Patent Publication Number: US-8526729-B2

Title: Image processing apparatus and method, and program

Description:
BACKGROUND OF THE INVENTION 
     1. Field of the Invention 
     The present invention relates to an image processing apparatus and method and a program, and, more particularly, to an image processing apparatus and method and a program, which are suitably used when image gray scale correction is performed. 
     2. Description of the Related Art 
     In the related art, as one image processing technology, without changing a detail component representative of texture, a gray scale correction process of compressing a luminance difference of another component is known (for example, see Japanese Unexamined Patent Application Publication No. 2009-177558). 
     Here, the outline of a gray scale correction process of Japanese Unexamined Patent Application Publication No. 2009-177558 will be described with reference to  FIG. 1 . 
     A luminance region information calculation unit  11  obtains a base value which is a luminance value of a base of a dark luminance side and a light luminance side of a histogram of a luminance value L(nl)(p) of an input image and stores such a base value in a luminance region information memory  12  as luminance region information. A block histogram calculation unit  13  divides the input image into a plurality of blocks in a spatial direction and a luminance direction, and calculates and stores a block histogram representing the number (frequency count) of pixels belonging to each block for each frame in a block histogram memory  14 . A block integration value calculation unit  15  calculates and stores the integration value (total sum) of luminance values of pixels belonging to each block of the above-described input image for each frame in a block integration value memory  16 . 
     A weighted sum-of-products unit  17  calculates a general luminance value Ll(nl)(p) representing approximate lightness of a subject to which each pixel of the input image belongs, based on the luminance value L(nl)(p) of the input image, the block histogram, and the block integration value. A tone curve calculation unit  18  calculates a tone curve for each pixel based on the luminance region information and the general luminance value Ll(nl)(p) and stores the tone curve in a tone curve memory  19 . A mapping unit  20  compresses (corrects) the gray scale of the general luminance value Ll(nl)(p) based on the tone curve and supplies an obtained general luminance value Lcl(nl)(p) to a contrast correction unit  22 . The mapping unit  21  compresses (corrects) the gray scale of the luminance value L(nl)(p) of the input image based on the tone curve and supplies an obtained luminance value Lc(nl)(p) to the contrast correction unit  22 . The contrast correction unit  22  corrects the contrast of the luminance image including the luminance value Lc(nl)(p) based on the general luminance value Lcl(nl)(p), the luminance value Lc(nl)(p) and the tone curve and outputs a luminance value Lu(nl)(p) obtained as the result. 
     SUMMARY OF THE INVENTION 
     However, in the gray scale correction process of Japanese Unexamined Patent Application Publication No. 2009-177558, in order to calculate the general luminance value Ll(nl)(p), the block histogram and the block integration value of one frame are held and thus the amount of memory necessary is increased. 
     In the gray scale correction process of Japanese Unexamined Patent Application Publication No. 2009-177558, in order to calculate the general luminance value Ll(nl)(p) of each pixel, a weighted sum-of-products operation of the block histogram, a weighted sum-of-products operation of the block integration value and a division of such results is performed. In addition, the tone curve is calculated based on the general luminance value Ll(nl)(p) for each pixel. As a result, the computational complexity is increased. 
     It is desirable to perform gray scale correction processing with a small amount of memory and a low computational complexity. 
     According to an embodiment of the present invention, there is provided an image processing apparatus including: a histogram calculation means for calculating a histogram of a luminance value for each spatial block obtained by dividing an input image in a spatial direction; a gray scale correction coefficient calculation means for calculating a gray scale correction coefficient used for correction of the gray scale of a luminance value for each bin of the histogram; and a gray scale correction means for performing gray scale correction of a luminance value of a target pixel based on the gray scale correction coefficient of the bin to which the target pixel of the input image belongs and the gray scale correction coefficient of a bin adjacent to the bin to which the target pixel belongs in the spatial direction and a luminance direction. 
     The gray scale correction coefficient calculation means may include a reference luminance value calculation means for calculating a luminance value obtained by correcting a representative value of the luminance value of the bin in a direction in which a frequency count of the histogram is increased when viewed from the corresponding bin, as a reference luminance value, and a coefficient calculation means for calculating the gray scale correction coefficient for each bin, based on an output luminance value for the reference luminance value of a predetermined gray scale correction characteristic function for outputting an output luminance value, in which the gray scale of an input luminance value is corrected, and the reference luminance value. 
     The reference luminance value calculation means may calculate a barycenter of the luminance value of the corresponding bin as the reference luminance value based on the frequency count of the corresponding bin and the frequency count of the bin adjacent to the corresponding bin in the spatial direction and the luminance direction. 
     The reference luminance value calculation means may calculate a primary differential coefficient of the luminance direction of the histogram and calculate the luminance value obtained by correcting the representative value of the luminance value of the corresponding bin in the direction in which the frequency count of the histogram is increased when viewed from the bin based on the primary differential coefficient, as the reference luminance value. 
     The reference luminance value calculation means may calculate a cumulative histogram function for the histogram and an inverse function of the cumulative histogram function, for each spatial block, calculate a monotonically increasing function passing through the vicinity of the coordinates of the cumulative histogram function for a luminance value in which the histogram becomes a maximum and the minimum value and the maximum value of a luminance value, as a luminance modulation function, for each spatial block, and calculate the reference luminance value by correcting the representative value of the luminance value of the corresponding bin based on the luminance modulation function and the inverse function of the cumulative histogram function. 
     The coefficient calculation means may calculate the gray scale correction characteristic function passing through a minimum value and the coordinates in which a predetermined output luminance value is assigned to a luminance value in which the cumulative count of the histogram becomes a predetermined value and a maximum value of a luminance value. 
     The gray scale correction means may interpolate the gray scale correction coefficient of the luminance value and the position of the target pixel based on the gray scale correction coefficients of the bin to which the target pixel belongs and the bin adjacent to the bin to which the target pixel belongs in the spatial direction and the luminance direction and perform gray scale correction of the luminance value of the target pixel based on the interpolated gray scale correction coefficient. 
     According to another embodiment of the present invention, there is provided an image processing method of an image processing apparatus for performing gray scale correction of a luminance value of an input image, including the steps of: calculating a histogram of a luminance value for each spatial block obtained by dividing the input image in a spatial direction; calculating a gray scale correction coefficient used for correction of the gray scale of a luminance value for each bin of the histogram; and performing gray scale correction of a luminance value of a target pixel based on the gray scale correction coefficient of the bin to which the target pixel of the input image belongs and the gray scale correction coefficient of a bin adjacent to the bin to which the target pixel belongs in the spatial direction and a luminance direction. 
     According to another embodiment of the present invention, there is provided a program for executing, on a computer, a process including the steps of: calculating a histogram of a luminance value for each spatial block obtained by dividing the input image in a spatial direction; calculating a gray scale correction coefficient used for correction of the gray scale of a luminance value for each bin of the histogram; and performing gray scale correction of a luminance value of a target pixel based on the gray scale correction coefficient of the bin to which the target pixel of the input image belongs and the gray scale correction coefficient of a bin adjacent to the bin to which the target pixel belongs in the spatial direction and a luminance direction. 
     According to an embodiment of the present invention, a histogram of a luminance value for each spatial block obtained by dividing the input image in a spatial direction is calculated, a gray scale correction coefficient used for correction of the gray scale of a luminance value for each bin of the histogram is calculated, and gray scale correction of a luminance value of a target pixel is performed based on the gray scale correction coefficient of the bin to which the target pixel of the input image belongs and the gray scale correction coefficient of a bin adjacent to the bin to which the target pixel belongs in the spatial direction and a luminance direction. 
     According to embodiments of the present invention, it is possible to perform gray scale correction processing with a small amount of memory and a low computational complexity. 
