encoders  720   a ,  720   b  may optionally communicate with each other while compressing the filtered illumination video and the filtered material video, respectively. 
     According to a feature of the present invention, in step  722 , the compressed filtered illumination video (video formed of filtered and compressed illumination images) and the compressed filtered material video (video formed of filtered and compressed material images), either in a parallel operation, or in a sequence, are stored by the CPU  12  in the memory  16  and/or transmitted, for example, via the Internet  26 , to a remote device configured, for example, as a website  28  (see  FIG. 1 ). The remote device comprises, for example, a PC, a smartphone, a tablet computer, or a device in a TV broadcast operation. 
       FIG. 23  is a flow chart for decompressing and recombining the compressed filtered illumination video and the compressed filtered material video described with respect to  FIG. 22 , according to an embodiment of the present invention. The steps of  FIG. 23  are the same as the steps of  FIG. 21 , except that the filtered illumination video and the filtered material video are not interpolated. 
     In step  724 , depending on whether the compressed recombined filtered intrinsic video is stored or transmitted in step  722 , the compressed recombined filtered intrinsic video is retrieved by CPU  12  or received by the remote device as a website  28  via the Internet  26 . 
     In steps  726  and  728 , decoders  720   a ,  720   b  of the CPU  12  or the remote device operate to perform, either in a parallel operation, or in a sequence, decompression (decoding) processes. 
     In decompression process of step  726 , decoder  720   a  performs a decompression process on the compressed version of the illumination video to output the decompressed filtered illumination video (step  730 ). 
     In decompression process of step  728 , decoder  720   b  performs a decompression process on the compressed version of the material video to output the decompressed filtered reflectance video ( 732 ). 
     Each of steps  724 ,  726  and  728  are implemented using known techniques for compression or decompression of digital video material, such as techniques compatible with one of ISO/MPEG-2 Visual, ITU-T H.264/AVC, HEVC or other known formats for compressed video material. 
     In step  734 , the CPU  12  or the remote device operates to recombine the illumination video output at step  730  and the material video output at step  732  to output a video appearing to the human visual system to be of essentially the same video quality as the original video (step  736 ), for example, the video depicted in the video file  18  initially processed by the CPU  12  according to the routine of  FIG. 23 . The recombined video can be created by the CPU  12  or the remote device using by calculating each of the video frames using the I=ML equation, as fully described above. 
       FIG. 24  shows a flow chart for processing a linear video, according to another embodiment of the present invention. The video processing method shown in  FIG. 24  reduces the material reflectance component of the linear video temporally and reduces the illumination component of the linear video spatially to further reduce the size of the linear video, as compared with the conventional method described with respect to  FIG. 15 , but essentially maintaining the quality of the video from a human perception standpoint. Such further reduction in the size of the video file allows for more efficient storage and faster data transmission. The steps of  FIG. 24  are the same as the steps of  FIG. 16 , except that in the method of  FIG. 24 , like the method of  FIG. 22 , the illumination component filtering includes spatially filtering the illumination video frames, to reduce the information content, without actually reducing the spatial resolution W×H of the illumination video frames. Also, like the method of  FIG. 22 , the material component filtering includes temporally filtering the material video frames, to reduce the information content, without actually reducing the frame rate F. 
     Steps  800 ,  802 ,  804 ,  806  of  FIG. 24  are the same as steps  500 ,  502 ,  504 ,  506  of  FIG. 16 . In step  800 , the CPU  12  receives an original video file, for example, a video file  18  from the memory  16 . In step  802 , the CPU  12  operates to generate intrinsic images from the each of the video frames of the original video file, for example, according to the techniques described in detail above, to output illumination maps (illumination video frames forming an illumination video) (step  804 ) and reflectance maps (material video frames forming a material video) (step  806 ). 
     Steps  808 ,  810 ,  812  are the same as the steps  708 ,  710 ,  712  of  FIG. 22 . In step  808 , the CPU  12  operates to separately perform, either in a parallel operation, or in a sequence, an illumination component filtering on the illumination video frames in a step  810  and a material component filtering on the material video frames in a step  812 . In this embodiment, in contrast with the methods of  FIGS. 16 and 20 , the illumination component filtering includes spatially filtering the illumination video frames, to reduce the information content, without actually reducing the spatial resolution W×h of the illumination video frames. The material component filtering includes temporally filtering the material video frames, to reduce the information content, without actually reducing the frame rate F. 
     Spatial and temporal filters may be properly chosen such that the reduction in size and quality performance is adjusted to be essentially identical to the method described with respect to  FIGS. 16 and 19  and the method described with respect to  FIGS. 20 and 21 . The spatial filtering and the temporal filtering may be performed by any appropriate filtering technique or techniques, including for example motion compensating filters, spatio-temporal filters, wavelet filters, subband filters. 
     In a step  814 , the CPU  12  operates to re-mix the filtered illumination video and the filtered material video according to a pixel-by-pixel or sample-by-sample operation to form a recombined filtered intrinsic video including both the filtered illumination video frames and the filtered material video frames. 
     In a step  816 , the CPU  12  may operate to separately perform gamma correction and/or tone adjustment on the recombined intrinsic video. 
     In a step  818 , an encoder of CPU  12  compresses or encodes the recombined filtered intrinsic video for transmission or storage. The encoder proceeds to compress or encode the recombined filtered intrinsic video according to a known compression format such as H.264/AVC, HEVC or another format. 
     According to a feature of the present invention, in step  820 , the compressed recombined filtered intrinsic video (video formed of filtered intrinsic images) is stored by the CPU  12  in the memory  16  and/or transmitted, for example, via the Internet  26 , to a remote device configured, for example, as a website  28  (see  FIG. 1 ). The remote device comprises, for example, a PC, a smartphone, a tablet computer, or a device in a TV broadcast operation. 
