Patent Publication Number: US-8988452-B2

Title: Color enhancement via gamut expansion

Description:
RELATED APPLICATIONS 
     The present application claims the benefit of U.S. Provisional Application No. 61/715,604 filed Oct. 18, 2012, and titled “COLOR ENHANCEMENT VIA GAMUT EXPANSION”. 
    
    
     BACKGROUND 
     Color enhancement is often a feature in media applications. For example, in video conference applications, where a low-lighting environment is often encountered, it may be useful to improve the overall lighting sensation and color appearance for end-to-end users. Adaptively enhancing the appearance of content originally with less colorfulness, for another instance, may be an appealing feature for web browsing. 
     Conventional approaches to improving color (e.g., saturation enhancement) often include the procedure of transforming the input content into HSV space, applying transfer/mapping functions (e.g., a scaling function on S-axis for saturation enhancement) in HSV space, and transforming the processed content back to its original color space. The computation consumed by these approaches can be high due to non-linear transformation to HSV space. 
     Absolute Colorimetric, one of the four general categories of gamut mapping defined by ICC color management, may commonly be used to match input content to an output device with its gamut different from that of the input content for better visualization. Absolute Colorimetric matches measured value to measured value (e.g., CIE XYZ value) and a 3×3 matrix multiplier is sufficient to operate such matching. 
    
    
     
       BRIEF DESCRIPTION OF THE DRAWINGS 
       The material described herein is illustrated by way of example and not by way of limitation in the accompanying figures. For simplicity and clarity of illustration, elements illustrated in the figures are not necessarily drawn to scale. For example, the dimensions of some elements may be exaggerated relative to other elements for clarity. Further, where considered appropriate, reference labels have been repeated among the figures to indicate corresponding or analogous elements. In the figures: 
         FIG. 1  is an illustrative diagram of an example color management system; 
         FIG. 2  is another illustrative diagram of an example color management system; 
         FIG. 3  is a flow chart illustrating an example color management process; 
         FIG. 4  is an illustrative diagram of an example histogram pattern; 
         FIG. 5  is an illustrative diagram of an example color gamut; 
         FIG. 6  is an illustrative diagram of an example system; 
         FIG. 7  is an illustrative diagram of an example system; and 
         FIG. 8  is a flow chart illustrating another example color management process; all arranged in accordance with at least some implementations of the present disclosure. 
     
    
    