    
    
     
       BRIEF DESCRIPTION OF THE DRAWINGS 
         FIG. 1  is a diagram illustrating the outline of a gray scale correction process of the related art; 
         FIG. 2  is a block diagram showing a digital video camera according to a first embodiment of the present invention; 
         FIG. 3  is a block diagram showing a configuration example of a DSP block; 
         FIG. 4  is a diagram showing an arrangement example of pixels of a mosaic image; 
         FIG. 5  is a block diagram showing a configuration example of a gray scale correction processing unit; 
         FIG. 6  is a block diagram showing a configuration example of a luminance gray scale correction unit; 
         FIG. 7  is a block diagram showing a configuration example of a block histogram calculation unit; 
         FIG. 8  is a block diagram showing a configuration example of a gray scale correction coefficient calculation unit; 
         FIG. 9  is a block diagram showing a configuration example of a gray scale correction characteristic determination unit; 
         FIG. 10  is a block diagram showing a configuration example of a reference luminance value calculation unit; 
         FIG. 11  is a block diagram showing a configuration example of a gray scale correction application unit; 
         FIG. 12  is a flowchart illustrating image processing; 
         FIG. 13  is a flowchart illustrating gray scale correction processing; 
         FIG. 14  is a flowchart illustrating image processing for block histogram calculation; 
         FIG. 15  is a flowchart illustrating luminance gray scale correction processing; 
         FIG. 16  is a flowchart illustrating gray scale correction coefficient table preparation processing; 
         FIG. 17  is a graph showing an example of a gray scale correction characteristic function; 
         FIG. 18  is a flowchart illustrating a gray scale correction characteristic determination processing; 
         FIG. 19  is a graph showing an example of a block histogram and a cumulative histogram function; 
         FIG. 20  is a flowchart illustrating reference luminance value calculation processing; 
         FIG. 21  is a block diagram showing a second configuration example of a reference luminance value calculation unit; 
         FIG. 22  is a flowchart illustrating reference luminance value calculation processing; 
         FIG. 23  is a graph showing an example of a primary differential coefficient function; 
         FIG. 24  is a graph showing an example of a luminance correction amount function; 
         FIG. 25  is a block diagram showing a third configuration example of a reference luminance value calculation unit; 
         FIG. 26  is a flowchart illustrating reference luminance value calculation processing; 
         FIG. 27  is a diagram illustrating a method of detecting a peak position of a cumulative histogram function; 
         FIG. 28  is a graph showing an example of a luminance modulation function; 
         FIG. 29  is a diagram illustrating a method of calculating a reference luminance value; 
         FIG. 30  is a diagram showing a second configuration example of a DSP block; 
         FIG. 31  is a diagram showing a second configuration example of a gray scale correction processing unit; 
         FIG. 32  is a diagram showing a third configuration example of a DSP block; 
         FIG. 33  is a diagram showing a third configuration example of a gray scale correction processing unit; 
         FIG. 34  is a diagram illustrating a position of an obtained luminance value; and 
         FIG. 35  is a block diagram showing a configuration example of a computer. 
     
    
    
     DESCRIPTION OF THE PREFERRED EMBODIMENTS 
     Hereinafter, modes (hereinafter, referred to as embodiments) for carrying out the present invention will be described. The description will be given in the following order. 
     1. First Embodiment (Example of Calculating Gray Scale Correction Coefficient by Barycenter of Luminance Value of Each Bin of Block Histogram of Each Spatial Block) 
     2. Second Embodiment (Example of Calculating Gray Scale Correction Coefficient Using Primary Differential Coefficient Function of Block Histogram of Each Spatial Block) 
     3. Third Embodiment (Example of Calculating Gray Scale Correction Coefficient Using Inverse Function of Luminance Modulation Function and Cumulative Histogram Function of Each Spatial Block) 
     4. Modified Example 
     1. First Embodiment 
     Configuration Example of Digital Video Camera 
       FIG. 2  is a block diagram showing a digital video camera according to a first embodiment of the present invention. The digital video camera includes a lens  111 , a diaphragm  112 , an image sensor  113 , a correlated double sampling circuit (CDS)  114 , an Analog/Digital (A/D) converter  115 , a Digital Signal Processor (DSP) block  116 , a timing generator (TG)  117 , a Liquid Crystal Display (LCD) driver  118 , an LCD  119 , a Compression/Decompression (CODEC)  120 , a memory  121 , a Central Processing Unit (CPU)  122 , an input device  123 , and a bus  124 . The DSP block  116  is a block including a signal processor (for example, digital signal processing (DSP)) and a memory such as a Random Access Memory (RAM) for holding image data, a processor executes a predetermined program so as to perform the image processing described below. Hereinafter, the DSP block  116  is referred simply to as the DSP  116 . 
     Incident light from a subject passing through an optical system including the lens  111 , the diaphragm  112  and the like reaches each light receiving element of an image pickup surface of the image sensor  113  and is converted into an electrical signal by photoelectric conversion of the light receiving element. The electrical signal output from the image sensor  113  is digitalized using the A/D converter  115  after removing noise using the correlated double sampling circuit  114  and the digitalized image data is temporarily stored in a memory of the DSP  116 . The timing generator  117  controls a signal processing system including the correlated double sampling circuit  114 , the A/D converter  115  and the DSP  116  such that the image data is input at a constant frame rate. That is, a stream of the image data is supplied to the DSP  116  at a constant frame rate. 
     In addition, the image sensor  113  has a dynamic range wider than that of an image sensor of a general Charge Coupled Device (CCD) or the like and picks up an image from a dark part to a light part of a subject without saturation or noise generation. Accordingly, the A/D converter  115  converts the input electrical signal into image data, the number of gray scales (for example, the number of gray scales which may be expressed by data of about 14 to 16 bits) of which is greater than the number of gray scales (for example, the number of gray scales which may be expressed by data of about 10 to 12 bits) of the general digital video camera. 
     The DSP  116  performs the below-described image processing with respect to the image data such that the dynamic range of the image data becomes, for example, a dynamic range which may be displayed by the LCD  119  and supplies the image-processed image data to the LCD driver  118  or the CODEC  120  as necessary. 
     The LCD driver  118  converts the image data supplied from the DSP  116  into an analog image signal. The LCD driver  118  supplies the analog image signal to the LCD  119  which is a finder of the digital video camera and displays an image based on the image signal. 
     The CODEC  120  encodes the image data supplied from the DSP  116  using a predetermined method and records the encoded image data in the memory  121  including a semiconductor, a magnetic recording medium, a magneto-optical recording medium, an optical recording medium, or the like. 
     The CPU  122  controls the overall processing of the digital video camera based on an input instruction or the like, for example, when a user manipulates the input device  123  including a manipulation button such as a shutter button. The DSP  116 , the timing generator  117 , the CODEC  120 , the memory  121 , the LCD  119 , the CPU  122  and the input device  123  are connected to each other via the bus  124 . 
     [Configuration Example of Function Realized by DSP Block of Digital Camera] 
       FIG. 3  is a block diagram showing a configuration example of a function realized by enabling an internal processor (arithmetic unit) of the DSP  116  to execute a predetermined program. The internal processor of the DSP  116  executes the predetermined program so as to realize a function including a white balance processing unit  151 , a demosaic processing unit  152 , a gray scale correction processing unit  153 , a gamma correction processing unit  154  and a YC transform processing unit  155 . 
     The white balance processing unit  151  acquires a mosaic image which is image data such as a moving image A/D converted by the A/D converter  115 . The mosaic image is an image in which data corresponding to several color components among R, G and B is stored in one pixel and, for example, pixels are arranged along a color array called a Bayer array shown in  FIG. 4 , and is also referred to as RAW data. 
     In  FIG. 4 , one square indicates one pixel and characters R, G and B in the square respectively indicate a pixel of R, a pixel of G and a pixel of B. The pixels of G are arranged in a checkered shape and the pixels of R and the pixels of B are alternately arranged in each row in the remaining parts. 
     Returning to the description of  FIG. 3 , the white balance processing unit  151  applies an appropriate coefficient to a pixel value of each pixel of the acquired mosaic image and adjusts white balance of the mosaic image such that color balance of an achromatic part of a subject actually becomes achromatic. The white balance processing unit  151  supplies the mosaic image, the white balance of which is adjusted, to the demosaic processing unit  152 . Hereinafter, the mosaic image, the white balance of which is adjusted, is denoted by Mw. 
     The demosaic processing unit  152  performs demosaic processing with respect to the mosaic image Mw supplied from the white balance processing unit  151  such that one pixel has all R, G and B components. Therefore, three pieces of image data of an R image, a G image and a B image corresponding to three color components of R, G and B are generated. The demosaic processing unit  152  supplies the three generated pieces of image data of the R image, G image and B image to the gray scale correction processing unit  153 . 
     In addition, hereinafter, three pieces of image data of the R image, the G image and the B image are also referred to as an RGB image. Hereinafter, a pixel value of a pixel position p of a mosaic image is denoted by M(p). In addition, a pixel value of a pixel position p of image data subjected to the demosaic processing is denoted by [Rw(p), Gw(p), Bw(p)]. Here, Rw(p) denotes a pixel value of an R component, Gw(p) denotes a pixel value of a G component, and Bw(p) denotes a pixel value of a B component. 
     The gray scale correction processing unit  153  performs gray scale correction processing with respect to the RGB image and supplies the RGB image subjected to gray scale correction processing to the gamma correction processing unit  154 . In addition, hereinafter, a pixel value of a pixel position p of image data subjected to gray scale correction processing is denoted by [Ru(p), Gu(p), Bu(p)]. Here, Ru(p) denotes a pixel value of an R component, Gu(p) denotes a pixel value of a G component, and Bu(p) denotes a pixel value of a B component. 
     The gamma correction processing unit  154  performs gamma correction with respect to the RGB image subjected to gray scale transformation. The gamma correction processing unit  154  supplies the RGB image subjected to gamma correction to the YC transform processing unit  155 . In addition, hereinafter, a pixel value of a pixel position p of image data subjected to gamma correction is denoted by [Ruγ(p), Guγ(p), Buγ(p)]. Here, Ruγ(p) denotes a pixel value of an R component, Guγ(p) denotes a pixel value of a G component, and Buγ(p) denotes a pixel value of a B component. 
