Patent Publication Number: US-9843812-B2

Title: Video transmission system with color gamut partitioning and method of operation thereof

Description:
CROSS-REFERENCE TO RELATED APPLICATION(S) 
     The present application contains subject matter related to co-pending U.S. patent application Ser. No. 14/541,741 filed Nov. 14, 2014. The related application is assigned to Sony Corporation and the subject matter thereof is incorporated herein by reference thereto. 
     This application claims the benefit of U.S. Provisional Patent Application Ser. No. 62/002,400 filed May 23, 2014, and the subject matter thereof is incorporated herein by reference in its entirety. 
    
    
     TECHNICAL FIELD 
     The present invention relates generally to a video transmission system, and more particularly to a system for encoding video transmissions with color gamut partitioning for data compression. 
     BACKGROUND ART 
     With the advanced development of camera technology, the amount of data associated with a single frame has grown dramatically. A few years ago camera technology was limited to a few thousand pixels per frame. That number has shot past 10 million pixels per frame on a relatively inexpensive camera and professional still and movie cameras are well beyond 20 million pixels per frame. 
     This increase in the number of pixels has brought with it breath-taking detail and clarity of both shapes and colors. As the amount of data needed to display a high definition frame has continued to grow, the timing required to display the data on a high definition television has dropped from 10&#39;s of milliseconds to less than two milliseconds. The unprecedented clarity and color rendition has driven the increase in the number of pixels that we desire to view. 
     In order to transfer the now massive amount of data required to identify the Luma (Y), the Chroma blue (Cb), and the Chroma red (Cr) for every pixel in the frame, some reduction in the data must take place. Luma is associated with the brightness value and both Chroma blue (Cb), and the Chroma red (Cr) are associated with the color value. Several techniques have been proposed, which trade a reduction in detail for full color, a reduction in color for more detail, or a reduction in both detail and color. There is yet to be found a balanced approach that can maintain the detail and represent the full color possibilities of each frame. 
     Thus, a need still remains for video transmission system with color prediction that can minimize the transfer burden while maintaining the full detail and color content of each frame of a video stream. In view of the ever increasing demand for high definition movies, photos, and video clips, it is increasingly critical that answers be found to these problems. In view of the ever-increasing commercial competitive pressures, along with growing consumer expectations and the diminishing opportunities for meaningful product differentiation in the marketplace, it is critical that answers be found for these problems. Additionally, the need to reduce costs, improve efficiencies and performance, and meet competitive pressures adds an even greater urgency to the critical necessity for finding answers to these problems. 
     DISCLOSURE OF THE INVENTION 
     The embodiments of the present invention provide a method of operation of a video transmission system including: receiving a first video frame from an input device, the first video frame having base frame parameters; dividing a color gamut into uniform regions for collecting color data from pixels of the base frame parameters; collecting pixel statistics from each of the uniform regions from the base frame parameters; determining chroma partition coordinates from the pixel statistics; deriving a search pattern of search points based on the chroma partition coordinates; and selecting a search point from the search pattern for color mapping of the first video frame. 
     The embodiments of the present invention provides a video transmission system, a video transmission unit for receiving a first video frame from an input device, the first video frame having base frame parameters; dividing a color gamut into uniform regions for collecting color data from pixels of the base frame parameters; collecting pixel statistics from each of the uniform regions from the base frame parameters; determining chroma partition coordinates from the pixel statistics; deriving a search pattern of search points based on the chroma partition coordinates; and selecting a search point from the search pattern for color mapping of the first video frame. 
     Certain embodiments of the invention have other steps or elements in addition to or in place of those mentioned above. The steps or element will become apparent to those skilled in the art from a reading of the following detailed description when taken with reference to the accompanying drawings. 
    
    
     
       BRIEF DESCRIPTION OF THE DRAWINGS 
         FIG. 1  is a functional block diagram of a video transmission system in an embodiment of the present invention. 
         FIG. 2  is an exemplary partitioning of the color gamut used in the video transmission system of  FIG. 1  in an embodiment of the present invention. 
         FIG. 3  is an exemplary method for chrominance partitioning using non-uniform regions. 
         FIG. 4  is an exemplary method for chrominance partitioning using uniform regions in another embodiment of the present invention. 
         FIG. 5  is an exemplary method for determining the search points of a pixel using uniform regions in an embodiment of the present invention. 
         FIG. 6  is an exemplary diagram of an arrangement of luma and chroma samples used in phase alignment. 
         FIG. 7  is an exemplary method for the preprocessing of statistics using the chrominance square shown in  FIG. 4 . 
         FIG. 8  is a flow chart of a method of operation of a video transmission system in another embodiment of the present invention. 
         FIG. 9  is an exemplary method for determining prediction errors of a partition from L×2×2 non-uniform regions. 
         FIG. 10  is a flow chart of a method of operation of a video transmission system in a further embodiment of the present invention. 
     