       FIG. 25  is a flow chart for decompressing the compressed recombined filtered intrinsic video from  FIG. 24 , according to an embodiment of the present invention. The steps of  FIG. 25  are similar to the steps of  FIG. 23 , in that the illumination video and the material video are not subject to interpolating steps, but different because the illumination video and the material video were previously recombined and are decompressed together by the same decoder. In step  822 , depending on whether the compressed recombined filtered intrinsic video is stored or transmitted in step  820 , the compressed recombined filtered intrinsic video is retrieved by CPU  12  or received by the remote device as a website  28  via the Internet  26 . 
     In a step  824 , a decoder the CPU  12  or the remote device operates to perform a decompression or decoding process on the compressed recombined filtered intrinsic video to output the recombined video (step  826 ). The decompression or decoding is implemented using known techniques for compression or decompression of digital video material, such as techniques compatible with one of ISO/MPEG-2 Visual, ITU-T H.264/AVC, HEVC or other known formats for compressed video material. The output video appears to the human visual system to be of essentially the same video quality as the original video (step  736 ), for example, the video depicted in the video file  18  initially processed by the CPU  12  according to the routine of  FIG. 24 . The recombined video can be created by the CPU  12  or the remote device using by calculating each of the video frames using the I=ML equation, as fully described above. 
     According to yet another feature of the present invention, instead of segregating a video file into illumination and material videos, the video file is subject to a scale separation operation. Scale separation is a technique for separating local variation within an image from global variation. An image is separated into large-scale features and small-scale features. A known method for performing scale separation on an image is described in “Fast Bilateral Filtering for the Display of High-Dynamic-Range Images,” Frédo Durand and Julie Dorsey, ACM Transactions of Graphics (Proceedings of the ACM SIGGRAPH &#39;02 Conference). Durand and Dorsey describe as scale separation technique that uses a bilateral filter to separate an image into a “level” channel and a “detail” channel. The level channel includes the low frequency components of the image and depicts large scale variations of the image, without details, which are depicted in the detail channel as high frequency components of the image. As such, the level channel is a reasonable approximation of log illumination intensity of the image, and the detail channel is a reasonable approximation of the log material intensity. 
     It is also known that bilateral filtering can be applied to videos, as is discussed in “Seperable bilateral filtering for fast video preprocessing,” Tuan Q. Pham and Lucas J. van Vliet, International Conference on Multimedia and Expo, IEEE, 2005. Pham uses a bilateral filter to reduce the noise in a video before compression, which can improve video compression. The bilateral filter operates temporally as well as in the spatial and intensity dimensions. By blurring away fine details (much of which is noise) but leaving the important large structures of the video, the resulting video stream is a smaller file at the same quality or a file of the same size with higher quality. 
     Another method of scale separation is described in “Two methods for display of high contrast images,” Jack Tumblin and Jessica K. Hodgins, ACM Trans. on Graphics 18, 1, pages 56-94, which describes using a Guassian blur filtering operation that can be used to separate out large-scale features and small-scale features. 
     With this feature of the present invention, advantage is made of a correspondence between low frequency and high frequency scale separation channels with observations of human visual perception. As observed, human perception of details of objects depicted in a scene recorded in a video file  18  is aligned with the details depicted in high frequency data provided by scale separation operations. Moreover, human perception of motion depicted in a sequence of images for the scene is aligned with motion displayed in a sequence of images formed from low frequency data provided by scale separation operations. 
     Embodiments of the present invention allow the high frequency component and the low frequency component of a video to be separated from each other in a precompression technique into separate components for filtering. Such separation of the high and low frequency components allows adjustments to be made to the high and low frequency components making up the video independently of each other for further reduction in video file size, yet maintaining aspects of the original video frames that are most important for human perception of videos. It is possible to alter or remove individual video frames of the video without affecting the quality of the video from a human perception standpoint. Due to the importance of high frequency components of an image for fine details and object boundaries in a video, but not necessarily the shape and movement, it may be possible to reduce the frame rate of the high frequency components for storage or transmission without affecting the quality of the video from a human perception standpoint. Also, due to the importance of low frequency components of an image for the shape and movement in a video, but not necessarily the fine details and object boundaries, it is possible to reduce the detail of the low frequency components for storage or transmission without affecting the quality of the video from a human perception standpoint. 
       FIG. 26  shows a flow chart for processing a video, according to another embodiment of the present invention. The video processing method shown in  FIG. 26 , in contrast with the methods described with respect to  FIGS. 16 to 23  involves scale separating a video file, instead of segregating the video file into illumination and material maps. Because the high frequency component resulting from scale separation is an approximation of the material reflectance of an image and the low frequency component resulting from scale separation is an approximation of the illumination component of an image, this embodiment can achieve similar results as the methods described with respect to  FIGS. 16 to 23 , without the complex processing required to segregate a video file into illumination and material maps. 
     The video processing method shown in  FIG. 26  reduces the high frequency component of the video temporally and reduces the low frequency component of the video spatially to further reduce the size of the video for transmission and/or storage, as compared with the conventional method described with respect to  FIG. 15 , but essentially maintaining the quality of the video from a human perception standpoint. Such further reduction in file size allows for more efficient storage and faster data transmission. In one alternative embodiment, the high frequency component of the video can be reduced temporally, without reducing the low frequency component of the video spatially. In another alternative embodiment, the low frequency component of the video can be reduced spatially, without reducing the high frequency component of the linear video temporally. These alternative embodiments still beneficially reduce the size of the video file. 
     In step  900 , the CPU  12  receives an original video file, for example, a linear video file  18  from the memory  16 . In step  902 , the CPU  12  operates to scale separate the video file to output low frequency components—the larger structures—in a level video (step  904 ) and high frequency components—the details—in a detail video (step  906 ). In one preferred embodiment, which is based on the method disclosed in “Fast Bilateral Filtering for the Display of High-Dynamic-Range Images,” Frédo Durand and Julie Dorsey, ACM Transactions of Graphics (Proceedings of the ACM SIGGRAPH &#39;02 Conference), the scale separation in step  902  includes applying a bilateral filter implementation to the video file to generate the level channel (the low frequency components representing the large scale features), then the level channel is subtracted from the original image to generate the detail channel (the high frequency components representing the fine details). The bilateral filter can be the temporal bilateral filter disclosed in “Seperable bilateral filtering for fast video preprocessing,” Tuan Q. Pham and Lucas J. van Vliet, International Conference on Multimedia and Expo, IEEE, 2005, but can be applied to the video in the manner described by Durand and Dorsey to separate the video into the large scale features (approximation of illumination) and the fine details (approximation of reflectance). Accordingly, the temporal bilateral filter can be applied to generate the level video, then the level video is subtracted from the original video to generate the detail video. The temporal bilateral filter can be applied with a larger range sigma and spatial sigma (as described by Durand and Dorsey) than it would be in a noise reduction technique. 