     The YC transform processing unit  155  performs YC matrix processing with respect to the RGB image subjected to gamma correction and performs band limit with respect to a chroma component so as to generate a Y image including a luminance component (Y component) and a C image including a color difference component (Cb or Cr component). The YC transform processing unit  155  supplies the generated Y image and C image to the LCD driver  118  or the CODEC  120  as necessary. In addition, hereinafter, a pixel value of a pixel position p of image data output from the YC transform processing unit  155  is denoted by [Y(p), C(p)]. Here, Y(p) denotes a value of a luminance component of a Y image and C(p) denotes a value of a color difference component of a C image. Hereinafter, the Cb component of the C image is denoted by Cb(p) and the Cr component of the C image is denoted by Cr(p). 
     [Configuration Example of Function of Gray Scale Correction Processing Unit] 
       FIG. 5  is a block diagram showing a configuration example of a function of the gray scale correction processing unit  153 . The gray scale correction processing unit  153  includes a luminance calculation unit  181 , a non-linear transform unit  182 , a luminance gray scale correction unit  183 , a non-linear transform unit  184 - 1  to a non-linear transform unit  184 - 3 , a gray scale correction unit  185 - 1  to a gray scale correction unit  185 - 3 , and a non-linear inverse transform unit  186 - 1  to a non-linear inverse transform unit  186 - 3 . 
     The luminance calculation unit  181  calculates a value (luminance value L(p)) of a luminance component corresponding to a pixel position from the pixel values Rw(p), Gw(p) and Bw(p) of the RGB image supplied from the demosaic processing unit  152  and supplies the value to the non-linear transform unit  182 . The non-linear transform unit  182  non-linearly transforms the luminance value L(p) from the luminance calculation unit  181  and supplies the resultingly obtained luminance value L(nl)(p) and the pixel position p thereof to the luminance gray scale correction unit  183  and the gray scale correction unit  185 - 1  to the gray scale correction unit  185 - 3 . 
     The luminance gray scale correction unit  183  compresses the gray scale of the luminance value L(nl)(p) from the non-linear transform unit  182  so as to perform gray scale correction of the luminance value L(nl)(p) and supplies the luminance value Lu(nl)(p) obtained by gray scale correction to the gray scale correction unit  185 - 1  to the gray scale correction unit  185 - 3 . 
     Each of the non-linear transform unit  184 - 1  to the non-linear transform unit  184 - 3  non-linearly transforms each of the pixel values Rw(p), Gw(p) and Bw(p) of the RGB image supplied from the demosaic processing unit  152 . Each of the non-linear transform unit  184 - 1  to the non-linear transform unit  184 - 3  supplies each of the pixel values R(nl)(p), G(nl)(p) and B(nl)(p) obtained by non-linear transform to the gray scale correction unit  185 - 1  to the gray scale correction unit  185 - 3 . Hereinafter, each of the non-linear transform units  184 - 1  to the non-linear transform unit  184 - 3  are merely referred to as the non-linear transform unit  184  if they are not necessary to be distinguished. 
     Each of the gray scale correction unit  185 - 1  to the gray scale correction unit  185 - 3  corrects the gray scale of each of the pixel values R(nl)(p), G(nl)(p) and B(nl)(p) from the non-linear transform unit  184 - 1  to the non-linear transform unit  184 - 3  using the luminance value L(nl)(p) from the non-linear transform unit  182  and the luminance value Lu(nl)(p) from the luminance gray scale correction unit  183 . Each of the gray scale correction unit  185 - 1  to the gray scale correction unit  185 - 3  supplies each of the pixel values Ru(nl)(p), Gu(nl)(p) and Bu(nl)(p) obtained by gray scale correction to each of the non-linear inverse transform unit  186 - 1  to the non-linear inverse transform unit  186 - 3 . 
     Each of the non-linear inverse transform unit  186 - 1  to the non-linear inverse transform unit  186 - 3  performs non-linear inverse transform which is the inverse transform of the non-linear transform by the non-linear transform unit  184  of the pixel values Ru(nl)(p), Gu(nl)(p) and Bu(nl)(p) from the gray scale correction unit  185 - 1  to the gray scale correction unit  185 - 3 . Each of the non-linear inverse transform unit  186 - 1  to the non-linear inverse transform unit  186 - 3  supplies each of the pixel values Ru(p), Gu(p) and Bu(p) obtained by non-linear inverse transform to the gamma correction processing unit  154 . 
     In addition, the gray scale correction unit  185 - 1  to the gray scale correction unit  185 - 3  are merely referred to the gray scale correction unit  185  if they are not necessary to be distinguished. In addition, hereinafter, the non-linear inverse transform unit  186 - 1  to the non-linear inverse transform unit  186 - 3  are merely referred to as the non-linear inverse transform unit  186  if they are not necessary to be distinguished. 
     [Configuration Example of Function of Luminance Gray Scale Correction Unit] 
       FIG. 6  is a block diagram showing a configuration example of the function of the luminance gray scale correction unit  183  of  FIG. 5 . The luminance gray scale correction unit  183  includes a block histogram calculation unit  201 , a block histogram memory  202 , a gray scale correction coefficient calculation unit  203 , a gray scale correction coefficient table memory  204 , and a gray scale correction application unit  205 . 
     The block histogram calculation unit  201  divides a luminance image corresponding to one frame including the luminance value L(nl)(p) supplied from the non-linear transform unit  182  vertically and horizontally into H by W spatial blocks. The block histogram calculation unit  201  divides a luminance range from the minimum value to the maximum value of a luminance value taken by a luminance image into D bin (luminance ranges) and counts the number (frequency count) of pixels belonging to each bin for each spatial block. A block histogram which is a histogram of a luminance value having D bins is calculated for each spatial block. The block histogram calculation unit  201  stores the calculated block histogram in the block histogram memory  202 . 
     The gray scale correction coefficient calculation unit  203  reads the block histogram of each spatial block from the block histogram memory  202  and a gray scale correction coefficient used for gray scale correction of the luminance value L(nl)(p) for each bin of each block histogram based on the block histogram is calculated. The gray scale correction coefficient calculation unit  203  stores a gray scale correction coefficient table which is a list of gray scale correction coefficient of each bit of each block histogram in the gray scale correction coefficient table memory  204 . 
     The gray scale correction application unit  205  reads the gray scale correction coefficient table from the gray scale correction coefficient table memory  204  and corrects the gray scale of the luminance value L(nl)(p) supplied from the non-linear transform unit  182  based on the gray scale correction coefficient represented by the gray scale correction coefficient table. The gray scale correction application unit  205  supplies the luminance value Lu(nl)(p) obtained by gray scale correction to the gray scale correction unit  185 . 
     In addition, hereinafter, a pixel to be processed, for example, a pixel of a luminance value L(nl)(p) supplied to the luminance gray scale correction unit  183  is also referred to as a target pixel. 
     [Configuration Example of Function of Block Histogram Calculation Unit] 
       FIG. 7  is a block diagram showing a configuration example of the function of the block histogram calculation unit  201  of  FIG. 6 . The block histogram calculation unit  201  includes a bin selection unit  221 , and a counter  222 - 1  to a counter  222 -N. 
     The bin selection unit  221  specifies a spatial block, to which the target pixel belongs, from the position p of the target pixel supplied from non-linear transform unit  182 . In addition, the bin selection unit  221  specifies a bin, to which the target pixel belongs, in the block histogram of the specified spatial block from the luminance value L(nl)(p) of the target pixel. The counter  222 - 1  to the counter  222 -N (N=H×W×D) are provided one by one in respective bins of respective block histograms. The bin selection unit  221  increases the value of the counter corresponding to the specified bin by 1 among the counter  222 - 1  to the counter  222 -N corresponding to the respective bins. 
     Each of the counter  222 - 1  to the counter  222 -N holds the frequency count of the pixel belonging to the bin of the corresponding block histogram. That is, each of the counter  222 - 1  to the counter  222 -N holds the number of pixels within the luminance range, to which the luminance corresponds, among the pixels within the corresponding spatial block. Each of the counter  222 - 1  to the counter  222 -N increases the held value according to an instruction of the bin selection unit  221 . Each of the counter  222 - 1  to the counter  222 -N stores the held value in the block histogram memory  202  as a frequency count of each bin of each block histogram, if the counter of the pixels of the luminance image corresponding to one frame is finished. In addition, hereinafter, the counter  222 - 1  to the counter  222 -N are simply referred to as the counter  222 , if they are not necessary to be distinguished. 
     [Configuration Example of Function of Gray Scale Correction Coefficient Calculation Unit] 
       FIG. 8  is a block diagram showing a configuration example of the function of the gray scale correction coefficient calculation unit  203  of  FIG. 6 . The gray sale correction coefficient calculation unit  203  includes a gray scale correction characteristic determination unit  241 , a gray scale correction characteristic memory  242 , a reference luminance value calculation unit  243  and a coefficient calculation unit  244 . 