     DETAILED DESCRIPTION 
     One or more embodiments or implementations are now described with reference to the enclosed figures. While specific configurations and arrangements are discussed, it should be understood that this is done for illustrative purposes only. Persons skilled in the relevant art will recognize that other configurations and arrangements may be employed without departing from the spirit and scope of the description. It will be apparent to those skilled in the relevant art that techniques and/or arrangements described herein may also be employed in a variety of other systems and applications other than what is described herein. 
     While the following description sets forth various implementations that may be manifested in architectures such system-on-a-chip (SoC) architectures for example, implementation of the techniques and/or arrangements described herein are not restricted to particular architectures and/or computing systems and may be implemented by any architecture and/or computing system for similar purposes. For instance, various architectures employing, for example, multiple integrated circuit (IC) chips and/or packages, and/or various computing devices and/or consumer electronic (CE) devices such as set top boxes, smart phones, etc., may implement the techniques and/or arrangements described herein. Further, while the following description may set forth numerous specific details such as logic implementations, types and interrelationships of system components, logic partitioning/integration choices, etc., claimed subject matter may be practiced without such specific details. In other instances, some material such as, for example, control structures and full software instruction sequences, may not be shown in detail in order not to obscure the material disclosed herein. 
     The material disclosed herein may be implemented in hardware, firmware, software, or any combination thereof. The material disclosed herein may also be implemented as instructions stored on a machine-readable medium, which may be read and executed by one or more processors. A machine-readable medium may include any medium and/or mechanism for storing or transmitting information in a form readable by a machine (e.g., a computing device). For example, a machine-readable medium may include read only memory (ROM); random access memory (RAM); magnetic disk storage media; optical storage media; flash memory devices; electrical, optical, acoustical or other forms of propagated signals (e.g., carrier waves, infrared signals, digital signals, etc.), and others. 
     References in the specification to “one implementation”, “an implementation”, “an example implementation”, “embodiment”, etc., indicate that the implementation described may include a particular feature, structure, or characteristic, but every implementation may not necessarily include the particular feature, structure, or characteristic. Moreover, such phrases are not necessarily referring to the same implementation. Further, when a particular feature, structure, aspect, element, or characteristic is described in connection with an implementation or embodiment, it is submitted that it is within the knowledge of one skilled in the art to effect such feature, structure, aspect, element, or characteristic in connection with other implementations or embodiments whether or not explicitly described herein. Any feature, structure, aspect, element, or characteristic from an embodiment can be combined with any feature, structure, aspect, element, or characteristic of any other embodiment. 
     Systems, apparatus, articles, and methods are described below including operations for color enhancement via gamut expansion. 
     As will be described in greater detail below, operations for general color enhancement based on a variant of the concept behind Absolute Colorimetric Mapping may be used for color enhancement via gamut expansion. Instead of (or in addition to) treating an input and an output as with two different gamuts, a virtual output gamut may first be generated to derive the 3×3 transform matrix in the Absolute Colorimetric Mapping operation. With actual output gamut being equal to the input one, the color/colorfulness of input content can be manipulated to present better visual quality. 
       FIG. 1  is an illustrative diagram of an example color management system  100 , arranged in accordance with at least some implementations of the present disclosure. In various implementations, color management system  100  may be configured to undertake color management. Further, in various embodiments, color management system  100  may be implemented as part of an image processor, video processor, and/or media processor. Additional components not illustrated here may be included in color management system  100 . 
     In the illustrated example, color management system  100  may include one or more graphics processing units (GPU)  102 , one or more memory stores  112 , and/or one or more display devices  120 . GPU  102  may be communicatively coupled to destination device  120 . Memory stores  112  may be communicatively coupled to GPU  102 . 
     GPU  102  may include processor(s), multicore processors, application specific integrated circuits, programmable logic devices, graphics cards, integrated graphics, general purpose graphics processing unit(s), or the like. In addition, memory stores  112  may be any type of memory such as volatile memory (e.g., Static Random Access Memory (SRAM), Dynamic Random Access Memory (DRAM), etc.) or non-volatile memory (e.g., flash memory, etc.), and so forth. In a non-limiting example, memory stores  112  may be implemented by cache memory. A system agent/memory controller may include any suitable implementation configured to manage system  100  and to control memory stores  112 . Destination device  120  may be configured to present video data. Without being limited by example, destination device  120  may include a display device, a projector, a printer, the like, and/or combinations thereof. In various examples, system  100  may be implemented as a chipset or as a system on a chip. In an example, system  100  may be implemented as a single chip or device having a graphics processor, a quad-core central processing unit, on-board cache, a memory controller input/output (I/O) module (not shown), and a system agent and memory controller configured to manage display function, I/O functions, and direct media interface (DMI) functions. 