     The level video and the detail video can be calculated as the bilateral blur of the intensity image or as the bilateral blur of each of the R, G and B independently. If the level video and detail video are calculated from the intensity image, the color from the original image is recombined with the level video and the detail video after they are filtered and recombined. If the level video and detail video are calculated from each of the R, G and B independently, the level video and detail video include the color components and it is not necessary to recombine the color from the original image with the level video and the detail video after they are filtered and recombined. 
     Because the video file is a linear video file, step  902  includes putting the video file through a log transform before the temporal bilateral filter is applied. In an alternative embodiment, the log transform may be replaced by a gamma correction operation, which behaves very similarly to a log transform operation. 
     The exemplary embodiment of scale separation involves bilateral filtering in the log domain; however, in other embodiments of the present invention, the scale separation can be performed by using any blurring filter, for example a Gaussian filter. The blurring can be performed in any domain, for example linear, log or gamma corrected. Performance will be better with any filter of the class of “edge preserving blurring filters,” such as bilateral filters, median filters, anisotropic diffusion, or guided filters, as described in “Guided Image Filtering,” K. He, J. Sun and X. Tang, Proceeding of European Conference Computer Vision (ECCV) (2010). 
     In step  908 , the CPU  12  operates to separately perform, either in a parallel operation, or in a sequence, a level component filtering on the level video output in step  910  and a detail component filtering on the detail video output in step  912 . In this embodiment, the level component filtering in step  910  includes spatially subsampling the level video and the detail component filtering in step  912  includes temporally subsampling the detail video. In this embodiment, similar to the subsampling described above with respect to  FIG. 16 , the level component filtering includes spatially subsampling the level video and the detail component filtering includes temporally subsampling the detail video. The spatial subsampling of the level video can include reducing the spatial resolution of each of the level video frames of the level video. The temporal subsampling of the detail video can include reducing the frame rate of the detail video by removing j detail video frame(s) out of every k detail video frames of the detail video in a repeating pattern. Steps  910  and  912  can also include additional or alternative filtering operations. 
     The spatial subsampling and the temporal subsampling reduce the sizes of the level video and the detail video, reducing the size of the video file storing the level and detail videos. In step  910 , the CPU  12  can perform one or more alternative or additional filtering processes on each of the level video frames, and in step  912 , the CPU  12  can perform one or more alternative or additional filtering processes on each of the detail video frames. 
     In a step  914 , the CPU  12  operates to separately interpolate the filtered level video and the filtered detail video and then re-mix the interpolated level video and the interpolated detail video according to a pixel-by-pixel or sample-by-sample operation to form a recombined scale-separated video. In this embodiment, CPU  12  operates to perform, either in a parallel operation, or in a sequence, separate interpolation processes on the filtered level video and the filtered detail video. If the level video and detail video are in the log domain, the re-mixing involves element-wise adding all of the channels of the pixels of the level video and the detail video (log(video)=log(level)+log(detail)), then exponentiating the log space output to get back to the linear-space version of the video. In this embodiment, the recombined scale-separated video file has a reduced file size compared to the video file input at step  900 . 
     Similar to step  514  of  FIG. 16 , the interpolating in step  914  can include creating interpolated level frames from the filtered level frames created in the level component subsampling in step  908  by interpolating spatially between pairs of horizontally and vertically adjacent pixels of each of the filtered level frames created in step  910  to output an interpolated level video for re-mixing. Also, similar to step  514 , the interpolating in step  914  can also include creating interpolated detail frames to replace the detail frames removed in the detail component subsampling in step  912  by interpolating each pixel position of a detail frame directly preceding the corresponding removed detail frame and a detail frame directly following the corresponding removed detail frame to output an interpolated detail video for re-mixing. 
     In a step  916 , gamma correction and/or tone adjustment can be performed on the recombined scale-separated video. In a step  918 , the recombined scale-separated video is compressed or encoded for transmission or storage. An encoder (or CPU carrying out the process) proceeds to compress or encode the recombined scale-separated video according to a known compression format such as H.264/AVC, HEVC or another format. 
     According to a feature of the present invention, in step  920 , the compressed recombined scale-separated video (video formed of filtered, interpolated and scale-separated images) is stored by the CPU  12  in the memory  16  and/or transmitted, for example, via the Internet  26 , to a remote device configured, for example, as a website  28  (see  FIG. 1 ). The remote device comprises, for example, a PC, a smartphone, a tablet computer, or a device in a TV broadcast operation. 
       FIG. 27  shows a flow chart for processing a video, according to another embodiment of the present invention. Similar to the embodiment described in  FIG. 26 , the video processing method shown in  FIG. 27  involves scale separating a video file. 
     In contrast to the method of  FIG. 26 , video file  18  received by CPU  12  can be a non-linear video file including non-linear, tone-adjusted data. Some video cameras can provide linear video, but most only provide non-linear, tone adjusted video. In step  1000 , the camera records the linear video file and performs a gamma correction/tone adjustment on the linear video file in a step  1002  before outputting a non-linear video to CPU  12 . In step  1004 , similar to step  902 , the CPU  12  operates to scale separate the video file to output low frequency components—the larger structures—in a level video (step  1006 ) and high frequency components—the details—in a detail video (step  1008 ). In one preferred embodiment, the scale separation in step  1004  includes applying a temporal bilateral filter implementation to the video file in the same manner as described above in step  902  to separate the video file into the detail video and the level video. In contrast to step  902 , because the camera performed the gamma correction/tone adjustment in step  1002 , the CPU  12  does not have to put the video through a log transform before the temporal bilateral filter is applied. A gamma corrected image or video is very similar to an image or video in the log domain. Accordingly, the scale separation technique works approximately as well on gamma corrected videos as it does on linear videos with a log transform. 