     The gray scale correction characteristic determination unit  241  reads the block histogram of each spatial block from the block histogram memory  202 . The gray scale correction characteristics determination unit  241  sets a spline control point for calculating a gray scale correction characteristic function used for gray scale correction of the luminance value for each spatial block based on the block histogram. In addition, the gray scale correction characteristic function is a function defining the characteristics of gray scale correction as described below with respect to  FIG. 17 , and outputs an output luminance value in which the gray scale of an input luminance value is corrected. The gray scale correction characteristic determination unit  241  stores the spline control point for each set spatial block in the gray scale correction characteristic memory  242 . 
     The reference luminance value calculation unit  243  reads the block histogram of each spatial block from the block histogram memory  202 . The reference luminance value calculation unit  243  calculates a reference luminance value for referring to the gray scale correction characteristic function for each bin of the block histogram of each spatial block and supplies the reference luminance value to the coefficient calculation unit  244 . 
     The coefficient calculation unit  244  reads the spline control point of each spatial block from the gray scale correction characteristic memory  242  and calculates the gray scale correction characteristic function of each spatial block based on the read spline control point. The coefficient calculation unit  244  calculates the gray scale correction coefficient for each bin of the block histogram of each spatial block based on the gray scale correction characteristic function of each spatial block and the reference luminance value. The coefficient calculation unit  244  stores the gray scale correction coefficient table which is a list of calculated gray scale correction coefficients in the gray scale correction coefficient table memory  204 . 
     [Configuration Example of Function of Gray Scale Correction Characteristic Determination Unit] 
       FIG. 9  is a block diagram showing a configuration example of the function of the gray scale correction characteristic determination unit  241  of  FIG. 8 . The gray scale correction characteristic determination unit  241  includes a cumulative histogram calculation unit  261 , a light-portion base level calculation unit  262 , an intermediate level calculation unit  263 , a dark-portion base level calculation unit  264  and a spline control point setting unit  265 . 
     The cumulative histogram calculation unit  261  reads the block histogram of each spatial block from the block histogram memory  202  and calculates a cumulative histogram function of each spatial block. The cumulative histogram calculation unit  261  supplies the calculated cumulative histogram function to the light-portion base level calculation unit  262 , the intermediate level calculation unit  263  and the dark-portion base level calculation unit  264 . 
     The light-portion base level calculation unit  262  calculates a light-portion base level which is a luminance value of a base of a light luminance side of the block histogram of each spatial block based on the cumulative histogram function. The light-portion base level calculation unit  262  supplies the calculated light-portion base level to the spline control point setting unit  265 . 
     The intermediate level calculation unit  263  calculates an intermediate level which is a luminance value of a central value of the block histogram of each spatial block based on the cumulative histogram function. The intermediate level calculation unit  263  supplies the calculated intermediate level to the spline control point setting unit  265 . 
     The dark-portion base level calculation unit  264  calculates a dark-portion base level which is a luminance value of a base of a dark luminance side of the block histogram of each spatial block based on the cumulative histogram function. The dark-portion base level calculation unit  264  supplies the calculated dark-portion base level to the spline control point setting unit  265 . 
     The spline control point setting unit  265  sets a spline control point used to calculate the gray scale correction characteristic function of each spatial block based on the light-portion base level, the intermediate level and the dark-portion base level and stores the spline control point in the gray scale correction characteristic memory  242 . 
     [Configuration Example of Function of Reference Luminance Value Calculation Unit] 
       FIG. 10  is a block diagram showing a configuration example of the function of the reference luminance value calculation unit  243  of  FIG. 8 . The reference luminance value calculation unit  243  includes a barycenter calculation unit  281 . 
     The barycenter calculation unit  281  reads the block histogram of each spatial block from the block histogram memory  202 . The barycenter calculation unit  281  calculates the barycenter of the luminance value of each bin of the block histogram of each spatial block as a reference luminance value Lg(nl)(p) of each bin and supplies the baryenter to the coefficient calculation unit  244 . 
     [Configuration Example of Function of Gray Scale Correction Application Unit] 
       FIG. 11  is a block diagram showing a configuration example of the function of the gray scale correction application unit  205  of  FIG. 6 . The gray scale correction application unit  205  includes a coefficient interpolation unit  301  and a coefficient application unit  302 . 
     The coefficient interpolation unit  301  reads the gray scale correction coefficient table from the gray scale correction coefficient table memory  204 . The coefficient interpolation unit  301  calculates the gray scale correction coefficient of the luminance value L(nl)(p) of the target pixel supplied from the non-linear transform unit  182  and the position p by an interpolation processing and supplies the gray scale correction coefficient to the coefficient application unit  302 . 
     The coefficient application unit  302  applies the gray scale correction coefficient calculated by the coefficient interpolation unit  301  to the luminance value L(nl)(p) supplied from the non-linear transform unit  182  so as to calculate a luminance value Lu(nl)(p), the gray scale of which is corrected. The coefficient application unit  302  supplies the calculated luminance value Lu(nl)(p) to the gray scale correction unit  185 . 
     [Description of Image Processing of DSP Block of Digital Camera] 
     Next, image processing executed by the DSP  116  will be described with reference to the flowchart of  FIG. 12 . In addition, this processing begins, for example, by photographing with the digital video camera of  FIG. 2  and begins when supply of a stream of image data (mosaic image) from the A/D converter  115  to the DSP  116  begins. The image data supplied to the DSP  116  is sequentially stored in an internal memory (not shown) of the DSP  116 . 
     In step S 11 , the white balance processing unit  151  reads a mosaic image. Specifically, the white balance processing unit  151  reads the mosaic image of a beginning frame stored in the internal memory (not shown) of the DSP  116 . 
     In step S 12 , the white balance processing unit  151  adjust white balance of the acquired mosaic image and supplies the adjusted mosaic image to the demosaic processing unit  152 . 
     In step S 13 , the demosaic processing unit  152  performs demosaic processing. That is, the demosaic processing unit  152  performs demosaic processing with respect to the mosaic image from the white balance processing unit  151  and generates an RGB image. 
     In step S 14 , the gray scale correction processing unit  153  performs gray scale correction processing and corrects the gray scale of the RGB image from the demosaic processing unit  152 . The gray scale correction processing unit  153  supplies the RGB image, the gray scale of which is corrected, to the gamma correction processing unit  154 . The details of the gray scale correction processing will be described below. 
     In step S 15 , the gamma correction processing unit  154  performs gamma correction with respect to the RGB image from the gray scale correction processing unit  153  and supplies the RGB image to the YC transform processing unit  155 . 
     In step S 16 , the YC transform processing unit  155  performs YC transform processing. For example, the YC transform processing unit  155  performs YC matrix processing with respect to the RGB image from the gamma correction processing unit  154  and performs band limit with respect to the chroma component so as to generate the Y image and the C image from the RGB image. In step S 17 , the YC transform processing unit  155  outputs the Y image and the C image. For example, the YC transform processing unit  155  outputs the Y image and the C image to the LCD driver  118  or the CODEC  120  as necessary. 
     In step S 18 , the white balance processing unit  151  determines whether or not a subsequent frame is present. For example, if a mosaic image of a subsequent frame is accumulated in the internal memory (not shown) of the DSP  116 , it is determined that the subsequent frame is present. 
     If it is determined that the subsequent frame is present in step S 18 , the processing returns to step S 11 , and the mosaic image of the next frame to be processed is read. In contrast, if it is determined that the subsequent frame is not present in step S 18 , the image processing is finished. 
     [Description of Gray Scale Correction Processing] 
     Next, the gray scale correction processing corresponding to the processing of step S 14  of  FIG. 12  will be described with reference to the flowchart of  FIG. 13 . 
     In step S 41 , the coefficient interpolation unit  301  ( FIG. 11 ) of the gray scale correction application unit  205  reads the gray scale correction coefficient table from the gray scale correction coefficient table memory  204 . In addition, details of the gray scale correction coefficient table will be described below. 
     In step S 42 , the gray scale correction processing unit  153  reads the pixel value and the pixel position of the target pixel of the RGB image from the demosaic processing unit  152 . That is, the gray scale correction processing unit  153  selects one target pixel from among pixels which are not still processed of the RGB image. The luminance calculation unit  181  of the gray scale correction processing unit  153  and the non-linear transform unit  184 - 1  to the non-linear transform unit  184 - 3  read the pixel value Rw(p) of the R component, the pixel value Gw(p) of the G component and the pixel value Bw(p) of the B component of the selected target pixel and the pixel position p thereof from the demosaic processing unit  152 . 
     In step S 43 , the luminance calculation unit  181  calculates the luminance value L(p) of the target pixel based on the read pixel values and supplies the luminance value to the non-linear transform unit  182 . For example, the luminance calculation unit  181  multiplies the read pixel value Rw(p) to pixel value Bw(p) by a predetermined coefficient so as to obtain a linear sum and sets the linear sum to a luminance value or sets a maximum value of the pixel value Rw(p) to pixel value Bw(p) to a luminance value. 