     In some examples, Absolute Colorimetric Mapping may be implemented in GPU  102  (e.g., such as an Intel® GenX Graphics Engine module) as one approach to gamut expansion. GPU  102  may include a gamut expansion logic module  108  and/or a gamut mapping logic module  110  for color management. Gamut expansion logic module  108  and/or gamut mapping logic module  110  may implement color management for output to destination device  120 . For example, gamut expansion logic module  108  may be implemented by GPU  102 . Gamut expansion logic module  108  may be configured to detect an extent of colorfulness based at least in part on a histogram associated with an input color space. Additionally or alternatively, gamut expansion logic module  108  may be configured to determine a virtual color space based at least in part on the detected extent of colorfulness associated with the input color space. Gamut mapping logic module  110  may be implemented by GPU  102 . Gamut mapping logic module  110  may be configured to transform input data from the input color space to the virtual color space via absolute colorimetric mapping. 
     In various embodiments, gamut expansion logic module  108  and/or gamut mapping logic module  110  may be implemented in hardware, while software may implement other logic modules. For example, in some embodiments, gamut expansion logic module  108  and/or gamut mapping logic module  110  may be implemented by application-specific integrated circuit (ASIC) logic while other logic modules may be provided by software instructions executed by logic processors. However, the present disclosure is not limited in this regard and gamut expansion logic module  108  and/or gamut mapping logic module  110  may be implemented by any combination of hardware, firmware and/or software. 
     As will be discussed in greater detail below, color management system  100 , as described in  FIG. 1  may be used to perform some or all of the various functions discussed below in connection with  FIGS. 2  and/or  3 . 
       FIG. 2  is a flow chart illustrating an example color management process  200 , arranged in accordance with at least some implementations of the present disclosure. In the illustrated implementation, process  200  may include one or more operations, functions or actions as illustrated by one or more of the blocks  202 ,  204 , and/or  206 . By way of non-limiting example, process  200  will be described herein with reference to example color management system  100  of  FIG. 1 . 
     Process  200  may be utilized as a computer-implemented method for color management. Process  200  may begin at block  202 , “DETECT EXTENT OF COLORFULNESS”, where an extent of colorfulness may be detected. For example, an extent of colorfulness may be detected based at least in part on a histogram associated with an input color space. 
     Processing may continue from operation  202  to operation  204 , “DETERMINE VIRTUAL COLOR SPACE BASED AT LEAST IN PART ON THE DETERMINED EXTENT OF COLORFULNESS”, where a virtual color space may be determined. For example, a virtual color space may be determined based at least in part on the detected extent of colorfulness associated with the input color space. 
     Processing may continue from operation  204  to operation  206 , “TRANSFORM INPUT DATA FROM THE INPUT COLOR SPACE TO THE VIRTUAL COLOR SPACE VIA ABSOLUTE COLOR METRIC MAPPING”, where input data from the input color space may be transformed. For example, input data from the input color space may be transformed to the virtual color space via absolute colorimetric mapping. 
     In operation, process  200  may be utilized as a computer-implemented method for color enhancement via gamut expansion. Process  200  may include gamut expansion for general color enhancement. Process  200  may utilize the operation of Absolute Colorimetric Mapping in (1) a detector of extent of colorfulness, and (2) a content-adaptive virtual color space generator to permit color enhancement with a simple 3×3 matrix multiplication. Accordingly, the virtual color space may be adapted to enhance the colorfulness of the input data as compared to the input color space. 
     Some additional and/or alternative details related to process  200  may be illustrated in one or more examples of implementations discussed in greater detail below with regard to  FIG. 3 . 
       FIG. 3  is a flow chart illustrating an example color management process  300 , arranged in accordance with at least some implementations of the present disclosure. In the illustrated implementation, process  300  may include one or more operations, functions or actions as illustrated by one or more of the blocks  312 ,  314 ,  316 ,  318 ,  320 ,  322 , and/or  324 . By way of non-limiting example, process  300  will be described herein with reference to example color management system  100  of  FIG. 1 . 
     In the illustrated implementation, color management system  100  may include logic modules  306 . For example, logic modules  306 , may include context derivation logic module  108 , gamut mapping logic module  110 , the like, and/or combinations thereof. Although color management system  100 , as shown in  FIG. 3 , may include one particular set of blocks or actions associated with particular modules, these blocks or actions may be associated with different modules than the particular module illustrated here. 
     Process  300  may be utilized as a computer-implemented method for color management. Process  200  may begin at block  312 , “DETECT EXTENT OF COLORFULNESS”, where an extent of colorfulness may be detected. For example, an extent of colorfulness may be detected based at least in part on a histogram associated with an input color space. 
     In some examples, detecting the extent of colorfulness may include determining a scaling factor adapted to adjust component colors. Additionally or alternatively, in some examples, detecting the extent of colorfulness may include determining a true grey point and/or a shifted grey point adapted to correct color shifts. 
     Processing may continue from operation  312  to operation  314 , “DETERMINE IF EXTENT OF COLORFULNESS GOES BEYOND A THRESHOLD VALUE”, where a determination may be made that the extent of colorfulness goes beyond a threshold value. For example, later performance of a determination of the virtual color space and the transformation of the input data from the input color space to the virtual color space may be performed in response to a determination that the extent of colorfulness goes beyond a threshold value. To be more specific, if the extent of colorfulness does not exceed a pre-set threshold, the virtual color space can be configured such that color enhancement effect is resulted. 
     Processing may continue from operation  314  to operation  316 , “DETECT WHITE BALANCE”, where a white balance may be detected. For example, a white balance associated with the input color space may be detected. 
     Processing may continue from operation  316  to operation  318 , “ADJUST WHITE POINT”, where a white point may be adjusted. For example, a white point associated with the virtual color space may be adjusted based at least in part on the detected white balance. 
     Alternatively, in some implementations, instead of performing operations  316 - 318 , operations configured to hold the white point associated with the virtual color space constant without regard to a detected white balance may be utilized. 