    
    
     BEST MODE FOR CARRYING OUT THE INVENTION 
     The following embodiments are described in sufficient detail to enable those skilled in the art to make and use the invention. It is to be understood that other embodiments would be evident based on the present disclosure, and that system, process, or mechanical changes may be made without departing from the scope of the embodiments of the present invention. 
     In the following description, numerous specific details are given to provide a thorough understanding of the invention. However, it will be apparent that the invention may be practiced without these specific details. In order to avoid obscuring the embodiments of the present invention, some well-known circuits, system configurations, and process steps are not disclosed in detail. 
     The drawings showing embodiments of the system are semi-diagrammatic and not to scale and, particularly, some of the dimensions are for the clarity of presentation and are shown exaggerated in the drawing FIGs. Similarly, although the views in the drawings for ease of description generally show similar orientations, this depiction in the FIGs. is arbitrary for the most part. Generally, the invention can be operated in any orientation. 
     Where multiple embodiments are disclosed and described, having some features in common, for clarity and ease of illustration, description, and comprehension thereof, similar and like features one to another will ordinarily be described with similar reference numerals. 
     The term “module” referred to herein can include software, hardware, or a combination thereof in an embodiment of the present invention in accordance with the context in which the term is used. For example, the software can be machine code, firmware, embedded code, and application software. Also for example, the hardware can be circuitry, processor, computer, integrated circuit, integrated circuit cores, a pressure sensor, an inertial sensor, a microelectromechanical system (MEMS), passive devices, or a combination thereof. 
     The term “unit” referred to herein means a hardware device, such as an application specific integrated circuit, combinational logic, core logic, integrated analog circuitry, or a dedicated state machine. The color components of a pixel within a video frame, such as the three values for Luminance (Y) and Chrominance (C b  and C r ). 
     Referring now to  FIG. 1 , therein is shown a functional block diagram of a video transmission system  100  in an embodiment of the present invention. The functional block diagram of a video transmission system  100  depicts a video transmission unit  102  linked to a video decoder unit  104  by a video stream transport  106 , which carries the compressed bit stream. The video decoder unit  104  can activate a reference capture unit  107  for interpreting the video stream transport  106 . The video decoder unit  104  can be coupled to a display  108 , such as a high definition television, a computer display, a tablet display, a smart phone display, or the like, by a decoded picture stream  110 . 
     The video stream transport  106  can be a wired connection, a wireless connection, a digital video disk (DVD), FLASH memory, or the like. The video stream transport  106  can capture the coded video stream from the video transmission unit  102 . 
     The video transmission unit  102  can include a control unit  103  for performing system operations and for controlling other hardware components. For example, the control unit  103  can include a processor, an embedded processor, a microprocessor, a hardware control logic, a hardware finite state machine (FSM), a digital signal processor (DSP), or a combination thereof. The control unit  103  can provide the intelligence of the video system, can control and operate the various sub-units of the video transmission unit  102 , and can execute any system software and firmware. 
     The video transmission unit  102  can include the various sub-units described below. An embodiment of the video transmission unit  102  can include a first input color space unit  112 , which can receive a first video frame  113  or a portion of a video frame. An input device  109  can be coupled to the video transmission unit  102  for sending a source video signal of full color to the video transmission unit  102 . The video input sent from the input device  109  can include color data  111 , which can include Luminance (Y) values and Chrominance (Cb and Cr) values for the whole picture. 
     The first input color space unit  112  can be coupled to a base layer encoder unit  114 , which can determine a Luma (Y) level for the first video frame  113  captured by the first input color space unit  112 . The base layer encoder unit  114  can output a first encoded video frame  116  as a reference for the contents of the video stream transport  106 . The first encoded video frame  116  can be loaded into the reference capture unit  107 , of the video decoder unit  104 , in order to facilitate the decoding of the contents of the video stream transport  106 . 
     The base layer encoder unit  114  can extract a set of base frame parameters  117  from the first video frame  113  during the encoding process. The base frame parameters  117  can include range values of Luminance (Y) and Chrominance (Cb and Cr). 
     The base layer encoder unit  114  can be coupled to a base layer reference register  118 , which captures and holds the base frame parameters  117  of the first video frame  113  held in the first input color space unit  112 . The base layer reference register  118  maintains the base frame parameters  117  including values of the Luminance and Chrominance derived from the first input color space unit  112 . 
     The base layer reference register  118  can provide a reference frame parameter  119 , which includes range values of Luminance (Y) and Chrominance (C b  and C r ) and is coupled to a color mapping unit  120  and the phase alignment unit  150 . The color mapping unit  120  can map Luminance (Y) and Chrominance (C b  and C r ) in the frame parameter  119  to the Luminance (Y′) and Chrominance (C b ′ and C r ′) in the color reference frame  121 . 
     The color reference frame  121  can provide a basis for a resampling unit  122  to generate a resampled color frame reference  124 . The resampling unit  122  can interpolate the color reference frame  121  on a pixel-by-pixel basis to modify the resolution or bit depth of the resampled color frame reference  124 . The resampled color frame reference  124  can match the color space, resolution, or bit depth of subsequent video frames  128  based on the Luminance and Chrominance of the first video frame  113  associated with the same encode time. 
     The first video frame  113  and the subsequent video frames  128  can be matched on a frame-by-frame basis, but can differ in the color space, resolution, or bit depth. By way of an example, the reference frame parameter  119  can be captured in BT.709 color HD format using 8 bits per pixel, while the resampled color frame reference  124  can be presented in BT.2020 color 4K format using 10 bits per pixel. It is understood that the frame configuration of the resampled color frame reference  124  and the subsequent video frames  128  are the same. 
     The phase alignment unit  150  is coupled to the base layer reference register  118  and to the mapping parameter determination unit  152  for aligning luma sample locations with chroma sample locations and aligning chroma sample locations with luma sample locations. Chroma sample locations are usually misaligned with luma sample locations. For example, in input video in 4:2:0 chroma format, the spatial resolution of chroma components is half of that of luma component in both horizontal and vertical directions. 
     The sample locations can be aligned by phase shifting operations that rely on addition steps. It has been found that phase alignment on the luma (Y) sample locations, chroma blue (Cb) sample locations, and chroma red (Cr) samples locations of the reference frame parameter  119  can be implemented with several additions and shifts per sample, which has much less impact on computational complexity than multiplication steps. 
     A second input color space unit  126  can receive the subsequent video frames  128  of the same video scene as the first video frame  113  in the same or different color space, resolution, or bit depth. The subsequent video frames  128  can be coupled to an enhancement layer encoding unit  130 . Since the colors in the video scene represented by the first video frame  113  and the subsequent video frames  128  are related, the enhancement layer encoding unit  130  can differentially encode only the difference between the resampled color frame reference  124  and the subsequent video frames  128 . This can result in a compressed version of a subsequent encoded video frame  132  because the differentially encoded version of the subsequent video frames  128  can be applied to the first encoded video frame  116  for decoding the subsequent video frames  128  while transferring fewer bits across the video stream transport  106 . 
     A matrix mapping with cross-color prediction for Luminance (Y) and Chrominance (C b  and C r ) can be calculated by the color mapping unit  120  using the equations as follows: 
     