     In step S 44 , the non-linear transform unit  182  non-linearly transforms the luminance value L(p) from the luminance calculation unit  181 . For example, non-linear transform unit  182  non-linearly transforms the luminance value L(p) using a function having an upwardly convex monotonically increasing characteristic such as a multiplication characteristic or a logarithmic characteristic by an index less than 1. The non-linear transform unit  182  supplies the luminance value L(nl)(p) obtained by transform and the pixel position p to the bin selection unit  221  ( FIG. 7 ) of the block histogram calculation unit  201  and the coefficient interpolation unit  301  and the coefficient application unit  302  ( FIG. 11 ) of the gray scale correction application unit  205 . 
     In step S 45 , the block histogram calculation unit  201  performs pixel processing for calculating the block histogram. Although the pixel processing for calculating the block histogram will be described below, the number of pixels belonging to each bin of the block histogram of each spatial block is counted by this processing. 
     In step S 46 , the luminance gray scale correction unit  183  performs luminance gray scale correction processing. Although details of luminance gray scale correction processing will be described below, the luminance value Lu(nl)(p) in which the gray scale of the luminance value L(nl)(p) of the target pixel is corrected is calculated and supplied to the gray scale correction unit  85  by this processing. 
     In step S 47 , the non-linear transform unit  184  non-linearly transforms the pixel values of the target pixel and supplies the pixel values to the gray scale correction unit  185 . That is, the non-linear transform unit  184 - 1  to the non-linear transform unit  184 - 3  performs the same non-linear transform as the non-linear transform performed in the process of the step S 44  with respect to the pixel values Rw(p), Gw(p) and Bw(p) of the RGB image. 
     In step S 48 , the gray scale correction unit  185 - 1  to the gray scale correction unit  185 - 3  correct the gray scales of the pixel values from the non-linear transform unit  184  using the luminance value L(nl)(p) from the non-linear transform unit  182  and the luminance value Lu(nl)(p) from the luminance gray scale correction unit  183 . The gray scale correction unit  185  supplies the pixel values, the gray scales of which are corrected, to the non-linear inverse transform unit  186 . 
     For example, the gray scale correction unit  185  multiplies a ratio of the luminance value L(nl)(p) to the luminance value Lu(nl)(p), the gray scale of which is corrected, by the pixel value of each color component. In greater detail, for example, the gray scale correction unit  185 - 1  obtains the pixel value Ru(nl)(p), the gray scale of which is corrected, by calculating Equation (1). 
     
       
         
           
             
               
                 
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     In Equation (1), the ratio of luminance values before and after gray scale correction, that is, the value obtained by dividing the luminance value Lu(nl)(p), the gray scale of which is corrected, by the luminance value L(nl)(p) before gray scale correction, is multiplied by the non-linearly transformed pixel value R(nl)(p). Even in the gray scale correction unit  185 - 2  and the gray scale correction unit  185 - 3 , the same calculation as the above-described Equation (1) is performed such that the gray scale of the pixel value is corrected. 
     In the case where the non-linear transform performed by the non-linear transform unit  182  and the non-linear transform unit  184  is a logarithmic characteristic, the gray scale of the pixel value may be corrected by calculation of Equation (2).
 
Equation 2
 
 Ru ( nl )( p )=( R ( nl )( p )− L ( nl )( p ))+ Lu ( nl )( p )  (2)
 
     In Equation (2), a difference value between the luminance value Lu(nl)(p), the gray scale of which is corrected, and the luminance value L(nl)(p) before gray scale correction is added to the pixel value R(nl)(p) so as to obtain the pixel value Ru(nl)(p), the gray scale of which is corrected. 
     In step S 49 , the non-linear inverse transform unit  186 - 1  to the non-linear inverse transform unit  186 - 3  perform non-linear inverse transform which is the inverse transform of the non-linear transform of the non-linear transform unit  184 , with respect to the pixel values from the gray scale correction unit  185 . 
     In step S 50 , the non-linear inverse transform unit  186 - 1  to the non-linear inverse transform unit  186 - 3  outputs the pixel values Ru(p), Gu(p) and Bu(p) obtained by non-linear inverse transform to the gamma correction processing unit  154 . 
     In step S 51 , the gray scale correction processing unit  153  determines whether or not all pixels of the RGB image of the frame to be processed are processed. In step S 51 , if it is determined that all pixels are not still processed in step S 51 , the processing returns to step S 42  and the above-described processing is repeated. 
     In contrast, if it is determined that all pixels are processed in step S 51 , the processing progresses to step S 52 . 
     In step S 52 , the gray scale correction coefficient calculation unit  203  ( FIG. 6 ) performs gray scale correction coefficient table preparation processing. Although details of gray scale correction coefficient table preparation processing will be described below, the gray scale correction coefficient table is prepared and stored in the gray scale correction coefficient table memory  204  by this processing. 
     Thereafter, the gray scale correction processing is finished and the processing progresses to step S 15  of  FIG. 12 . 
     In the gray scale correction processing, the gray scale correction coefficient table is prepared based on the luminance image corresponding to one frame and is stored in the gray scale correction coefficient table memory  204 . The luminance gray scale correction unit  183  performs gray scale correction processing using the gray scale correction coefficient table prepared from a frame in time prior to a frame, which is being currently processed. Accordingly, even when an image to be processed is a moving image, it is possible to perform gray scale correction processing with a large operator size by a low computational complexity and a small amount of working memory, without scanning all pixels of an image two times. 
     [Description of Pixel Processing for Calculating Block Histogram] 
     Next, pixel processing for calculating the block histogram, which corresponds to the processing of step S 45  of  FIG. 13 , will be described with reference to the flowchart of  FIG. 14 . 
     In step S 101 , the bin selection unit  221  ( FIG. 7 ) specifies the bin to which the target pixel belongs. Specifically, the bin selection unit  221  specifies the spatial block, to which the target pixel belong, from the position p of the target pixel. In addition, the bin selection unit  221  specifies the bin, to which the target pixel belongs, in the block histogram of the spatial block, to which the target pixel belongs, from the luminance value L(nl)(p) of the target pixel. 
     In step S 102 , the bin selection unit  221  increases the value of the counter  222  corresponding to the specified bin by one. 
     Thereafter, the pixel processing for calculating the block histogram is finished and the processing progresses to step S 46  of  FIG. 13 . 
     The pixels of the luminance image including the luminance value L(nl)(p) are classified in a spatial direction and a luminance direction and the number of pixels is counted such that the block histogram of each spatial block is generated. 
     [Description of Luminance Gray Scale Correction Processing] 
     Next, the luminance gray scale correction processing of step S 46  of  FIG. 13  will be described with reference to the flowchart of  FIG. 15 . 
     In step S 121 , the coefficient interpolation unit  301  ( FIG. 11 ) reads the gray scale correction coefficient of the bin, to which the target pixel belongs, and the adjacent bin. For example, the coefficient interpolation unit  301  selects the bin within a predetermined luminance range from the bin, to which the target pixel belongs, as the adjacent bin, among the bin of the block histogram of the spatial block, to which the target pixel belongs, and the bin of the block histogram of the spatial block within a predetermined range from the spatial block, to which the target pixel belongs. That is, the coefficient interpolation unit  301  selects the bin in which the distance from the bin, to which the target pixel belongs, in both the spatial direction and the luminance direction is within a predetermined range as the adjacent bin. The coefficient interpolation unit  301  reads the gray scale correction coefficient of the bin, to which the target pixel belongs, and the adjacent bin from the gray scale correction coefficient table. 
     In step S 122 , the coefficient interpolation unit  301  calculates the gray scale correction coefficient of the target pixel by an interpolation process. That is, the coefficient interpolation unit  301  performs interpolation of the gray scale correction coefficient of the spatial direction and the luminance direction by an appropriate interpolation processing based on the gray scale correction coefficient of the bin, to which the target pixel belongs, and the adjacent bin and performs interpolation of the gray scale correction coefficient of the luminance value L(nl)(p) of the target pixel and the position p. At this time, for example, an interpolation processing used for interpolation of same-interval data, such as linear interpolation or third spline interpolation is performed. The coefficient interpolation unit  301  supplies the calculated gray scale correction coefficient to the coefficient application unit  302 . 
     In step S 123 , the coefficient application unit  302  applies the gray scale correction coefficient calculated by the coefficient interpolation unit  301  to the luminance value L(nl)(p) of the target pixel. For example, the coefficient application unit  302  multiplies the luminance value L(nl)(p) of the target pixel by the gray scale correction coefficient calculated the coefficient interpolation unit  301  so as to calculate the luminance value Lu(nl)(p), the gray scale of which is corrected. Alternatively, if the non-linear transform performed by the non-linear transform unit  182  is a logarithmic characteristic, the coefficient application unit  302  adds the gray scale correction coefficient calculated by the coefficient interpolation unit  301  to the luminance value L(nl)(p) of the target pixel so as to calculate the luminance value Lu(nl)(p), the gray scale of which is corrected. 
     In step S 124 , the coefficient application unit  302  outputs the luminance value Lu(nl)(p) after gray scale correction to the gray scale correction unit  185  of  FIG. 5 . 
     Thereafter, the luminance gray scale correction processing is finished and the processing progresses to step S 47  of  FIG. 13 . 