     In step  1010 , as with step  908 , the CPU  12  operates to separately perform, either in a parallel operation, or in a sequence, a level component filtering on the level video output in step  1012 , which includes spatially subsampling the level video, and a detail component filtering on the detail video output in step  1014 , which includes temporally subsampling the detail video. Steps  1012  and  1014  can also include additional or alternative filtering operations. 
     In a step  1016 , as with step  914 , the CPU  12  operates to separately interpolate the filtered level video and the filtered detail video and then re-mix the interpolated level video and the interpolated detail video according to a pixel-by-pixel or sample-by-sample operation to form a recombined scale-separated video. In this embodiment, CPU  12  operates to perform, either in a parallel operation, or in a sequence, separate interpolation processes on the filtered level video and the filtered detail video. The re-mixing involves element-wise adding all of the channels of the pixels of the level video and the detail video (recombined video=interpolated level video+interpolated detail video). 
     Similar to step  914  of  FIG. 26 , the interpolating in step  1016  can include creating interpolated level frames from the filtered level frames created in the level component subsampling in step  1010  by interpolating spatially between pairs of horizontally and vertically adjacent pixels of each of the filtered level frames created in step  1012  to output an interpolated level video for re-mixing. Also, similar to step  914 , the interpolating in step  1016  can also include creating interpolated detail frames to replace the detail frames removed in the detail component subsampling in step  1014  by interpolating each pixel position of a detail frame directly preceding the corresponding removed detail frame and a detail frame directly following the corresponding removed detail frame to output an interpolated detail video for re-mixing. 
     In a step  1018 , as with step  918 , the recombined scale-separated video is compressed or encoded for transmission or storage. Because gamma correction/tone adjustment was applied to the video file in step  1002 , gamma correction/tone adjustment does not need to be applied to the video before compression as with step  916  in  FIG. 26 . 
     According to a feature of the present invention, in step  1020 , as with step  920 , the compressed recombined scale-separated video (video formed of filtered, scale-separated and interpolated images) is stored by the CPU  12  in the memory  16  and/or transmitted, for example, via the Internet  26 , to a remote device configured, for example, as a website  28  (see  FIG. 1 ). 
       FIG. 28  shows a flow chart for processing a linear video, from video file  18 , according to another embodiment of the present invention. Steps  1100 ,  1102 ,  1104 ,  1106  of  FIG. 28  are the same as steps  900 ,  902 ,  904 ,  906  of  FIG. 26 . In step  1100 , the CPU  12  receives an original video file, for example, a video file  18  from the memory  16 . In step  1102 , the CPU  12  operates to generate scale-separated images from the each of the video frames of the original video file to output a level video (step  1104 ) and a detail video (step  1106 ). In this embodiment, the level video is in the same range of values as the original video, while the detail video is originally centered around zero and is shifted to be centered around 128 before saving, and then shifted back when adding the level and detail videos back together. 
     Steps  1108 ,  1110 ,  1112  of  FIG. 28  are the same as steps  908 ,  910 ,  912  of  FIG. 26 . In step  1108 , the CPU  12  operates to separately perform, either in a parallel operation, or in a sequence, a level component filtering on the level video output in step  1110 , which includes spatially subsampling the level video, and a detail component filtering on the detail video output in step  1112 , which includes temporally subsampling the detail video. Steps  1110  and  1112  can also include additional or alternative filtering operations. 
     Starting at step  1114 , the method of  FIG. 28  begins to vary from the method of  FIG. 26 . In a step  1114 , in contrast to the method of  FIG. 26 , in which the filtered level and details videos are first interpolated and recombined, the CPU  12  can operate to separately perform either in a parallel operation, or in a sequence, gamma correction and/or tone adjustment on the filtered level video (step  1116 ) and the filtered detail video (step  1118 ). 
     In a step  1120 , the CPU  12  operates to separately compress or encode, either in a parallel operation, or in a sequence, filtered level video and the filtered detail video, which are performed by separate encoders  1120   a ,  1120   b , respectively, of CPU  12 . For example, the CPU  12  operates to convert the level maps and detail maps to a known sampling format such as RGB, YCrCb or YUV. The CPU  12  then proceeds to compress the converted level maps and detail maps according to a known compression format such as H.264/AVC, HEVC or another format. The individual encoders  1120   a ,  1120   b  can optionally communicate with each other while compressing the filtered level video and the filtered detail video, respectively. In one embodiment, steps  1110 ,  1112  and/or steps  1116 ,  1118  can also be performed by encoders  1120   a ,  1120   b.    
     According to a feature of the present invention, in step  1122 , the compressed filtered level video (video formed of filtered and compressed level images) and the compressed filtered detail video (video formed of filtered and compressed detail images), either in a parallel operation, or in a sequence, are stored by the CPU  12  in the memory  16  and/or transmitted, for example, via the Internet  26 , to a remote device configured, for example, as a website  28  (see  FIG. 1 ) in the form of two video streams, a stream of the compressed filtered level video and a stream of the compressed filtered detail video, separately or together. The compressed filtered level video and the compressed filtered detail video can then be decompressed, interpolated recombined in the same manner as the compressed filtered illumination video and the compressed filtered material video are with respect to  FIG. 21 . 
       FIG. 29  shows a flow chart for processing a linear video, from video file  18 , according to another embodiment of the present invention. Steps  1200 ,  1202 ,  1204 ,  1206 ,  1208  of  FIG. 29  are the same as steps  1000 ,  1002 ,  1004 ,  1006 ,  1008  of  FIG. 27 . In step  1200 , the camera records the linear video file and performs a gamma correction/tone adjustment on the linear video file in a step  1202  before outputting a non-linear video to CPU  12 . In step  1204 , the CPU  12  operates to generate scale-separated images from the each of the video frames of the original video file to output a level video (step  1206 ) and a detail video (step  1208 ). In this embodiment, the level video is in the same range of values as the original video, while the detail video is originally centered around zero and is shifted to be centered around 128 before saving, and then shifted back when adding the level and detail videos back together. As with step  1004 , the scale separation in step  1204  includes applying a temporal bilateral filter implementation to the video file in the same manner as described above in step  902  to separate the video file into the detail video and the level video, without first putting the video through a log transform. 