       
         
           
             
               
                 
                   
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     where the variables of g** and b* are mapping parameters. For each region, the output of Y′, C b ′ and C r ′ of the color mapping process is computed from the input from Equation 1a and 1b above. The y of the first input color space  112  is obtained by phase alignment of Y to the sampling position of C b  and C r  of the first input color space  112 . The c b  and c r  of the first input color space  112  are obtained by phase alignment of C b  and C r  to the sampling position of Y of the first input color space  112 . It has been found that the color mapping unit  120  can use equation 1a and equation 1b to map color between a base layer and an enhancement layer using addition and shifting steps instead of more computer intensive trilinear and tetrahedral interpolation. 
     The phase alignment operations performed inside the color mapping unit  120  are the same as the phase alignment performed by the phase alignment unit  150  as defined by standard Scalable High Efficiency Video coding (SHVC). The phase alignment of the sampling positions will be explained in further detail below. 
     A downscale unit  154  is used to downscale input from the second input color space unit  126 . The subsequent video frames  128  from the second input color space unit  126  can be sent to the downscale unit  154  before creation of an enhancement layer by the enhancement layer encoding unit  130 . The subsequent video frames  128  are downscaled to a much lower resolution to match the resolution provided by the base layer encoder unit  114 . The downscale unit  154  is coupled to the mapping parameter determination unit  152  for sending a downscaled video input  155  to the mapping parameter determination unit  152 . 
     The mapping parameter determination unit  152  is used to determine mapping parameters  157  and to estimate the corresponding prediction errors between the downscale video input  155  and the output of the color reference frame  121  from the color mapping unit  120 . The mapping parameter determination unit  152  determines the parameters in a 3D lookup table color gamut scalability (CGS) model on the inputted video layers. 
     The mapping parameter determination unit  152  can be coupled to the color mapping unit  120 . Using 3D lookup tables based on uniform and non-uniform partitioning, the mapping parameter determination unit  152  can determine more accurate mapping parameters. The mapping parameters  157  are sent to the color mapping unit  120 . 
     The color mapping unit  120  can use the mapping parameters  157  from the mapping parameter determination unit  152 . The color mapping unit  120  uses the mapping parameters  157  to color map color data in the reference frame parameter  119  to the color data in the color reference frame  121 . The mapping parameter determination unit  152  will be explained in further detail below. 
     A bit stream multiplex unit  134  multiplexes the first encoded video frame  116  and the subsequent encoded video frame  132  for the generation of the video stream transport  106 . During operation, the bit stream multiplex unit  134  can pass look-up table (LUT), generated by the mapping parameter determination unit  152 , that can be used as a reference to predict the subsequent encoded video frame  132  from the first encoded video frame  116 . It has been found that this can minimize the amount of data sent in the video stream transport  106  because the pixels in the first input color space unit  112  and the second input color space unit  126  represent the same scene, so the pixels in the first color space unit  112  can be used to predict the pixels in the second input color space unit  126 . 
     Since the color relationship between the first video frame  113  and the subsequent video frames  128  may not change drastically over time, the LUT for mapping color from the reference frame parameter  119  to the color reference frame  121  is only transferred at the beginning of the video scene or when update is needed. The LUT can be used for any follow-on frames in the video scene until it is updated. 
     It has been discovered that an embodiment of the video transmission unit  102  can reduce the transfer overhead and therefore compress the transfer of the video stream transport  106  by transferring the first encoded video frame  116  as a reference. This allows the subsequent encoded video frame  132  of the same video scene to be transferred to indicate only the changes with respect to the associated first encoded video frame  116 . 
     Referring now to  FIG. 2 , therein is shown an exemplary partitioning of the color gamut  201  used in the video transmission system  100  of  FIG. 1  in an embodiment of the present invention. The partition of the color space  201  can be figuratively represented by a cube having a luminance axis  202  of (Y), a chroma blue axis  204  of (Cb), and a chroma red axis  206  of (Cr). 
     The cube shown in  FIG. 2  can represent a color gamut scalability (CGS) model based on a 3D lookup table. For example, a base layer color space can be split into small cubes, where each cube is associated with the mapping parameters in Equations 1a and 1b for mapping base layer color in the cube to enhancement layer color. For a given base layer color sample in a cube, the computation of its prediction in the enhancement layer color space is made using the mapping parameters of the cube with Equations 1a and 1b, above. 
     The color (Y,Cb,Cr) of a pixel  211  at a location in a picture is a point in the cube, which is defined by [0, Y max )×[0, Cb max )×[0, Cr max ), where Y max =2 BitDepthY , and Cb max =Cr max =2 BitDepthC . The exemplary partitioning of the color gamut  201  as a CGS model depicts the luminance axis  202  that can be divided into eight uniform steps. 
     The chroma blue axis  204  can proceed away from the luminance axis  202  at a 90 degree angle. The Chroma red axis  206  can proceed away from the luminance axis  202  and the chroma blue axis  204  at a 90 degree angle to both. For example, the representation of the color gamut  201  is shown as a cube divided into 8×2×2 regions. 
     The exemplary partitioning shows a method of using a 8×2×2 non-uniform partitioning of the color gamut  201 . The partitioning of Y or the luminance axis  202  is divided into eight uniform regions whereas the partitioning of the chroma blue axis  204  and the chroma red axis  206  can be divided jointly into four regions, where the regions of the chroma blue axis  204  and the chroma red axis  206  are non-uniform. 
     The partitioning of the chroma blue axis  204  and the chroma red axis  206  can be indicated by the coordinates of of point (m, n) or as chroma partition coordinates  250 . The partition for Cb is m and the partition of Cr is n, where 0≦m&lt;Cb max  and 0≦n&lt;Cr max . The partitions of Cb and Cr are independent of Y and are signaled relative to the mid position. 
     Using the color space model, color statistics of the pixels can be collected and mapped from information provided by the base layer reference register  118 . These color statistical values can fall within a luminance range  260 , a chroma blue range  262 , and a chroma red range  264 . These color statistical range values are used by the system to provide a prediction for the encoding process of the subsequent encoded video frame  132  of  FIG. 1 . 
     The relative position (m−Cb max /2) and (n−Cr max /2) are signaled, where Cb max =Cr max =2 BitDepthC . Each pixel collected from the base frame parameters  117  is assigned into one of the regions created by the chroma partition coordinates  250 . To locate chroma partition boundaries, the offsets relative to the uniform partitioning in dot line (centerlines), which correspond to zero chroma values, are signaled for the two chroma components, respectively, in bitstream. 
     For example, a blue offset  252  represents the value for the offset for Cb and a red offset  254  represents the value for the offset for Cr. Generally, the partition offsets of chroma components are highly content dependent. To determine suboptimal offsets at encoder side with minimal computation, it has been found that average values of all samples in each chroma component in base layer reconstructed picture are calculated and used as the positions of the non-uniform partition boundaries. 
     Consequently, the offsets are calculated relative to the centerlines and signaled as part of the color mapping parameters. The determination of the chroma partition coordinates  250  will be explained in further detail below. 
     It has been discovered that the partitioning of the color gamut  201  into 8×2×2 non-uniform regions can further minimize the amount of data encoded in the subsequent encoded video frame  132  because only a single partition value for the chroma blue axis  204  and a single partition value for Chroma red axis  206  can be transferred as opposed to a different partition for each of the luminance regions of the luminance axis  202 . This reduction in coding overhead results in better balance of bitrate and the detail transferred in the video stream transport  106  of  FIG. 1 . The data transfer of the LUT can be reduced by the minimization of the single partition value for chroma blue axis  204  and the single partition value for Chroma red axis  206  used in the differential encoding of the subsequent encoded video frame  132 . 
     Referring now to  FIG. 3 , therein is shown an exemplary method for chrominance partitioning using non-uniform regions  303 . The example shows a two-dimensional example of the partitioning of the chrominance (Cb, Cr) of the color gamut  201  of  FIG. 2 . Since the partition of Luminance (Y) is uniform, the partitioning of Cb and Cr can be represented by a two dimensional square or a chrominance square  302 . For illustrative purposes, the example can represent a top view of the 8×2×2 cube shown in  FIG. 2 . 
     The chrominance square  302  includes the chrominance values, excluding the luminance values, of a pixel from (0, 0) to (Cb max , Cr max ). The chrominance square  302  can be partitioned into a plurality of the non-uniform regions  303 , such as the 2×2 regions shown by the fine dotted lines. 
     In this example, the partition of the color gamut  201  can be figuratively represented by a square divided into four non-uniform regions. The partitioning of the chrominance square  302  is designated by the chroma partition coordinates  250  of (m, n), which designate a chroma blue partition  304  and a chroma red partition  306 . 
     A collection of chrominance statistics from the base frame parameters  117  of  FIG. 1  determine the values for the chroma partition coordinates  250 . For example, m is equal to the average values of Cb collected from the base frame parameters  117  for all of the pixels in a single image. The average values of Cb can be rounded into an integer and can be represented by:
 