     [Description of Gray Scale Correction Coefficient Table Preparation Processing] 
     Next, the gray scale correction coefficient table preparation processing of step S 52  of  FIG. 13  will be described with reference to the flowchart of  FIG. 16 . 
     In step S 141 , the gray scale correction coefficient calculation unit  203  ( FIG. 8 ) selects a target spatial block. That is, the gray scale correction coefficient calculation unit  203  selects one of spatial blocks, in which the gray scale correction coefficient is not still calculated, as the target spatial block. 
     In step S 142 , the gray scale correction characteristic determination unit  241  performs the gray scale correction characteristic determination processing. In addition, although details of the gray scale correction characteristic determination processing will be described below, for example, spline control points P 1  to P 5  of the gray scale correction characteristic function  351  shown in  FIG. 17  are set by this processing. The horizontal axis of  FIG. 17  denotes an input luminance and the vertical axis thereof denotes an output luminance. 
     In step S 143 , the reference luminance value calculation unit  243  selects a target bin. That is, the reference luminance value calculation unit  243  selects one of the bins, in which the gray scale correction coefficient is not calculated, among the bins of the block histogram of the target spatial block, as the target bin. 
     In step S 144 , the reference luminance value calculation unit  243  performs reference luminance value calculation processing. In addition, although details of the reference luminance value calculation processing will be described below, through this processing, the reference luminance value Lg(nl)(p) used to refer to the gray scale correction characteristic function is calculated and is supplied to the coefficient calculation unit  244 , in order to calculate the gray scale correction coefficient of the target bin. 
     In step S 145 , the coefficient calculation unit  244  calculates the reference result of the gray scale correction characteristic function. Specifically, the coefficient calculation unit  244  reads the spline control point P 1  to P 5  ( FIG. 17 ) of the gray scale correction characteristic function of the target bin from the gray scale correction characteristic memory  242 . The coefficient calculation unit  244  calculates the gray scale correction characteristic function passing through the spline control points P 1  to P 5 , for example, by a predetermined interpolation processing such as third spline interpolation. The coefficient calculation unit  244  calculates an output luminance value when the reference luminance value Lg(nl)(p) calculated by the reference luminance value calculation unit  243  is applied to the gray scale correction characteristic function as an input luminance value as the reference result of the gray scale correction characteristic function. 
     In step S 146 , the coefficient calculation unit  244  calculates the gray scale correction coefficient. For example, the coefficient calculation unit  244  calculates a ratio (reference result reference luminance value Lg(nl)(p)) of the reference result to the reference luminance value Lg(nl)(p) of the gray scale correction characteristic function to the reference as the gray scale correction coefficient of the target bin. Alternatively, if the non-linear transform performed by the non-linear transform unit  182  is a logarithmic characteristic, the coefficient calculation unit  244  calculates the difference between the reference result and the reference luminance value Lg(nl)(p) of the gray scale correction characteristic function as the gray scale correction coefficient of the target bin. 
     In step S 147 , the coefficient calculation unit  244  stores the gray scale correction coefficient in the memory. That is, the coefficient calculation unit  244  stores the calculated gray scale correction coefficient in the gray scale correction coefficient table memory  204  in association with the position of the spatial direction and the luminance direction of the target bin. 
     In step S 148 , the reference luminance value calculation unit  243  determines whether or not all bins of the block histogram of the target spatial block have been processed. The reference luminance value calculation unit  243  determines that all bins of the block histogram of the target spatial block are not still processed if the bin in which the gray scale correction coefficient is not still calculated is remained and the processing returns to step S 143 . 
     Thereafter, in step S 148 , the processing of steps S 143  to S 148  is repeatedly executed until it is determined that all bins of the block histogram of the target spatial block have been processed. The gray scale correction coefficient of each bin of the block histogram of the target spatial block is calculated and stored in the gray scale correction coefficient table memory  204 . 
     In contrast, in step S 148 , if it is determined that all bins of the block histogram of the target spatial block have been processed, the processing progresses to step S 149 . 
     In step S 149 , the gray scale correction coefficient calculation unit  203  determines whether or not all spatial blocks have been processed. The gray scale correction coefficient calculation unit  203  determines that all spatial blocks are not still processed if the spatial block in which the gray scale correction coefficient is not still calculated is remained and the processing returns to step S 141 . 
     Thereafter, in step S 149 , the processing of steps S 141  to S 149  is repeatedly executed until it is determined that all spatial blocks have been processed. The gray scale correction coefficient of each bin of the block histogram of each spatial block is calculated and the gray scale correction coefficient table in which each bin of each block histogram is associated with the gray scale correction coefficient is stored in the gray scale correction coefficient table memory  204 . 
     In contrast, if it is determined that all spatial blocks have been processed in step S 149 , the gray scale correction coefficient table preparation processing is finished and the processing progresses to step S 15  of  FIG. 12 . 
     [Description of Gray Scale Correction Characteristic Determination Processing] 
     Next, the gray scale correction characteristic determination processing of step S 142  of  FIG. 16  will be described with reference to the flowchart of  FIG. 18 . 
     In step S 161 , the cumulative histogram calculation unit  261  ( FIG. 9 ) calculates the cumulative histogram function of the target spatial block. Specifically, the cumulative histogram calculation unit  261  reads the block histogram of the target spatial block from the block histogram memory  202 . The cumulative histogram calculation unit  261  sequentially adds the frequency count of each bin of the read block histogram from a side in which the luminance is small so as to calculate the cumulative histogram function. The cumulative histogram calculation unit  261  supplies the calculated cumulative histogram function to the light-portion base level calculation unit  262 , the intermediate level calculation unit  263  and the dark-portion base level calculation  264 . 
       FIG. 19  shows a detailed example of the cumulative histogram function. The horizontal axis of  FIG. 19  shows a luminance and the vertical axis denotes a frequency count or a cumulative frequency. A cumulative histogram function  362  of the block histogram  361  shown by a plurality of histograms is shown. 
     First, a point in which the value of the luminance direction is equal to a minimum value (hereinafter, referred to as a minimum level) of the luminance value of the luminance image and the value of the cumulative frequency direction becomes 0 becomes a start point of the cumulative histogram function  362 . For each bin, a point in which the value of the luminance direction becomes a maximum value of the luminance range of each bin and the value of the cumulative frequency direction becomes the cumulative value (cumulative frequency) of the frequency count from the bin having a minimum luminance range to the corresponding bin is set. Accordingly, a point in which the value of the luminance direction is equal to a maximum value (hereinafter, referred to as a maximum level) of the luminance value of the luminance image and the value of the cumulative frequency direction becomes equal to the number of pixels within the spatial block (100% level) becomes an end point of the cumulative histogram function  362 . By connecting the start point and the point set sequentially for each bin, the cumulative histogram function  362  is generated. 
     In step S 162 , the light-portion base level calculation unit  262 , the intermediate level calculation unit  263  and the dark-portion base level calculation unit  264  calculate the light-portion base level, the intermediate level and the dark-portion base level, respectively. Specifically, the light-portion base level calculation unit  262  calculates, for example, a luminance value in which the cumulative frequency of the cumulative histogram function becomes 95% of the number of pixels within the spatial block and supplies the calculated luminance value to the spline control point setting unit  265  as the light-portion base level. In addition, the intermediate level calculation unit  263  calculates, for example, a luminance value in which the cumulative frequency of the cumulative histogram function becomes 50% of the number of pixels within the spatial block and supplies the calculated luminance value to the spline control point setting unit  265  as the intermediate level. In addition, the dark-portion base level calculation unit  264  calculates, for example, a luminance value in which the cumulative frequency of the cumulative histogram function becomes 5% of the number of pixels within the spatial block and supplies the calculated luminance value to the spline control point setting unit  265  as the dark-portion base level. 
     In step S 163 , the spline control point setting unit  265  sets the spline control points of the gray scale correction characteristic function. Now, the example of the method of setting the spline control point will be described with reference to  FIG. 17 . 
     As described above, in  FIG. 17 , five spline control points P 1  to P 5  are set. The spline control point P 1  is a point in which the value of the input luminance direction becomes the minimum value (minimum level) of the luminance value of the luminance image and the value of output luminance direction becomes the minimum value (hereinafter, referred to as the predetermined minimum level) of the luminance value of the luminance image after gray scale transform. The spline control point P 2  is a point in which the value of the input luminance direction becomes a dark-portion base level calculated by the dark-portion base level calculation unit  264  and the value of output luminance direction becomes a predetermined dark-portion base level. The spline control point P 3  is a point in which the value of the input luminance direction becomes an intermediate level calculated by the intermediate level calculation unit  263  and the value of output luminance direction becomes a predetermined intermediate level. The spline control point P 4  is a point in which the value of the input luminance direction becomes a light-portion base level calculated by the light-portion base level calculation unit  262  and the value of output luminance direction becomes a predetermined light-portion base level. The spline control point P 5  is a point in which the value of the input luminance direction becomes a maximum value (maximum level) of the luminance value of the luminance image and the value of output luminance direction becomes a maximum value (hereinafter, referred to as a predetermined maximum level) of the luminance value of the luminance image after gray scale transform. 