     Processing may continue from operation  318  to operation  320 , “DETERMINE VIRTUAL COLOR SPACE BASED AT LEAST IN PART ON THE DETERMINED EXTENT OF COLORFULNESS”, where a virtual color space may be determined. For example, a virtual color space may be determined based at least in part on the detected extent of colorfulness associated with the input color space. 
     As discussed above, detecting the extent of colorfulness may include determining a scaling factor adapted to adjust component colors. In such examples, the determination of the virtual color space may be based at least in part on the scaling factor. 
     Processing may continue from operation  320  to operation  322 , “TRANSFORM INPUT DATA FROM THE INPUT COLOR SPACE TO THE VIRTUAL COLOR SPACE VIA ABSOLUTE COLOR METRIC MAPPING”, where input data from the input color space may be transformed. For example, input data from the input color space may be transformed to the virtual color space via absolute colorimetric mapping. 
     As discussed above, detecting the extent of colorfulness may include determining a true grey point and/or a shifted grey point adapted to correct color shifts. In such examples, the transformation of the input data from the input color space to the virtual color space is based at least in part on the true grey point and/or the shifted grey point. 
     Processing may continue from operation  322  to operation  324 , “TRANSFORM INPUT DATA FROM THE VIRTUAL COLOR SPACE TO A DESTINATION COLOR SPACE VIA ABSOLUTE COLOR METRIC MAPPING”, where input data may be transformed from the virtual color space to a destination color space. For example, transforming input data from the virtual color space to a destination color space associated with a destination device may be performed via absolute colorimetric mapping. 
     In operation, process  300  may be utilized as a computer-implemented method for color enhancement via gamut expansion. Process  300  may include gamut expansion for general color enhancement. Process  300  may utilize the operation of Absolute Colorimetric Mapping in (1) a detector of extent of colorfulness, and (2) a content-adaptive virtual color space generator to permit color enhancement with a simple 3×3 matrix multiplication. Accordingly, the virtual color space may be adapted to enhance the colorfulness of the input data as compared to the input color space. 
     In operation, processes  200  and  300 , as illustrated in  FIGS. 2 and 3 , may be utilized as a computer-implemented method for color enhancement via gamut expansion. Processes  200  and/or  300  presents a general color enhancement module based on a variant of the concept behind Absolute Colorimetric Mapping.  FIG. 1  shows the framework of the proposed module. Processes  200  and/or  300  may transform the Absolute Colorimetric Mapping (which may be implemented in GPU hardware) to a general color enhancement unit by implementing (1) a detector of the extent of colorfulness of input content in its color space, and/or (2) a content-adaptive virtual color space generator. Processes  200  and/or  300  may provide an economic (e.g., via proper configuration of GPU hardware) solution to general color enhancement of media content. 
       FIG. 8  is a flow chart illustrating another illustration of color management system  100 . In the illustrated example, color management system  100  may include gamut expansion basic mode module  802 , inverse gamma module  804 , 3×3 mapping matrix module  806 , gamma module  808 , module inverse gamma  814 , 3×3 mapping matrix module  816 , gamma module  818 , blend factor module  820 , 3×3 mapping matrix derivation module  830 , and/or coefficients module  832 , the like, and/or combinations thereof. In this example, gamut expansion basic mode module  802 , inverse gamma module  804 , 3×3 mapping matrix module  806 , gamma module  808 , module inverse gamma  814 , 3×3 mapping matrix module  816 , gamma module  818 , and blend factor module  820  may be implemented by hardware in the graphics processing unit of system  100 . Similarly, 3×3 mapping matrix derivation module  830  may be implemented by software, while coefficients module  832  may be implemented as hardware state parameters. However, the present disclosure is not limited in this regard, and any of the modules illustrated in  FIG. 8  may be implemented by any combination of hardware, firmware and/or software. 
     In operation, gamut expansion basic mode module  802  may determine if a gamut expansion basic mode is to be used or not. Utilizing module  810 , non-basic gamut expansion mode calculates per-pixel content adaptive blending factor CMw(p) to provide per-pixel blending between the input image and the absolute colorimetric mapping of the input as the output result of gamut expansion module. Basic mode, on the other hand, takes a pre-configured blending factor CMw to provide the global blending between the input image and the absolute colorimetric mapping of the input as the output of gamut expansion module. Module inverse gamma  804 / 814 , 3×3 mapping matrix module  806 / 816 , and gamma module  808 / 818  may perform absolute colorimetric mapping. For non-basic mode, the absolute colorimetric mapping performed by module inverse gamma  804 , 3×3 mapping matrix module  806 , and gamma module  808  may be modified by per-pixel blend factor CMw(p) calculated by module  810 . For basic mode, a pure absolute colorimetric mapping is achievable with module inverse gamma  814 , 3×3 mapping matrix module  816 , gamma module  818 , and setting CMw=1 in module  820 . 
     In operation, 3×3 mapping matrix derivation module  830  may derive coefficients  832  of the 3×3 mapping matrix. Such coefficients  832  may be retrieved from the Original Equipment Manufacturer (OEM). Such coefficients  832  may include values R in , G in , B in , W in , R OUT , G OUT , B OUT , W OUT . 
     In the following, the proposed variant concept of Absolute Colorimetric Mapping may embed the content-adaptive virtual color space generator for general color enhancement. The detector of the extent of colorfulness of input content is then presented in Section 2.2. Finally in Section 2.3, an example experimental result of enhancing colors of a video sequence captured under low-lighting condition with the invention is shown. 
     2.1 A Variation of Absolute Colorimetric Mapping for General Color Enhancement 
     Absolute Colorimetric Mapping is one of the four general categories of gamut mapping defined by ICC color management. Absolute Colorimetric Mapping is commonly used to match input content to an output device with its gamut different from that of the input content for better visualization. 
     Denoting the (linear) color space of input content as RGB in , the (linear) color space of an output device as RGB out , Absolute Colorimetric matches measured value (e.g., primary (R P   in , G P   in , B P   in ) of RGB in  in XYZ space) to measured value (e.g., primary (R P   out , G P   out , B P   out ) of RGB out  in XYZ space). Gamut mapping between in RGB in  and out RGB out  is then achieved through a 3×3 matrix multiplication: 
     