     Steps  1210 ,  1212 ,  1214  of  FIG. 28  are the same as steps  1010 ,  1012 ,  1014  of  FIG. 27 . In step  1210 , the CPU  12  operates to separately perform, either in a parallel operation, or in a sequence, a level component filtering on the level video output in step  1212 , which includes spatially subsampling the level video, and a detail component filtering on the detail video output in step  1214 , which includes temporally subsampling the detail video. Steps  1212  and  1214  can also include additional or alternative filtering operations. 
     Starting at step  1216 , the method of  FIG. 29  begins to vary from the method of  FIG. 27 . In a step  1216 , as with step  1120  of  FIG. 28 , the CPU  12  operates to separately compress or encode, either in a parallel operation, or in a sequence, the filtered level video and the filtered detail video, which are performed by separate encoders  1216   a ,  1216   b , respectively, of CPU  12 . 
     Then in step  1218 , as with step  1122  of  FIG. 28 , the compressed filtered level video (video formed of filtered and compressed level images) and the compressed filtered detail video (video formed of filtered and compressed detail images), either in a parallel operation, or in a sequence, are stored by the CPU  12  in the memory  16  and/or transmitted, for example, via the Internet  26 , to a remote device configured, for example, as a website  28  (see  FIG. 1 ) in the form of two video streams, a stream of the compressed filtered level video and a stream of the compressed filtered detail video, separately or together. The compressed filtered level video and the compressed filtered detail video can then be decompressed, interpolated recombined in the same manner as the compressed filtered illumination video and the compressed filtered material video are with respect to  FIG. 21 . 
     In alternative embodiments of the methods described with respect to  FIGS. 26 to 29 , the level component filtering includes applying spatially or other type of filtering the level video frames, without subsampling and interpolating. The filtering may be performed by any appropriate filtering technique or techniques, including for example motion compensating filters, spatio-temporal filters, wavelet filters, subband filters. 
       FIG. 30  shows a flow chart for processing a video, according to another embodiment of the present invention. Similar to the embodiments described in  FIGS. 26 to 29 , the video processing method shown in  FIG. 30  involves scale separating a video file. 
     In step  1300 , a processor, for example CPU  12 , receives a gamma corrected video file. The gamma corrected video file may be, for example, in CIE Rec. 603 color space for standard definition transmission, in CIE Rec. 709 color space for HD transmission, in sRGB color space, or simple gamma correction wherein linear values from the camera sensor have undergone a simple gamma correction as such as output=input̂(1/2.2). 
     In a step  1302 , the processor converts the gamma corrected video file into a linear video file in an approximately linear color space, by applying an inverse of gamma correction to the gamma corrected video file. The inverse may be in CIE Rec. 603, in CIE Rec. 709, in sRGB, or simple gamma depending on the coding standard of the gamma corrected video file. If the coding standard of the gamma corrected video file is not known, any of CIE Rec. 603, CIE Rec. 709, sRGB, or simple gamma may be selected. In one preferred embodiment, if the coding standard of the gamma corrected video file is not known, CIE Rec. 709 is selected for inversion. In a step  1304 , the processor puts the linear video through a log transform to convert the linear video file into a log video file in a log color space. 
     Next, in a scale separation step  1306 , the processor operates to scale separate the video file to output low frequency components—the larger structures—in a level video (step  1308 ) and high frequency components—the details—in a detail video (step  1310 ). An edge preserving blur filter is applied to the log video file in a step  1308 . In this preferred embodiment, the edge preserving blur filter is a guided filter, such as the one described in “Guided Image Filtering,” K. He, J. Sun and X. Tang, Proceeding of European Conference Computer Vision (ECCV) (2010), mentioned above. In step  1308 , the guided filter generates a level video. In the preferred embodiment, the guided filter has a spatial sigma of 15 and a range sigma of 1.2, applied to the log video file. The level video is then used in a step  1310  to generate the detail video by subtracting the level video from the input log video file. Following steps  1308 ,  1310 , the detail video and the level video are exponentiated to convert the detail video and the level video back into linear space for further filtering. 
     In step  1312 , after the level video and the detail video are exponentiated, the processor operates to separately perform, either in a parallel operation, or in a sequence, a level component filtering on the level video and a detail component filtering on the detail video. In this embodiment, the level component filtering in step  1314  includes spatially blurring the level video, and the detail component filtering in step  1316  includes temporally blurring the detail video. 
     The temporal blurring is performed by a temporal Gaussian filter. In one preferred embodiment, the temporal Gaussian filter is a standard Gaussian filtering performing a simple weighted average of four frames, centered around frame N: (1*(frame N−2)+2*(frame N−1)+8*(frame N)+2*(frame N+1))/13.0. 
     In another preferred embodiment, the temporal blurring may be performed by a temporal Gaussian filter with motion compensation. Motion compensation uses estimation of the motion of real-world surfaces between frames in the video sequence. Motion estimation can be obtained by any one of several methods well-known in the art. One class of motion estimation techniques is optical flow. For a survey of optical flow techniques, see, for example, “A Database and Evaluation Methodology for Optical Flow,” S. Baker, D. Scharstein, J. P. Lewis, S. Roth, M. Black, and R. Szeliski, International Journal of Computer Vision, 92(1):1-31, March 2011. A second class of motion estimation techniques uses feature correspondence to track specific scene elements between frames, such as is described in “Feature Based Methods for Structure and Motion Estimation,” Philip H. S. Torr and Andrew Zisserman, ICCV Workshop on Vision Algorithms, pages 278-294, 1999. A third class of motion estimation techniques uses frequency-domain correspondence, such as is described in “An FFT-based technique for translation, rotation, and scale-invariant image registration”, B. S Reddy and B. N. Chatterji, IEEE Transactions on Image Processing 5, no. 8 (1996): 1266-1271. Finally, a fourth class of motion estimation techniques is block-based motion estimation. For a survey of block-based motion estimation techniques, see “Survey on Block Matching Motion Estimation Algorithms and Architectures with New Results,” Yu-Wen Huang, Ching-Yeh Chen, Chen-Han Tsai, Chun-Fu Shen, Liang-Gee Chen, Journal of VLSI signal processing systems for signal, image and video technology, March 2006, Volume 42, Issue 3, pp 297-320. 