     In addition, the predetermined minimum level, the predetermined dark-portion base level, the predetermined intermediate level, the predetermined light-portion base level, the predetermined maximum level are values which are set in advance from the view point of which of the five luminance levels, that is, the minimum level, the dark-portion base level, the intermediate level, the light-portion base level and the maximum level, is allocated after gray scale correction to the luminance level. 
     In step S 164 , the spline control point setting unit  265  stores the spline control points in the memory. That is, the spline control point setting unit  265  stores the coordinates of the set spline control points P 1  to P 5  of the target spatial block in the gray scale correction characteristic memory  242 . 
     Thereafter, the gray scale correction characteristic determination processing is finished and the processing progresses to step S 143  of  FIG. 16 . 
     [Description of Reference Luminance Value Calculation Processing] 
     Next, details of the reference luminance value calculation processing of step S 144  of  FIG. 16  will be described with reference to the flowchart of  FIG. 20 . 
     In step S 181 , the barycenter calculation unit  281  ( FIG. 10 ) reads the frequency count of the target bin and the adjacent bin. Specifically, the barycenter calculation unit  281  selects the bin other than the target bin and each bin of the block histogram of the spatial block within the predetermined range from the target spatial block from among the bins of the block histogram of the target spatial block, as the adjacent bin (hereinafter, referred to as the adjacent bin) of the target bin. That is, the coefficient interpolation unit  301  selects each bin (excluding the target bin) of the block histogram of the target spatial block and the adjacent spatial block as the adjacent bin. 
     In addition, the coefficient interpolation unit  301  reads the frequency count of the target bin and the adjacent bit from the block histogram memory  102 . 
     In step S 182 , the barycenter calculation unit  281  calculates the barycenter of the luminance value of the target bin. Specifically, the barycenter calculation unit  281  calculates the weighted average of the luminance values of the pixels belonging to the target bin and the adjacent bin based on the representative value of the luminance values of the target bin and the adjacent bin and the frequency count of the target bin and the adjacent bin as the barycenter of the luminance value of the target bin. 
     In addition, as the representative value of the luminance values of the target bin and the adjacent bin, for example, the central value of the luminance range of each bin is used. In addition, the weight used to calculate the weighted average may be set to be larger in a bin close in distance to the target bin in the spatial direction and a bin close in distance to the target bin in the luminance direction. 
     In step S 183 , the barycenter calculation unit  281  outputs the calculated barycenter to the coefficient calculation unit  244  as the reference luminance value Lg(nl)(p) of the target bin. 
     Thereafter, the reference luminance value calculation processing is finished and the processing progresses to step S 145  of  FIG. 16 . 
     Effects of First Embodiment 
     Since the reference luminance value Lg(nl)(p) of the target bin is set to the barycenter of the luminance value of the target bin, the reference luminance value Lg(nl)(p) becomes a value obtained by correcting the representative value of the luminance value of the target bin in a direction in which the frequency count of the block histogram of the target spatial block is increased when viewed from the target bin. Accordingly, the reference luminance value Lg(nl)(p) of each bin of the block histogram becomes closer to the luminance level corresponding to the hill of the block histogram as compared with the representative value of the luminance value of each bin. As a result, the reference luminance value Lg(nl)(p) has the same characteristic as the general luminance value Ll(nl)(p) of Japanese Unexamined Patent Application Publication No. 2009-177558. Accordingly, since the computation for determining the gray scale correction coefficient based on the reference luminance value Lg(nl)(p) and the gray scale correction characteristic function corresponds to computation in which a tone curve is applied to the general luminance value of Japanese Unexamined Patent Application Publication No. 2009-177558, it is possible to obtain the same gray scale compression effect as Japanese Unexamined Patent Application Publication No. 2009-177558. That is, it is possible to compress the luminance difference of the other component without changing a detail component representative of texture. 
     In the first embodiment of the present invention, only by calculating the gray scale correction coefficient for each bin of each block histogram, obtaining the gray scale correction coefficient of each pixel from the gray scale correction coefficient for each bin by an interpolation operation and applying the gray scale correction coefficient to each pixel, it is possible to perform gray scale correction of the luminance value of each pixel. Accordingly, the general luminance value and tone curve calculation processing of each pixel which is necessary for the gray scale correction processing of Japanese Unexamined Patent Application Publication No. 2009-177558 is not necessary. In the gray scale correction processing of Japanese Unexamined Patent Application Publication No. 2009-177558, two interpolation operations and one division operation are necessary for calculating the general luminance value. In contrast, in the first embodiment of the present invention, only one interpolation operation is necessary when the gray scale correction coefficient of each pixel is obtained. Accordingly, according to the first embodiment of the present invention, it is possible to reduce the computational complexity as compared with the gray scale correction processing of Japanese Unexamined Patent Application Publication No. 2009-177558 and, as a result, to reduce the scale of the circuit or software for performing the gray scale correction processing. 
     In addition, in the first embodiment of the present invention, in order to calculate the luminance value Lu(nl)(p) of each pixel, the block histogram and the spline control point may be hold for each spatial block and the reference luminance value Lg(nl)(p) and the gray scale correction coefficient are held for each bin of each block histogram. In contrast, in the gray scale correction processing of Japanese Unexamined Patent Application Publication No. 2009-177558, in order to calculate the luminance value Lu(nl)(p) of each pixel, the block histogram and the block integration value are held for each spatial block and the tone curve are held for each pixel. Accordingly, according to the first embodiment of the present invention, it is possible to reduce the necessary amount of memory as compared with the gray scale correction processing of Japanese Unexamined Patent Application Publication No. 2009-177558. 
     In the first embodiment of the present invention, in order to calculate the luminance value Lu(nl)(p) of each pixel, in step S 41 , the gray scale correction coefficient table may be only read. In contrast, in the gray scale correction processing of Japanese Unexamined Patent Application Publication No. 2009-177558, in order to calculate the general luminance value Ll(nl)(p) of each pixel, the block histogram and the block integration value of the block corresponding to the corresponding pixel and the adjacent block thereof are simultaneously read. Accordingly, according to the first embodiment of the present invention, it is possible to reduce the number of access times to the memory and to shorten processing time, as compared with the gray scale correction processing of Japanese Unexamined Patent Application Publication No. 2009-177558. 
     2. Second Embodiment 
     Overview of Second Embodiment 
     Next, a second embodiment of the present invention will be described with reference to  FIGS. 21 to 24 . 
     In the second embodiment of the present invention, the computational complexity may be reduced by changing the method of calculating the reference luminance value Lg(nl)(p). 
     [Configuration Example of Reference Luminance Value Calculation Unit] 
     In the digital video camera of the second embodiment of the present invention, instead of the reference luminance value calculation unit  243  of  FIG. 10 , a reference luminance value calculation unit  243  of  FIG. 21  is provided. In  FIG. 21 , the parts corresponding to  FIG. 10  are denoted by the same reference numerals and the description thereof will be appropriately omitted. 
     The reference luminance value calculation unit  243  of  FIG. 21  includes a primary differential coefficient calculation unit  401 , a luminance correction amount calculation unit  402 , and a luminance correction unit  403 . 
     The primary differential coefficient calculation unit  401  reads the block histogram of each spatial block from the block histogram memory  202 , and calculates and supplies a primary differential coefficient function of each block histogram to the luminance correction amount calculation unit  402 . 
     The luminance correction amount calculation unit  402  calculates a luminance correction amount function for each spatial block based on the primary differential coefficient function of each block histogram and supplies the luminance correction amount function to the luminance correction unit  403 . 
     The luminance correction unit  403  reads the block histogram of each spatial block from the block histogram memory  202 . The luminance correction unit  403  corrects the representative value of the luminance value of each bin of each block histogram based on the luminance correction amount function and calculates and supplies the reference luminance value Lg(nl)(p) of each bin to the coefficient calculation unit  244 . 
     [Description of Reference Luminance Value Calculation Processing] 
     Next, the reference luminance value calculation processing of the case where the reference luminance value calculation unit  243  has the configuration shown in  FIG. 21  will be described with reference to the flowchart of  FIG. 22 . In addition, the reference luminance value calculation processing corresponds to the processing of step S 144  of  FIG. 16 . 
     In step S 201 , the primary differential coefficient calculation unit  401  determines whether or not the luminance correction amount function of the target spatial block is uncalculated. If it is determined that the luminance correction amount function of the target spatial block is uncalculated, the processing progresses to step S 202 . 
     In step S 202 , the primary differential coefficient calculation unit  401  reads the block histogram of the target spatial block from the block histogram memory  202 . 
     In step S 203 , the primary differential coefficient calculation unit  401  calculates the primary differential coefficient function of the block histogram of the target spatial block. For example, the primary differential coefficient calculation unit  401  performs a primary differential operation with respect to the block histogram of the target spatial block, which is discrete data, using a discrete differential operator such as a Sobel operator ([−1 0 1]) so as to calculate the primary differential coefficient function. The primary differential coefficient calculation unit  401  supplies the calculated primary differential coefficient function to the luminance correction amount calculation unit  402 . 