       
         
           
             
               
                 
                   
                     
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     Where M out /M in  in Eq. (1-2) is the 3×3 matrix transforming between linear RGB and CIE XYZ; (XR P   out , YG P   out , ZB P   out ) are the tristimulus values of R P   out  at its maximum value, and similarly for G P   out , B P   out , and (R P   in , G P   in , B P   in ). Note that rather than specifying the tristimulus values for each primary, it is more common to specify the chromaticity coordinates (e.g., (x, y, z), x+y+z=1) of the primary colors and the white point, W, plus the maximum luminance of white Y W . 
     Processes  200  and/or  300  may take the same operation as Eq. (1-1), but transform the concept of mapping two different gamut to the mapping of RGB in  to an intermediate “virtual” color space RGB virtual , where the color of an input can be manipulated to have better visual appearance with the actual output color space being RGB in . That is 
     
       
         
           
             
               
                 
                   
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     Denoting the chromaticity values of (R P   in , G P   in , B P   in ) and W in  (e.g., the white point in RGB in ) as ((x R   P   in , y R   P   in ), (x G   P   in , y G   P   in ), (X B   P   in , y B   P   in )) and (x W     in   , y W     in   ), RGB virtual  may be generated with its primary (R P   virtual , G P   virtual , B P   virtual ) and white point W virtual  illustrated as 
     
       
         
           
             
               
                 
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     Where sf R /sf G /sf B  may represent the scaling factor for stretching/suppressing component colors while Θ and (x Δ , y Δ ) may represent parameters utilized for color correction. The matrix transforming RGB virtual  to CIE XYZ can be derived from Eq. (2-2) as 
     
       
         
           
             
               
                 
                   
                     
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     Note that x+y+z=1 and S(k) represents the kth component of S. The above formula (e.g., Eq. (2-3) and (2-4)) may be generally used for deriving linear RGB to CIE XYZ transform matrix.  FIG. 5  shows an example to illustrate the manipulation of color based on this variant concept of Absolute Colorimetric Mapping. While input content (2-2) shows the original color distribution of the input content, it can be observed from chromaticity distribution (2-4) that the extent of color distribution of the manipulated/processed input has been expanded. Therefore, more vivid color can be expected from the processed content. 
     2.2 A Detector of Extent of Colorfulness 
     The specification of sf R /sf G /sf B , Θ and (x Δ , y Δ ) in Eq. (2-2) may be utilized to generate a proper “virtual” color space for color enhancement based on the operation of Absolute Colorimetric Mapping. Θ and (x Δ , y Δ ) may be utilized as parameters available for correcting color shift of input content, and may be obtained/derived from related modules in a GPU graphics pipeline. For example, the information of true gray point and shifted gray point which can be translated to the information of (x W     virtual   , y W     virtual   ) and (x W     in   , y W     in   ) and here can be obtained from the Automatic White Balance (AWB) module in a GPU. For input without color distortion but with less colorfulness, enhancement may be achieved with proper specification of sf R /sf G /sf B  in Eq. (2-2). To achieve automatic, content-adaptive enhancement, a detector of the extent of color/colorfulness input content might be utilized. Such a detector may work based on statistics derived from U/V histogram which is available from a GPU graphics pipeline: 
     
       
         
           
             
               
                 
                   
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     Where kε(R, G, B), (μ U , μ V ) may represent the mean value of the U/V histogram for 8-bit input and (σ U , σ V ) may represent the standard deviation of the normalized U/V histogram (e.g., normalized to the sum of the histogram). The intuition behind Eq. (3-1) is that a more colorful image should have its U/V histogram more equally distributed and thus σ U /σ V  may serve as a good indicator for the extent of color/colorfulness an input has. Some practical examples are shown in  FIG. 4 . In some examples, σ th  in Eq. (3-1) may represent a threshold value for determining whether an input is “non-colorful” enough for color enhancement to be applied on it. 
     Also, f b   k (μ U −128, μ V −128)*h b   k (|σ U , −σ th |, |σ V −σ th |) would be set to be 1. h a   k  (|σ U , −σ th |, |σ V −σ th |)ε(0,1) is a function proportional to (|σ U , −σ th | and σ V −σ th |. While (μ U −128) and (μ V −128) can provide a rough estimation of majority color input content, f a   k (μ U −128, μ V −128)ε(0,1) in Eq. (3-1) may provide the user a function that may be adjustable to stretch each primary differently according to the major color of the content. 
     Note that the practical usage of Eq. (2-1) and Eq. (3-1) is not limited to color enhancement and thus the range of f a   k (μ U −128, μ V −128), f b   k (μ U −128, μ V −128), h a   k  (|σ U ,−σ th |, |σ V −σ th |), and h b   k  (|σ U ,−σ th |, |σ V −σ th |) are not confined to the ranges described above. 
     2.3 Experimental Results 
     The proposed detector and the enhancement approach based on the variant of Absolute Colorimetric Mapping have been applied to images/video with low quality of colors. For example, a snap shot of video was captured with laptop built-in web-cam under low-lighting condition. (Θ, x Δ , y Δ ) was set to be (0,0,0) and the resulted (sf R /sf G /sf B ) was (0.8,0.55,0.85). An improvement of the color after applying the invention was observed. Note that the enhanced output does not guarantee to be “true color”. To embed color correction in the process, related information (e.g., information from AWB module or information about the lighting of the capture) may be used to derive a proper (Θ, x Δ , y Δ ). 
     While implementation of example processes  200  and  300 , as illustrated in  FIGS. 2 and 3 , may include the undertaking of all blocks shown in the order illustrated, the present disclosure is not limited in this regard and, in various examples, implementation of processes  200  and  300  may include the undertaking only a subset of the blocks shown and/or in a different order than illustrated. 
     In addition, any one or more of the blocks of  FIGS. 2 and 3  may be undertaken in response to instructions provided by one or more computer-program products. Such program products may include signal bearing media providing instructions that, when executed by, for example, a processor, may provide the functionality described herein. The computer-program products may be provided in any form of computer-readable medium. Thus, for example, a processor including one or more processor core(s) may undertake one or more of the blocks shown in  FIGS. 3 and 4  in response to instructions conveyed to the processor by a computer-readable medium. 
     As used in any implementation described herein, the term “module” refers to any combination of software, firmware and/or hardware configured to provide the functionality described herein. The software may be embodied as a software package, code and/or instruction set or instructions, and “hardware”, as used in any implementation described herein, may include, for example, singly or in any combination, hardwired circuitry, programmable circuitry, state machine circuitry, and/or firmware that stores instructions executed by programmable circuitry. The modules may, collectively or individually, be embodied as circuitry that forms part of a larger system, for example, an integrated circuit (IC), system on-chip (SoC), and so forth. 
       FIG. 4  is an illustrative diagram of an example histogram pattern. In the illustrated implementation  400 , images with higher extent of colorfulness in general have U/V histograms more equally distributed. For example, U/V histogram  402  may be associated with an image with more color/colorfulness while U/V histogram  404  may be associated with an image with less colorfulness. Note that the U/V histogram may be normalized to its maximum value for better visualization here. 
       FIG. 5  is an illustrative diagram of an example color gamut. In the illustrated implementation  500 , manipulation of the color with the variant concept of Absolute Colorimetric Mapping is shown for better visualization. In the illustration, input image (2-1), a chromaticity distribution of the input (2-2) where the chromaticity of the primary is shown as the red, green, and blue square respectively, a virtual color space (2-3) with its white point same as the input color space and its primary is shown as star, a chromaticity distribution (2-4) of the manipulated color (e.g.: 
     