m= Cb 
 
     The coordinate of n equals the average of Cr collected from the base frame parameters  117  for all of the pixels in the same image. The average values of Cr can be rounded into an integer and represented by the equation:
 
n= Cr 
 
     The chroma partition coordinates  250  determine the non-uniform partitioning of the chrominance square  302  into four regions. The values of (m, n) are converted into offset values  308  relative the midpoints of the range of Cb and Cr. For color mapping parameters, the offsets are calculated relative to the centerlines and signaled. 
     Referring now to  FIG. 4 , therein is shown an exemplary method for chrominance partitioning using uniform regions  401  in another embodiment of the present invention. The example shows the chrominance square  302  divided into 32×32 regions. 
     The chrominance square  302  can include uniform partitions of the chroma blue axis  204  and the chroma red axis  206 . The number of partitions for the chroma blue axis  204  can be an even integer of M and the number of partitions for the chroma red axis  206  can be an even integer of N. For illustrative purposes, the chrominance square  302  can include uniform M×N partitions of 32×32, where M and N are powers of two. It is understood that M×N can be 2×2, 4×4, 8×8, and 16×16, as examples. 
     Each intersection of the 32×32 square can include a grid point  402 . The grid point  402  is the designated partition for the estimated chrominance value (Cb, Cr) of a pixel. The grid point  402  value for the chroma blue axis  204  can be represented by (u). The grid point  402  for the chroma red axis  206  can be represented by (v). 
     It has been found that to reduce computation time, the search points for the chroma partition coordinate  250  of  FIG. 2  is limited to the grid point  402 : 
                 (       u   ·       Cb   max     M       ,     v   ·       Cr   max     N         )     ⁢   0     ≤   u   &lt;   M         
and where 0≦v&lt;N.
 