     Any of these techniques can be used to find correspondences between frames as an estimation of the scene content motion between frames. Such motion estimation may be in the form of integer pixel offsets between frames or may include subpixel alignment with fractional offsets between frames. Additionally, motion estimation may be calculated as a single translation, scale, and/or rotation of the entire frame as a whole, or it may be calculated densely, allowing spatially varying motion estimation within each frame. 
     In another exemplary embodiment of the present invention, motion compensation can be implemented using a pyramid-based block motion estimation for pixel motion according to a feature of the present invention.  FIG. 31  shows a graphic representation of an image pyramid for the exemplary image of  FIG. 3 b   . The CPU  12  can form multi-resolution representations of the original image (the finest scale) as a scale-spaced pyramid of representations of the image. As shown in  FIG. 31 , in a scale-spaced pyramid, a set of images is generated, for example, using well known unsmoothed down-sampling, Gaussian and/or Laplacian pyramid formation techniques, each at a different scale of resolution relative to the resolution of the original image, from a finest resolution (defined as the original image shown in  FIG. 3 b   ) to relatively coarser resolutions, at each upper level of the pyramid, for example, a downsampled 2× version of the image and a downsampled 4× (coarsest scale) version of the image, as shown. 
     Image pyramids, such as the one shown in  FIG. 31 , support efficient multi-scale image processing by extending the portion of an image examined during execution of the process. For example, as shown in  FIG. 31 , the portion of the image covered by a fixed-sized block defined by the black box illustrated in each of the finest scale, downsampled 2× and downsampled 4× (coarsest scale) versions of the image, covers a larger portion of the image at each higher level of the pyramid, as clearly shown. 
     According to a feature of the present invention, advantage is taken of the increased portion of an image, for example, a frame of a video, when examined at a coarsest scale version of the image. In an exemplary embodiment of the present invention, a block motion estimation is used. Referring now to  FIG. 32 , a flow chart is shown for a block motion estimation. In step  1400 , an image to match, image to distort and a search radius are selected and input to the CPU  12 . For example, the image to match can be an original, current material frame, as shown in  FIG. 18 , and the image to distort is an adjacent material frame, either a material frame that is a previous or subsequent frame, to the image to match, in the sequence shown in  FIG. 18 . A block to match is sized according to a preselected number of pixel, for example a 3×3 square block of pixels. 
     In step  1402 , the CPU  12  operates, for each pixel location x, y in the spatial plane of the image to match, to set a 3×3 pixel square block to match around each location x, y. In step  1404 , the CPU  12 , arranges a 3×3 block to distort, at each x′, y′ location in the image to distort, within a search neighborhood around the corresponding x, y location in the image to distort. The search neighborhood is sized according to the selected search radius. For example, a default search radius is set at 8, providing a 17×17 pixel area as the search neighborhood. 
     In step  1406 , the CPU  12  computes, for each block to match from the image to match, a distance in a color space, from the respective block to match to each 3×3 block to distort around each x′, y′ pixel location within the respective search neighborhood for the relevant x, y location, in the image to distort. For example, for each computation, the CPU  12  can execute a sum squared difference computation for the 3×3 blocks. For each pixel location x, y in the image to match, the CPU  12  identifies the smallest distance in the color space, between the block to match for the respective location x, y and the blocks to distort for the x′, y′ locations in the search neighborhood of the image to distort. Thus, for each location x, y in the image to match, the CPU  12  identifies a corresponding, smallest distance block to distort at an x′, y′ location, from the search neighborhood of the image to distort, that most likely corresponds to the x, y pixel location in the image to match. 
     In step  1408 , the CPU  12  estimates the motion of each x, y pixel location in the image to match, between the image to match and the image to distort. For example, for each x, y location, the motion estimate is x′−x, y′−y, with the x′, y′ location being the block to distort within the search neighborhood, at the smallest distance to the respective block to match, as identified in step  1406 . This provides a motion vector for each pixel of the image to match. 
     In step  1410 , the CPU  12  outputs a motion estimate image with a motion estimate for each pixel location, as estimated in step  1408 . 
     In the execution of the block motion estimate routine of  FIG. 32 , the CPU  12  must perform calculations relative to (2*search radius+1) 2  possible 3×3 blocks in the search neighborhood of the image to distort. This fact results in a considerable computational overhead directly related to the size of the selected search radius. According to a feature of the present invention, the use of an image pyramid enables the use of a relatively small search radius to examine a relatively large portion of a coarsest scale version of an image frame, as described above. 
     Referring now to  FIG. 33 , there is shown a flow chart for a pyramid block motion estimation according to a feature of the present invention. In step  1500 , an image to match, image to distort and a search radius are selected and input to the CPU  12 , as in the routine of  FIG. 32 . In addition, a number of pyramid levels is set. In the example of  FIG. 31 , there are two levels beyond the original image (downsampled 2× and downsampled 4×, coarsest scale versions of the image). In step  1502 , the CPU  12  operates to build an image pyramid for each of the image to match (current frame) and image to distort (adjacent frame), using any known image pyramid building techniques. 
     In step  1504 , the CPU  12  performs the block motion estimation routine of  FIG. 32 , as described above, using the coarsest levels of each of the image to match and image to distort. At the coarsest level, the search radius can be set, for example, at 2, resulting in a 5×5 pixel block search neighborhood around each pixel location. This compares to the default search radius of 8, resulting in a 17×17 search neighborhood, for execution of the block motion estimation without the use of a pyramid. A 5×5 pixel area search neighborhood will cover a significantly greater portion of the coarsest version of the image than the 17×17 pixel area search neighborhood covers at the finest level of the image, with a significantly reduced computational overhead. 