       
         
           
             
               
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     and a resultant enhanced input (2-5) is shown. 
       FIG. 6  illustrates an example system  600  in accordance with the present disclosure. In various implementations, system  600  may be a media system although system  600  is not limited to this context. For example, system  600  may be incorporated into a personal computer (PC), laptop computer, ultra-laptop computer, tablet, touch pad, portable computer, handheld computer, palmtop computer, personal digital assistant (PDA), cellular telephone, combination cellular telephone/PDA, television, smart device (e.g., smart phone, smart tablet or smart television), mobile internet device (MID), messaging device, data communication device, and so forth. 
     In various implementations, system  600  includes a platform  602  coupled to a display  620 . Platform  602  may receive content from a content device such as content services device(s)  630  or content delivery device(s)  640  or other similar content sources. A navigation controller  650  including one or more navigation features may be used to interact with, for example, platform  602  and/or display  620 . Each of these components is described in greater detail below. 
     In various implementations, platform  602  may include any combination of a chipset  605 , processor  610 , memory  612 , storage  614 , graphics subsystem  615 , applications  616  and/or radio  618 . Chipset  605  may provide intercommunication among processor  610 , memory  612 , storage  614 , graphics subsystem  615 , applications  616  and/or radio  618 . For example, chipset  605  may include a storage adapter (not depicted) capable of providing intercommunication with storage  614 . 
     Processor  610  may be implemented as a Complex Instruction Set Computer (CISC) or Reduced Instruction Set Computer (RISC) processors; x86 instruction set compatible processors, multi-core, or any other microprocessor or central processing unit (CPU). In various implementations, processor  610  may be dual-core processor(s), dual-core mobile processor(s), and so forth. 
     Memory  612  may be implemented as a volatile memory device such as, but not limited to, a Random Access Memory (RAM), Dynamic Random Access Memory (DRAM), or Static RAM (SRAM). 
     Storage  614  may be implemented as a non-volatile storage device such as, but not limited to, a magnetic disk drive, optical disk drive, tape drive, an internal storage device, an attached storage device, flash memory, battery backed-up SDRAM (synchronous DRAM), and/or a network accessible storage device. In various implementations, storage  614  may include technology to increase the storage performance enhanced protection for valuable digital media when multiple hard drives are included, for example. 
     Graphics subsystem  615  may perform processing of images such as still or video for display. Graphics subsystem  615  may be a graphics processing unit (GPU) or a visual processing unit (VPU), for example. An analog or digital interface may be used to communicatively couple graphics subsystem  615  and display  620 . For example, the interface may be any of a High-Definition Multimedia Interface, Display Port, wireless HDMI, and/or wireless HD compliant techniques. Graphics subsystem  615  may be integrated into processor  610  or chipset  605 . In some implementations, graphics subsystem  615  may be a stand-alone card communicatively coupled to chipset  605 . 
     The graphics and/or video processing techniques described herein may be implemented in various hardware architectures. For example, graphics and/or video functionality may be integrated within a chipset. Alternatively, a discrete graphics and/or video processor may be used. As still another implementation, the graphics and/or video functions may be provided by a general-purpose processor, including a multi-core processor. In further embodiments, the functions may be implemented in a consumer electronics device. 
     Radio  618  may include one or more radios capable of transmitting and receiving signals using various suitable wireless communications techniques. Such techniques may involve communications across one or more wireless networks. Example wireless networks include (but are not limited to) wireless local area networks (WLANs), wireless personal area networks (WPANs), wireless metropolitan area network (WMANs), cellular networks, and satellite networks. In communicating across such networks, radio  618  may operate in accordance with one or more applicable standards in any version. 
     In various implementations, display  620  may include any television type monitor or display. Display  620  may include, for example, a computer display screen, touch screen display, video monitor, television-like device, and/or a television. Display  620  may be digital and/or analog. In various implementations, display  620  may be a holographic display. Also, display  620  may be a transparent surface that may receive a visual projection. Such projections may convey various forms of information, images, and/or objects. For example, such projections may be a visual overlay for a mobile augmented reality (MAR) application. Under the control of one or more software applications  616 , platform  602  may display user interface  622  on display  620 . 
     In various implementations, content services device(s)  630  may be hosted by any national, international and/or independent service and thus accessible to platform  602  via the Internet, for example. Content services device(s)  630  may be coupled to platform  602  and/or to display  620 . Platform  602  and/or content services device(s)  630  may be coupled to a network  660  to communicate (e.g., send and/or receive) media information to and from network  660 . Content delivery device(s)  640  also may be coupled to platform  602  and/or to display  620 . 