     Referring now to  FIG. 5 , therein is shown an exemplary method for determining the search points of a pixel using uniform regions in an embodiment of the present invention. For illustrative purposes, the example shows the chrominance square  302  divided into uniform 32×32 regions although it is understood that the values of M and N can be a power of 2. 
     The uniform M×N partitioning of the chrominance square  302  can be used to find a more accurate 2×2 non-uniform partitioning than the partition designated by the average values of Cb and Cr. To improve the partition designated by the average values of Cb and Cr, the average values of Cb and Cr, which were collected from the base frame parameters  117  of  FIG. 1 , can be quantized into discrete units to find a search point  502  of a pixel on the chrominance square  302 . The quantized search points of (ū,  v ) are quantized by the following equations:
 
 ū =└ Cb · M /Cb max ┘
 
   v   =└ Cr · N /Cb max ┘
 
     where the boundary or limits of the search points for the partition coordinate  250  are limited to (u×Cb max /M, v×Cr max /N) where 0≦u&lt;M and 0≦v&lt;N. It has been found that computation is reduced because searches can be limited to these boundaries instead of conducting a search at all possible partition coordinates. 
     The search point  502  of (ū,  v ) can then be used to calculate a search pattern  504  of estimated chrominance values. The search pattern  504  includes a set of grid points that are near the search point  502 , for example, where |u−ū|≦1 and |v− v |≦1. The example search pattern  504  can be represented by the surrounding points on the chrominance square that are less than or equal to one away from the search point of (ū,  v ). 
     Referring now to  FIG. 6 , therein is shown an exemplary diagram of an arrangement of luma and chroma samples used in phase alignment. The example shows a typical arrangement of luma and chroma samples in 4:2:0 chroma format. 
     The squares marked as Y correspond to a luma sample location  602 . The circles marked with a C correspond to a chroma sample location  604 . For input video in 4:2:0 chroma format, the spatial resolution of chroma components is half that of luma component in both horizontal and vertical directions. Further, the chroma sample locations  604  are usually misaligned with luma sample locations  602 . In order to improve the precision of the color mapping process, it has been found that sample locations of different color components can be aligned before the cross component operations are applied. 
     For example, when calculating the output for luma component, chroma sample values will be adjusted to be aligned with the corresponding the luma sample location  602  to which they apply. Similarly, when calculating the output for chroma components, luma sample values will be adjusted to be aligned with the corresponding samples of the chroma sample location  604  to which they apply. 
     When calculating the output for luma component, chroma sample values will be adjusted to be aligned with the corresponding luma sample location to which they apply as shown in the pseudo code example below:
 
 y ( C   0 )=( Y ( Y   0 )+ Y ( Y   4 )+1)&gt;&gt;1
 
 y ( C   1 )=( Y ( Y   2 )+ Y ( Y   6 )+1)&gt;&gt;1
 
 y ( C   2 )=( Y ( Y   8 )+ Y ( Y   12 )+1)&gt;&gt;1
 
 y ( C   3 )=( Y ( Y   10 )+ Y ( Y   14 )+1)&gt;&gt;1
 
 c   b ( Y   4 )=( C   b ( C   0 )×3 +C   b ( C   2 )+2)&gt;&gt;2
 
 c   b ( Y   5 )=(( C   b ( C   0 )+ C   b ( C   1 ))×3+( C   b ( C   2 )+ C   b ( C   3 ))+4)&gt;&gt;3
 
 c   b ( Y   8 )=( C   b ( C   2 )×3 +C   b ( C   0 )+2)&gt;&gt;2
 
 c   b ( Y   9 )=(( C   b ( C   2 )+ C   b ( C   3 ))×3+( C   b ( C   0 )+ C   b ( C   1 ))+4)&gt;&gt;3
 
 c   r ( Y   4 )=( C   r ( C   0 )×3 +C   r ( C   2 )+2)&gt;&gt;2
 
 c   r ( Y   5 )=(( C   r ( C   0 )+ C   r ( C   1 ))×3+( C   r ( C   2 )+ C   r ( C   3 ))+4)&gt;&gt;3
 
 c   r ( Y   8 )=( C   r ( C   2 )×3 +C   r ( C   0 )+2)&gt;&gt;2
 
 c   r ( Y   9 )=(( C   r ( C   2 )+ C   r ( C _3))×3+( C   r ( C   0 )+ C   r ( C   1 ))+4)&gt;&gt;3
 
     As shown above, it has been discovered that the formulas used in the calculation of the phase alignment of the luma sample location  602  and the chroma sample location  604  can be implemented with several additions operations and shift operations per sample, which reduces computational time and complexity than computations used on multiplication operations. 
     Referring now to  FIG. 7 , therein is shown an exemplary method for the preprocessing of statistics using the chrominance square  302  shown in  FIG. 4 . The dotted lines along the Cb axis and Cr axis are defined by the equations (m·Cb max /M) and (n·Cr max /N), respectively. 
     A pixel (Y, Cb, Cr) is in a region R L×M×N  (l, m, n), where 0≦l&lt;L, 0≦m&lt;M, 0≦n&lt;N, if: 
                       l   =     ⌊     Y   ·     L   /     Y   max         ⌋       ,     m   =     ⌊     Cb   ·     M     Cb   max         ⌋       ,   and     ⁢     
     ⁢     n   =     ⌊     Cr   ·     N   /     Cr   max         ⌋               Equation   ⁢           ⁢   D               
For example, the (Y, Cb, Cr) in here is the phase aligned (Y, c b , c r ) or (y, C b , C r ) version of Y, C b , C r  from the first input color space unit  112 . The color space of (Y, Cb, Cr) can be divided into L×M×N regions and each region can be represented as R(l, m, n).
 