     In steps  1506  and  1508 , the CPU  12  up-samples the motion estimates for the coarser level, as determined in step  1504 , to a next finer level, and uses the up-sampled estimate for estimating motion at the next finer level. Steps  1506  and  1508  are performed by the CPU  12  for each level of the pyramid, including the original image in the pyramid. 
     As shown in  FIG. 33 , for each level in step  1506 , the up-sampling of the motion estimates to a finer level is performed by the CPU  12  by setting, for example: 
       FineMotion(2 x, 2 y )=2*CoarseMotion( x,y ), 
       FineMotion(2 x+ 1,2 y )=2*CoarseMotion( x,y ), 
       FineMotion(2 x, 2 y+ 1)=2*CoarseMotion( x,y ), 
       FineMotion(2 x+ 1,2 y+ 1)=2*CoarseMotion( x,y ). 
     (Wherein each FineMotion is the new motion estimate at the finer level, and CoarseMotion is the motion estimate at the coarser level, when downsampled 2× at each higher level of the pyramid) 
     In step  1508 , for each iteration, the CPU  12  uses the FineMotion up-sampled in step  1506 , and performs a block motion estimate at the current level of the pyramid, initiating a search at a search neighborhood centered at a location of the current, finer level determined by the motion estimate for the coarser level. 
       FIGS. 34 a - e    show diagrams of an exemplary pyramid search center determined by execution of the routine of  FIG. 33 . A motion estimate vector is provided for each pixel of a 2×2 coarsest level version of an image ( FIG. 34 a   ), according to the routine of  FIG. 33 . Step  1506  is executed by the CPU  12  to provide up-sampled motion estimate vectors for each pixel location of a 4×4 next finer level of the pyramid ( FIG. 34 b   ). The CPU  12  executes the block motion estimation routine of  FIG. 32 , in the example of  FIGS. 34 a - e   , for the pixel location circled in the image shown in  FIG. 34   c.    
     According to a feature of the present invention, the execution of the routine of  FIG. 32  at the location and level of the pyramid shown in  FIG. 34 c   , is initiated at a location predicted by the motion vector from the coarser level ( FIG. 34 a   ).  FIG. 34 d    shows the motion vector at the coarser level used to up-sample the motion vector at the location shown in  FIG. 34 c   . The value and direction of the motion vector of  FIG. 34 d    predicts that the pixel location in the image to distort will likely be at the location shown in  FIG. 34 e   . The CPU  12  then executes the routine of  FIG. 32 , to estimate the motion of the location shown in  FIG. 34 c   , centering the search neighborhood around the location predicted at the coarser level, as shown in  FIG. 34   e.    
     During execution of the routine of  FIG. 32  for the location shown in  FIG. 34 c   , the CPU  12  can use a relatively small search radius, for example, the search radius of 2 used at the coarsest level, since the centering of the search neighborhood around the location estimated at the coarser level ( FIG. 34 e   ) improves the probability of locating the location in the image to distort. 
     Referring once again to  FIG. 33 , upon completing steps  1506  and  1508  for each level, including the finest level, the CPU provides the motion estimates for the finest level (step  1510 ) and outputs a motion estimation image for the finest level (step  1512 ). 
       FIG. 35  is a flow chart for an expanded pyramid block motion estimation according to a feature of the present invention. In step  1600 , an image to match, image to distort, a search radius and a number pf pyramid levels are selected and input to the CPU  12 , as in the routine of  FIG. 33 . In addition, a pyramid level to stop expanding search is set, as will be described. In the routine of  FIG. 35 , the CPU  12  performs steps  1602  and  1604 , as in the routine of  FIG. 33 . 
     In the expanded pyramid block motion estimation according to the present invention, steps  1606  and  1608  are also performed as in the routine of  FIG. 33 , with the added feature of expanding the number of search neighborhoods examined by the CPU  12  at designated levels of the pyramid. For example, as shown in  FIGS. 36 a - c   , the motion vectors for two neighbor locations at the coarser level ( FIG. 36 a   ) are used to predict locations at the finer level where motion estimation is to be performed, as shown in  FIG. 36 b   . A search neighborhood is initiated by centering at each of those locations, as well as at the actual pixel location for which the motion estimation is being performed, as shown in  FIG. 36 c   . The CPU  12  then executes the routine of  FIG. 32 , to estimate the motion of the location shown in  FIG. 34 c   , centering search neighborhoods around each of the locations shown in  FIGS. 36 b  and  c , and 34 e   . The expanded searches are performed by the CPU  12  at each level, up until the pyramid level to stop expanding search set in step  1600  is reached. 
     A best result is selected by the CPU  12  as the single block to distort x′, y′ having the smallest distance in a color space, to the block to match x, y, from among the several search neighborhoods in the expanded pyramid block motion estimation embodiment of the present invention. In an additional exemplary embodiment of the present invention, an n-best match technique can be implemented instead of a single best match. 
     Steps  1610  and  1612  are performed by the CPU  12  as in the routine of  FIG. 33 . 
     To perform a temporal Gaussian filter with motion compensation, motion estimation between frames is first computed, for example, by any method described in the previous paragraphs, such as the block-based pyramid motion estimation according to the present invention. Let the motion from frame n to frame m be represented as (MX(x,y,n,m), MY(x,y,n,m)) where MX(x,y,n,m) is the motion in the x direction at location (x,y) between frames n and m, and MY(x,y,n,m) is likewise the motion in the y direction at location (x,y) between frames n and m. To find the temporally blurred version of frame n at pixel location (x,y), a weighted average of the motion-compensated locations in nearby frames is computed. The motion-compensated location in frame m of the original location (x,y) in frame n is (x+MX(x,y,n,m), y+MY(x,y,n,m)). If motion estimation includes non-integer alignment (i.e. subpixel alignment), then any standard interpolation method, such as bilinear or bicubic interpolation, can be used to find the proper interpolated value between pixel locations. 