     In various implementations, content services device(s)  630  may include a cable television box, personal computer, network, telephone, Internet enabled devices or appliance capable of delivering digital information and/or content, and any other similar device capable of unidirectionally or bidirectionally communicating content between content providers and platform  602  and/display  620 , via network  660  or directly. It will be appreciated that the content may be communicated unidirectionally and/or bidirectionally to and from any one of the components in system  600  and a content provider via network  660 . Examples of content may include any media information including, for example, video, music, medical and gaming information, and so forth. 
     Content services device(s)  630  may receive content such as cable television programming including media information, digital information, and/or other content. Examples of content providers may include any cable or satellite television or radio or Internet content providers. The provided examples are not meant to limit implementations in accordance with the present disclosure in any way. 
     In various implementations, platform  602  may receive control signals from navigation controller  650  having one or more navigation features. The navigation features of controller  650  may be used to interact with user interface  622 , for example. In embodiments, navigation controller  650  may be a pointing device that may be a computer hardware component (specifically, a human interface device) that allows a user to input spatial (e.g., continuous and multi-dimensional) data into a computer. Many systems such as graphical user interfaces (GUI), and televisions and monitors allow the user to control and provide data to the computer or television using physical gestures. 
     Movements of the navigation features of controller  650  may be replicated on a display (e.g., display  620 ) by movements of a pointer, cursor, focus ring, or other visual indicators displayed on the display. For example, under the control of software applications  616 , the navigation features located on navigation controller  650  may be mapped to virtual navigation features displayed on user interface  622 , for example. In embodiments, controller  650  may not be a separate component but may be integrated into platform  602  and/or display  620 . The present disclosure, however, is not limited to the elements or in the context shown or described herein. 
     In various implementations, drivers (not shown) may include technology to enable users to instantly turn on and off platform  602  like a television with the touch of a button after initial boot-up, when enabled, for example. Program logic may allow platform  602  to stream content to media adaptors or other content services device(s)  630  or content delivery device(s)  640  even when the platform is turned “off” In addition, chipset  605  may include hardware and/or software support for (8.1) surround sound audio and/or high definition (7.1) surround sound audio, for example. Drivers may include a graphics driver for integrated graphics platforms. In embodiments, the graphics driver may comprise a peripheral component interconnect (PCI) Express graphics card. 
     In various implementations, any one or more of the components shown in system  600  may be integrated. For example, platform  602  and content services device(s)  630  may be integrated, or platform  602  and content delivery device(s)  640  may be integrated, or platform  602 , content services device(s)  630 , and content delivery device(s)  640  may be integrated, for example. In various embodiments, platform  602  and display  620  may be an integrated unit. Display  620  and content service device(s)  630  may be integrated, or display  620  and content delivery device(s)  640  may be integrated, for example. These examples are not meant to limit the present disclosure. 
     In various embodiments, system  600  may be implemented as a wireless system, a wired system, or a combination of both. When implemented as a wireless system, system  600  may include components and interfaces suitable for communicating over a wireless shared media, such as one or more antennas, transmitters, receivers, transceivers, amplifiers, filters, control logic, and so forth. An example of wireless shared media may include portions of a wireless spectrum, such as the RF spectrum and so forth. When implemented as a wired system, system  600  may include components and interfaces suitable for communicating over wired communications media, such as input/output (I/O) adapters, physical connectors to connect the I/O adapter with a corresponding wired communications medium, a network interface card (NIC), disc controller, video controller, audio controller, and the like. Examples of wired communications media may include a wire, cable, metal leads, printed circuit board (PCB), backplane, switch fabric, semiconductor material, twisted-pair wire, co-axial cable, fiber optics, and so forth. 
     Platform  602  may establish one or more logical or physical channels to communicate information. The information may include media information and control information. Media information may refer to any data representing content meant for a user. Examples of content may include, for example, data from a voice conversation, videoconference, streaming video, electronic mail (“email”) message, voice mail message, alphanumeric symbols, graphics, image, video, text and so forth. Data from a voice conversation may be, for example, speech information, silence periods, background noise, comfort noise, tones and so forth. Control information may refer to any data representing commands, instructions or control words meant for an automated system. For example, control information may be used to route media information through a system, or instruct a node to process the media information in a predetermined manner. The embodiments, however, are not limited to the elements or in the context shown or described in  FIG. 6 . 