     The parameters g 00 , g 01 , g 02 , b 0  of region R(l, m, n) are obtained by linear regression which minimizes a L2 distance of luma between the color reference frame  121  of  FIG. 1  and the downscaled video input  155  of  FIG. 1  and can be represented by:
 
 E   y′ ( l, m, n )=Σ (Y,c     b     ,c     r     )εR(l,m,n) ( y ′−( g   00   Y+g   01   c   b   +g   02   c   r   +b   0 )) 2    Equation A
 
     The parameters g 10 , g 11 , g 12 , b 1  of region R(l, m, n) are obtained by linear regression which minimizes a L2 distance of chroma blue between the color reference frame  121  and the downscaled video input  155  and can be represented by:
 
 E   c     b     ′ ( l, m, n )=Σ (y,C     b     ,C     r     )εR(l,m,n) ( c   b ′−( g   10   y+g   11   C   b   +g   12   C   r   +b   1 )) 2    Equation B
 
     The parameters g 20 , g 21 , g 22 , b 2  of region R(l, m, n) are obtained by linear regression which minimizes a L2 distance of chroma red between the color reference frame  121  of  FIG. 1  and the downscaled video input  155  of  FIG. 1  and can be represented by:
 
 E   c     r     ′ ( l, m, n )=Σ (y,C     b     ,C     r     )εR(l,m,n) ( c   r ′−( g   20   y+g   21   C   b   +g   22   C   r   +b   2 )) 2    Equation B
 
The prediction error of E(l, m, n) in a region R(l, m, n) in the color space is given by the following equation:
 
 E ( l, m, n )= E   y′ ( l, m, n )+ E   c     b     ′ ( l, m, n )+ E   c     r     ′ ( l, m, n )   Equation H
 
     The prediction error of partitioning the color space into L×M×N regions is the sum of the prediction error in each region: 
     
       
         
           
             
               
                 
                   
                     ∑ 
                     
                       l 
                       = 
                       o 
                     
                     
                       L 
                       - 
                       1 
                     
                   
                   ⁢ 
                   
                       
                   
                   ⁢ 
                   
                     
                       ∑ 
                       
                         m 
                         = 
                         0 
                       
                       
                         M 
                         - 
                         1 
                       
                     
                     ⁢ 
                     
                         
                     
                     ⁢ 
                     
                       
                         ∑ 
                         
                           n 
                           = 
                           0 
                         
                         
                           N 
                           - 
                           1 
                         
                       
                       ⁢ 
                       
                           
                       
                       ⁢ 
                       
                         E 
                         ⁡ 
                         
                           ( 
                           
                             l 
                             , 
                             m 
                             , 
                             n 
                           
                           ) 
                         
                       
                     
                   
                 
               
               
                 
                   Equation 
                   ⁢ 
                   
                       
                   
                   ⁢ 
                   G 
                 
               
             
           
         
       
     
     In general, linear regression has a closed form solution using the statistics of the training data. In this case, the statistics for determining the parameters for R(l, m, n) is designated as S(l, m, n) which consists of statistics for Equation A, Equation B, and Equation C, above. 
     Further for example, when fully expanding Equation A, the follow statistics are found to be necessary and sufficient to evaluate Equation A and obtain the corresponding optimal solution:
 
Σ (Y,c     b     ,c     r     )εR(l,m,n)   y′, Σ   (Y,c     b     ,c     r     )εR(l,m,n)   Y, Σ   (Y,c     b     ,c     r     )εR(l,m,n)   c   b , Σ (Y,c     b     ,c     r     )εR(l,m,n)   c   r ,
 
Σ (Y,c     b     ,c     r     )εR(l,m,n)   y′Y, Σ   (Y,c     b     ,c     r     )εR(l,m,n)   y′c   b , Σ (Y,c     b     ,c     r     )εR(l,m,n)   y′c   r , Σ (Y,c     b     ,c     r     )εR(l,m,n)   Y   2 ,
 
Σ (Y,c     b     ,c     r     )εR(l,m,n)   c   b   Y, Σ   (Y,c     b     ,c     r     )εR(l,m,n)   c   r   Y, Σ   (Y,c     b     ,c     r     )εR(l,m,n)   c   b   2 , Σ (Y,c     b     ,c     r     )εR(l,m,n)   c   r   c   b ,
 
Σ (Y,c     b     ,c     r     )εR(l,m,n)   c   r   2 , Σ (Y,c     b     ,c     r     )εR(l,m,n) ( y ′) 2 , Σ (Y,c     b     ,c     r     )εR(l,m,n) 1    Equation E
 
     By fully expanding Equation B and Equation C, the following statistics are found to be necessary and sufficient to evaluate Equations B and C and obtain the corresponding optimal solutions:
 
Σ (Y,c     b     ,c     r     )εR(l,m,n)   c   b ′, Σ (Y,c     b     ,c     r     )εR(l,m,n)   c   r ′, Σ (Y,c     b     ,c     r     )εR(l,m,n)   y, Σ   (Y,c     b     ,c     r     )εR(l,m,n)   C   b ,
 