     In a preferred embodiment, the temporal Gaussian filter with motion compensation is computed such that the blurred value at location (x,y) in frame n is (1*frame(n−2, x+MX(x,y,n,n−2), y+MY(x,y,n,n−2))+2*frame(n−1, x+MX(x,y,n,n−1), y+MY(x,y,n,n−1))+8*frame(n, x, y)+2*frame(n+1, MX(x,y,n,n+1), MY(x,y,n,n+1)))/13.0. Here, frame(n, x, y) represents the pixel value in frame n at location (x,y). If the location (x,y) includes non-integer values (for subpixel alignment), then a standard interpolation technique, such as bilinear or bicubic interpolation, is used to determine subpixel values. 
     In another preferred embodiment, the temporal Gaussian filter with motion compensation is computed such that the blurred value at location (x,y) in frame n is (1*frame(n−2, x+MX(x,y,n,n−2), y+MY(x,y,n,n−2))+2*frame(n−1, x+MX(x,y,n,n−1), y+MY(x,y,n,n−1))+8*frame(n, x, y)+2*frame(n+1, x+MX(x,y,n,n+1), y+MY(x,y,n,n+1))+1*frame(n+2, x+MX(x,y,n,n+2), y+MY(x,y,n,n+2)))/14.0. 
     In another preferred embodiment, the temporal Gaussian filter with motion compensation is computed such that the blurred value at location (x,y) in frame n is (1*frame(n−2, x+MX(x,y,n,n−2), y+MY(x,y,n,n−2))+2*frame(n−1, x+MX(x,y,n,n−1), y+MY(x,y,n,n−1))+8*frame(n, x, y))/11.0. 
     In another preferred embodiment, the temporal Gaussian filter with motion compensation is computed such that the blurred value at location (x,y) in frame n is (1*frame(n−2, x+MX(x,y,n,n−2), y+MY(x,y,n,n−2))+5*frame(n−1, x+MX(x,y,n,n−1), y+MY(x,y,n,n−1))+8*frame(n, x, y)+5*frame(n+1, x+MX(x,y,n,n+1), y+MY(x,y,n,n+1))+1*frame(n+2, x+MX(x,y,n,n+2), y+MY(x,y,n,n+2)))/20.0. 
     In another preferred embodiment, the temporal Gaussian filter with motion compensation is computed such that the blurred value at location (x,y) in frame n is (1*frame(n−2, x+MX(x,y,n,n−2), y+MY(x,y,n,n−2))+5*frame(n−1, x+MX(x,y,n,n−1), y+MY(x,y,n,n−1))+8*frame(n, x, y))/14.0. 
     In another preferred embodiment, the temporal Gaussian filter with motion compensation is computed such that the blurred value at location (x,y) in frame n is (5*frame(n−2, x+MX(x,y,n,n−2), y+MY(x,y,n,n−2))+7*frame(n−1, x+MX(x,y,n,n−1), y+MY(x,y,n,n−1))+8*frame(n, x, y)+7*frame(n+1, x+MX(x,y,n,n+1), y+MY(x,y,n,n+1))+5*frame(n+2, x+MX(x,y,n,n+2), y+MY(x,y,n,n+2)))/32.0. 
     The spatial blurring is performed by an edge-preserving blurring filter. In this preferred embodiment, the edge-preserving blurring filter is a guided filter, such as the one described in “Guided Image Filtering,” K. He, J. Sun and X. Tang, Proceeding of European Conference Computer Vision (ECCV) (2010), mentioned above. 
     A first example of spatial blurring involves applying a gamma correction from the definition of sRGB space to the linear-space level channel, then applying a guided filter with a spatial sigma of 3 and a range sigma of 0.025. After the guided filter is applied, an inverse sRGB gamma correction is applied to get back to linear space. 
     A second example of spatial blurring involves first converting the linear-space level video back to log space (or not exponentiating the level video from log space to linear space after step  1308 ), then applying a guided filter with a spatial sigma of 3 and a range sigma of 0.175. After the guided filter is applied, the level video is converted back to linear space by exponentiating. 
     A third example of spatial blurring involves first converting the linear-space level video back to log space (or not exponentiating the level video from log space to linear space after step  1308 ), then applying a guided filter with a spatial sigma of 3 and a range sigma of 0.125. After the guided filter is applied, the level video is converted back to linear space by exponentiating. 
     In a step  1318 , the processor operates to re-mix the spatially blurred level video and the temporally blurred detail video. The re-mixing involves multiplying the spatially blurred level video, which is in linear space, times temporally blurred detail video, which is also in linear space. 
     In a step  1320 , the processor converts the recombined scale-separated video, which is in linear space, back into the input gamma corrected space to form an output video. In a preferred embodiment, after step  1320 , the scale-separated recombined video is in the same color space as in step  1300  and the gamma correction applied in step  1320  involves the same coding standard as the inverse gamma correction applied in step  1302 . In this embodiment, the output video is essentially visually indistinguishable from the video file input at step  1300 . Tone adjustment can also be performed on the output video. In a step  1322 , the recombined scale-separated video is output for compression. 
     Due to the scale separation and filtering, the output video file is capable of being compressed by a greater amount with equivalent setting than the video file input at step  1300 . When compression is applied by any standard compression technique such as H.264 or HEVC to both the original video input at step  1300  and the output video in step  1322 , with similar compression settings, the compressed output video is smaller than the compressed original video (i.e., video that has not been scale separated, filtered and recombined). 
     In additional exemplary embodiments of the present invention, the pyramid-based block motion estimation is performed by the CPU  12  on the video prior to the scale separation and/or at other times during the method of the present invention, for example, after scale separation and filtering, and/or with the spatial filtering steps. 
     In the preceding specification, the invention has been described with reference to specific exemplary embodiments and examples thereof. It will, however, be evident that various modifications and changes may be made thereto without departing from the broader spirit and scope of the invention as set forth in the claims that follow. The specification and drawings are accordingly to be regarded in an illustrative manner rather than a restrictive sense.