     As described above, system  600  may be embodied in varying physical styles or form factors.  FIG. 7  illustrates implementations of a small form factor device  700  in which system  600  may be embodied. In embodiments, for example, device  700  may be implemented as a mobile computing device having wireless capabilities. A mobile computing device may refer to any device having a processing system and a mobile power source or supply, such as one or more batteries, for example. 
     As described above, examples of a mobile computing device may include a personal computer (PC), laptop computer, ultra-laptop computer, tablet, touch pad, portable computer, handheld computer, palmtop computer, personal digital assistant (PDA), cellular telephone, combination cellular telephone/PDA, television, smart device (e.g., smart phone, smart tablet or smart television), mobile internet device (MID), messaging device, data communication device, and so forth. 
     Examples of a mobile computing device also may include computers that are arranged to be worn by a person, such as a wrist computer, finger computer, ring computer, eyeglass computer, belt-clip computer, arm-band computer, shoe computers, clothing computers, and other wearable computers. In various embodiments, for example, a mobile computing device may be implemented as a smart phone capable of executing computer applications, as well as voice communications and/or data communications. Although some embodiments may be described with a mobile computing device implemented as a smart phone by way of example, it may be appreciated that other embodiments may be implemented using other wireless mobile computing devices as well. The embodiments are not limited in this context. 
     As shown in  FIG. 7 , device  700  may include a housing  702 , a display  704 , an input/output (I/O) device  706 , and an antenna  708 . Device  700  also may include navigation features  712 . Display  704  may include any suitable display unit for displaying information appropriate for a mobile computing device. I/O device  706  may include any suitable I/O device for entering information into a mobile computing device. Examples for I/O device  706  may include an alphanumeric keyboard, a numeric keypad, a touch pad, input keys, buttons, switches, rocker switches, microphones, speakers, voice recognition device and software, and so forth. Information also may be entered into device  700  by way of microphone (not shown). Such information may be digitized by a voice recognition device (not shown). The embodiments are not limited in this context. 
     Various embodiments may be implemented using hardware elements, software elements, or a combination of both. Examples of hardware elements may include processors, microprocessors, circuits, circuit elements (e.g., transistors, resistors, capacitors, inductors, and so forth), integrated circuits, application specific integrated circuits (ASIC), programmable logic devices (PLD), digital signal processors (DSP), field programmable gate array (FPGA), logic gates, registers, semiconductor device, chips, microchips, chip sets, and so forth. Examples of software may include software components, programs, applications, computer programs, application programs, system programs, machine programs, operating system software, middleware, firmware, software modules, routines, subroutines, functions, methods, procedures, software interfaces, application program interfaces (API), instruction sets, computing code, computer code, code segments, computer code segments, words, values, symbols, or any combination thereof. Determining whether an embodiment is implemented using hardware elements and/or software elements may vary in accordance with any number of factors, such as desired computational rate, power levels, heat tolerances, processing cycle budget, input data rates, output data rates, memory resources, data bus speeds and other design or performance constraints. 
     One or more aspects of at least one embodiment may be implemented by representative instructions stored on a machine-readable medium which represents various logic within the processor, which when read by a machine causes the machine to fabricate logic to perform the techniques described herein. Such representations, known as “IP cores” may be stored on a tangible, machine readable medium and supplied to various customers or manufacturing facilities to load into the fabrication machines that actually make the logic or processor. 
     While certain features set forth herein have been described with reference to various implementations, this description is not intended to be construed in a limiting sense. Hence, various modifications of the implementations described herein, as well as other implementations, which are apparent to persons skilled in the art to which the present disclosure pertains are deemed to lie within the spirit and scope of the present disclosure. 
     The above examples may include specific combination of features. However, such the above examples are not limited in this regard and, in various implementations, the above examples may include the undertaking only a subset of such features, undertaking a different order of such features, undertaking a different combination of such features, and/or undertaking additional features than those features explicitly listed. For example, all features described with respect to the example methods may be implemented with respect to the example apparatus, the example systems, and/or the example articles, and vice versa.