Σ (Y,c     b     ,c     r     )εR(l,m,n)   C   r , Σ (Y,c     b     ,c     r     )εR(l,m,n)   c   b   ′y, Σ   (Y,c     b     ,c     r     )εR(l,m,n)   C   b   c   b ′, Σ (Y,c     b     ,c     r     )εR(l,m,n)   C   r   c   b ′,
 
Σ (Y,c     b     ,c     r     )εR(l,m,n)   c   r   ′y, Σ   (Y,c     b     ,c     r     )εR(l,m,n)   C   b   c   r ′, Σ (Y,c     b     ,c     r     )εR(l,m,n)   C   r   c   r ′, Σ (Y,c     b     ,c     r     )εR(l,m,n)   y   2 ,
 
Σ (Y,c     b     ,c     r     )εR(l,m,n)   C   b   y, Σ   (Y,c     b     ,c     r     )εR(l,m,n)   C   r   y, Σ   (Y,c     b     ,c     r     )εR(l,m,n)   C   b   2 , Σ (Y,c     b     ,c     r     )εR(l,m,n)   C   b   C   r ,
 
Σ (Y,c     b     ,c     r     )εR(l,m,n)   C   r   2 , Σ (Y,c     b     ,c     r     )εR(l,m,n)   c   b   2 ′, Σ (Y,c     b     ,c     r     )εR(l,m,n)   c   r   2 ′, Σ (Y,c     b     ,c     r     )εR(l,m,n) 1,    Equation F
 
     As shown by the example region marked in  FIG. 7 , statistics for S L×M×N (l, m, n) of the pixels in region R L×M×N (l, m, n), 0≦l&lt;L, 0≦m&lt;M, 0≦n&lt;N are collected as a vector containing the elements in Equation E and Equation F, provided above. 
     Referring now to  FIG. 8 , therein is shown a flow chart of a method of operation of the video transmission system  100  of  FIG. 1  in another embodiment of the present invention. The flow chart can include a detailed view of the mapping parameter determination unit  152  of  FIG. 1 . The prediction unit  152  can include sub-units for determining search points in the search pattern  504  of  FIG. 5  and can be used to select a partition in the search pattern with the least square error. 
     The sub-units of the prediction unit  152  can include a color unit  802 , a collection unit  804 , a calculation unit  805 , and a selection unit  812 . In another embodiment, the control unit  103  of  FIG. 1  can directly perform the operations of the sub-units of the prediction unit  152 . 
     The color unit  802  can divide the color gamut  201  of  FIG. 2  of the three color components of a pixel (Y, Cb, Cr) into L×M×N uniform regions, where it has been found that L, M, N are preferred to be a power of 2 to replace multiplications by shifts. For example, the color unit  802  can generate a 3D lookup table by dividing the color gamut  201  into 8×32×32 regions. The 8×32×32 region partition can include the uniform regions  401  of  FIG. 4 . Further for example, the divisions can include 8×2×2, 8×4×4, 8×8×8, 8×10×10, 8×12×12, and so forth as examples. 
     Each phase aligned pixel of a video frame can fall within or be represented by a location within the 3D lookup table or cube. The color unit  802  can divide the color gamut  201  using the uniform partitioning method shown in  FIGS. 4-5  for collecting statistics in accordance to Equations 1a and 1b. In subsequent operations, the mapping parameter determination unit  152  can merge the statistics of multiple regions of uniform partitions to form the statistics for the non-uniform partition shown in in  FIG. 2-3  for computing the color mapping parameters of the non-uniform partition. 
     The base layer color space, taken from the first input color space unit  112  of  FIG. 1 , can be split into small cubes, for the collection of the statistics of the pixels in the cube for Equation 1. For example,  FIG. 4  and  FIG. 5  describe the method of dividing the color gamut  201  into a 3D table with 8×32×32 uniform regions  302 . The partitions of the chrominance square  302  of  FIG. 3  are used to assign each pixel of a video frame from the base frame parameters  117  of  FIG. 1  to a partition based on the phase aligned pixel value (Y, Cb, Cr) according to Equation D. 
     Further, for a given example of the chroma partition coordinates  250 ,  FIG. 2  and  FIG. 3  describe the method of dividing the color gamut  201  into a 3D lookup table with 8×2×2 non-uniform regions for color mapping of the base layer color to the enhancement layer color. The partitions of the color gamut  201  are used to assign each pixel of a video frame from the base frame parameters  117  of  FIG. 1  to a partition based on the phase aligned pixel value (Y, Cb, Cr) and to map the color to the enhancement layer color by Equations 1a and 1b. 
     The collection unit  804  collects statistics  803  for each of the 8×32×32 uniform regions R(l, m, n) in  401 . The pixel statistics  803  can include statistics in Equation E and Equation F for the 8×32×32 uniform partition. 
     The collection unit  804  is coupled to the color unit  802  and the calculation unit  805 . The collection unit  804  can send the pixel statistics  803  to the calculation unit  805 . 
     The calculation unit  805  can receive the pixel statistics  803  and can obtain average and quantized values for Cb and Cr for deriving search points based on the pixel statistics  803 . The calculation unit  805  can include an averages unit  806 , a quantization unit  808 , and a pattern unit  810 . 
     The averages unit  806  can obtain the average values for Cb and Cr ( Cb ,  Cr ) of the picture from the pixel statistics  803  of the L×M×N regions collected by the collect module  804 . For example, the ( Cb ,  Cr ) values can be computed from the pixel statistics  803  of the 8×32×32 uniform region. The averages unit  806  is coupled to the collection unit  804 . 
     The quantization unit  808  quantizes the average values of Cb and Cr ( Cb ,  Cr ) to determine (ū,  v ) using the method shown in  FIG. 5 . The values for (ū,  v ) can be partition coordinates and can be used to determine the search point  502  of  FIG. 5 . The quantization unit  808  is coupled to the averages unit  806 . 
     The pattern unit  810  derives a list of search points for generating the search pattern  504  of  FIG. 5 . The search pattern  504  is generated using the method shown in  FIG. 5  and shows a collection of points that are less than or equal to one away from the value of the (ū,  v ). The pattern unit  810  is coupled to the quantization unit  808 . 
     The selection unit  812  can search for the most accurate chroma partition coordinate  250  as a search point from the search pattern  504  which minimize a L2 distance between 121 and 155 in Equation G for the L×2×2 partition designated by the chroma coordinate  250 . 
     In order to minimize the L2 distance of the 8×2×2 partition, the selection unit  812  includes a merge block  814  to compute the statistic vector S L×2×2 (l, m, n)  815  according to Equation E and F for the 8×2×2 partition from the statistic vector S L×M×N (l, m, n)  803  collected for the L×M×N uniform partition by vector addition: S L×2×2 (l, m, n)=Σ R     L×M×N     (l,m′,n′)⊂R     L×2×2     (l,m,n) S L×M×N (l, m′,n′). The merged statistics  815  can be sent to a merged prediction block  816 . 
     The selection unit  812  can include the merged prediction block  816 . The merged prediction block  816  can determine merged prediction parameters  817  which minimize the merged prediction error Equation H of each L×2×2 region by the least square method and the corresponding merged prediction error  819 . 
     The selection unit  812  can include an addition block  818  coupled to the merged prediction block  816 . The addition block  818  can add the merged prediction error  819  of every regions in the L×2×2 non-uniform partition to generate a partition prediction error  821  of the L×2×2 regions according to Equation G. The addition block  818  can be coupled to a selection block  820 . 
     The selection unit  812  can include a selection block  820  for receiving the partition prediction error  821  and the corresponding prediction parameters  817  of the L×2×2 non-uniform partition. The selection block  820  can identify the search point within the search pattern  504  with the last square error. The selection unit  812  can output the identified partition and the associated prediction parameters  830 . 
     Referring now to  FIG. 9 , therein is shown an exemplary method determining prediction errors of a partition from L×2×2 non-uniform regions. For illustrative purposes, the partitioning of the chrominance square  302  is 32×32 for M×N. 
     The search point  502  of (u, v) is used to divide the chrominance square  302  into four regions where R L×2×2 (·, m, n), 0≦m&lt;2, 0≦n&lt;2. The statistics of the search point  502  of (u, v) are the statistics of the four regions and are taken from the base frame parameters  117  of  FIG. 1 . 
     Each region R L×2×2  (l, m, n) is a collection of regions from R L×M×N . Statistics of a region R L×2×2 (l, m, n) is a sum of the statistics of regions R L×M×N (l, m′, n′) in R L×2×2 (l, m, n). 
     It has been discovered that the video transmission unit  102  can reduce computation time in video encoding and produce highly accurate color prediction of 8×2×2 and improvements over other compression methods. 
     In summary, the video transmission unit  102  of  FIG. 1  can perform the following:
         1. Divide color space of Y, C b , C r  into L×M×N uniform regions, where L,M,N are preferred to be power of 2.   2. Collect statistics, such as the pixel statistics  803  of  FIG. 8 , needed for least square prediction for each of the L×M×N regions.   3. Obtain the average value ( Cb ,  Cr ) of the whole picture from the statistics of the L×M×N regions.   4. Quantize ( Cb ,  Cr ) to (ū,  v ) as a point in M×N   5. Determine a search pattern for the partition based on (ū,  v ) as a subset of the M×N points.   6. For each search point in the search pattern for the partition:
           a) Add the statistics of the L×M×N uniform regions to form the statistics of the L×2×2 non-uniform regions.   b) Determine the prediction parameters and the prediction error of each of the L×2×2 regions by the least square method.   c) Add the prediction error of the L×2×2 regions in the partition to form the prediction error of the partition.   d) Select the search point in the search pattern with the least square error and the corresponding partition.   
               

     Referring now to  FIG. 10 , therein is shown a flow chart of a method  1000  of operation of a video transmission system in a further embodiment of the present invention. The method  1000  includes: receiving a first video frame from an input device, the first video frame having base frame parameters in a block  1002 ; dividing a color gamut into uniform regions for collecting color data from pixels of the base frame parameters in a block  1004 ; collecting pixel statistics from each of the uniform regions from the base frame parameters in a block  1006 ; determining chroma partition coordinates from the pixel statistics in a block  1008 ; deriving a search pattern of search points based on the chroma partition coordinates in a block  1010 ; and selecting a search point from the search pattern for color mapping of the first video frame in a block  1002 . 
     The resulting method, process, apparatus, device, product, and/or system is straightforward, cost-effective, uncomplicated, highly versatile and effective, can be surprisingly and unobviously implemented by adapting known technologies, and are thus readily suited for efficiently and economically operating video transmission systems fully compatible with conventional encoding and decoding methods or processes and technologies. 
     Another important aspect of the embodiments of the present invention is that it valuably supports and services the historical trend of reducing costs, simplifying systems, and increasing performance. 
     These and other valuable aspects of the embodiments of the present invention consequently further the state of the technology to at least the next level. 
     While the invention has been described in conjunction with a specific best mode, it is to be understood that many alternatives, modifications, and variations will be apparent to those skilled in the art in light of the aforegoing description. Accordingly, it is intended to embrace all such alternatives, modifications, and variations that fall within the scope of the included claims. All matters hithertofore set forth herein or shown in the accompanying drawings are to be interpreted in an illustrative and non-limiting sense.