       FIG. 23  shows an example of the primary differential coefficient function  451  calculated by performing a primary differential operation with respect to the block histogram  361  shown in  FIG. 19 . In addition, the horizontal axis of  FIG. 23  denotes a luminance and the vertical axis thereof denotes a frequency count. 
     In step S 204 , the luminance correction amount calculation unit  402  scales the amplitude of the frequency count direction of the primary differential coefficient function to an appropriate size and calculates the luminance correction amount function. For example, the luminance correction amount calculation unit  402  detects a position where the slope of the primary differential coefficient function  451  becomes a minimum (a downward-sloping gradient becomes a maximum). The luminance correction amount calculation unit  402  scales (normalization) the amplitude of the primary differential coefficient function such that the slope of the detected position becomes −1, thereby calculating the luminance correction amount function. For example, as shown in  FIG. 24 , the amplitude of the primary differential coefficient function  451  such that the slope of the position A in which the slope of the primary differential coefficient function  451  becomes a minimum becomes −1 is scaled, thereby calculating the luminance correction amount function  461 . The luminance correction amount calculation unit  402  supplies the calculated luminance correction amount function to the luminance correction unit  403 . Thereafter, the processing progresses to step S 205 . 
     In step S 201 , if it is determined that the luminance correction amount function of the target spatial block is calculated, the processing of steps S 202  to S 204  is skipped and the processing progresses to step S 205 . 
     In step S 205 , the luminance correction unit  403  corrects the representative value of the luminance value of the target bin by the luminance correction amount. For example, the luminance correction unit  403  sets the central value of the luminance range of the target bin to the representative value of the luminance value of the target bin and obtains the value of the luminance correction amount function for the representative value as the luminance correction amount. The luminance correction unit  403  adds the luminance correction amount to the representative value of the luminance value of the target bin so as to correct the representative value of the luminance value of the target bin. 
     In step S 206 , the luminance correction unit  403  outputs the corrected luminance value to the coefficient calculation unit  244  as the reference luminance value Lg(nl)(p) of the target bin. 
     Thereafter, the reference luminance value calculation processing is finished and the processing progresses to step S 145  of  FIG. 16 . 
     In the second embodiment of the present invention, the processing other than the above-described reference luminance value calculation processing is equal to that of the first embodiment and the description thereof is repeated and thus will be omitted. 
     Effects of Second Embodiment 
     In the second embodiment of the present invention, similarly to the first embodiment, the reference luminance value Lg(nl)(p) of the target bin becomes a value obtained by correcting the representative value of the luminance value of the target bin in a direction in which the frequency count of the block histogram of the target spatial block is increased when viewed from the target bin. By appropriately adjusting the size of the luminance correction amount by the processing of step S 204 , it is possible to prevent excessive correction for exceeding the luminance level corresponding to the hill of the block histogram. 
     In addition, in the first embodiment of the present invention, it is necessary to perform one division operation for each bin of each block histogram, in order to calculate weighted average of the luminance value, in the calculation of the reference luminance value Lg(nl)(p). In contrast, in the second embodiment of the present invention, in the calculation of the reference luminance value Lg(nl)(p), in order to scale the primary differential coefficient function, only one division operation may be performed for each spatial block. Accordingly, according to the second embodiment of the present invention, it is possible to reduce the computational complexity as compared with the first embodiment. 
     3. Third Embodiment 
     Overview of Third Embodiment 
     Next, a third embodiment of the present invention will be described with reference to  FIGS. 25 to 29 . 
     In the third embodiment of the present invention, the computational complexity may be reduced by changing the method of calculating the reference luminance value Lg(nl)(p). 
     [Configuration Example of Reference Luminance Value Calculation Unit] 
     In the digital video camera of the third embodiment of the present invention, instead of the reference luminance value calculation unit  243  of  FIG. 10 , a reference luminance value calculation unit  243  of  FIG. 25  is provided. In  FIG. 25 , the parts corresponding to  FIG. 10  are denoted by the same reference numerals and the description thereof will be appropriately omitted. 
     The reference luminance value calculation unit  243  of  FIG. 25  includes a cumulative histogram calculation unit  501 , a cumulative histogram memory  502 , an inverse function calculation unit  503 , a cumulative histogram inverse function memory  504 , a peak detection unit  505 , a peak position list memory  506 , a luminance modulation function calculation unit  507 , a luminance modulation function memory  508 , a mapping unit  509  and a mapping unit  510 . 
     The cumulative histogram calculation unit  501  reads the block histogram of each spatial block from the block histogram memory  202  and calculates a cumulative histogram function of each spatial block. The cumulative histogram calculation unit  261  stores the calculated cumulative histogram function of each spatial block in the cumulative histogram memory  502 . 
     The inverse function calculation unit  503  reads the cumulative histogram function of each spatial block from the cumulative histogram memory  502 , calculates an inverse function (hereinafter, referred to as a cumulative histogram inverse function) of each cumulative histogram function, and stores the inverse function in the cumulative histogram inverse function memory  504 . 
     The peak detection unit  505  reads the cumulative histogram function of each spatial block from the cumulative histogram memory  502 . The peak detection unit  505  detects a position (hereinafter, referred to as a peak position) corresponding to a luminance value in which the corresponding block histogram becomes a maximum in the cumulative histogram function of each spatial block. The peak detection unit  505  stores a peak position list representing the coordinates of the detected peak position of each cumulative histogram function in the peak position list memory  506 . 
     The luminance modulation function calculation unit  507  reads the peak position list of the cumulative histogram function of each spatial block from the peak position list memory  506 . The luminance modulation function calculation unit  507  calculates a luminance modulation function for each spatial block based on the peak position list and stores the luminance modulation function in the luminance modulation function memory  508 . 
     The mapping unit  509  reads the block histogram of each spatial block from the block histogram memory  202  and reads the luminance modulation function of each spatial block from the luminance modulation function memory  508 . The mapping unit  509  calculates the frequency count corresponding to the representative value of the luminance value of each bin of each block histogram based on the luminance modulation function and supplies the frequency count to the mapping unit  510 . 
     The mapping unit  510  reads the cumulative histogram inverse function of each spatial block from the cumulative histogram inverse function memory  504 . The mapping unit  510  calculates the reference luminance value Lg(nl)(p) for each bin of each block histogram based on each cumulative histogram inverse function and the frequency count corresponding to the representative value of the luminance value of each bin of each block histogram. The mapping unit  510  supplies the calculated reference luminance value Lg(nl)(p) for each bin of each block histogram to the coefficient calculation unit  244 . 
     [Description of Reference Luminance Value Calculation Processing] 
     Next, the reference luminance value calculation processing of the case where the reference luminance value calculation unit  243  has the configuration shown in  FIG. 25  will be described with reference to the flowchart of  FIG. 26 . In addition, the reference luminance value calculation processing corresponds to the processing of step S 144  of  FIG. 16 . 
     In step S 301 , the cumulative histogram calculation unit  501  determines whether or not the luminance modulation function and the cumulative histogram inverse function of the target spatial block are uncalculated. If it is determined that the luminance modulation function and the cumulative histogram inverse function of the target spatial block are uncalculated, the processing progresses to step S 302 . 
     In step S 302 , the cumulative histogram calculation unit  501  reads the block histogram of the target spatial block from the block histogram memory  202 . 
     In step S 303 , the cumulative histogram calculation unit  501  calculates the cumulative histogram function of the target spatial block, similarly to the processing of step S 161  of  FIG. 18 . The cumulative histogram calculation unit  501  stores the calculated cumulative histogram function in the cumulative histogram memory  502 . 
     Hereinafter, the cumulative histogram function is represented by y=f(x). x denotes a luminance and y denotes a cumulative frequency. 
     In step S 304 , the peak detection unit  505  detects the peak position of the cumulative histogram function. Specifically, the peak detection unit  505  reads the cumulative histogram function of the target spatial block from the cumulative histogram memory  502 . The peak detection unit  505  detects a position where the gradient of the cumulative histogram function becomes a maximum and the gradient becomes greater than a predetermined threshold value as the peak position. 
     For example,  FIG. 27  shows the same block histogram  361  and the cumulative histogram function  362  as  FIG. 19 . In this case, for example, a peak position P 11  and a peak position P 12  where the gradient of the cumulative histogram function  362  becomes a maximum and the gradient becomes greater than a predetermined threshold value are detected. A bin corresponding to the peak position P 11  and the peak position P 12  is a bin in which the frequency count becomes a maximum and the frequency count becomes greater than a predetermined threshold value in the block histogram  361 . Accordingly, the peak position P 11  and the peak position P 12  become coordinates on the cumulative histogram function  362  corresponding to a luminance value in which the block histogram  361  becomes a maximum. 
     In addition, hereinafter, as shown in  FIG. 27 , the luminance value and the cumulative frequency of the peak position P 11  are denoted by respectively a peak level  1  and an output peak level  1  and the luminance value and the cumulative frequency of the peak position P 12  are denoted by respectively a peak level  2  and an output peak level  2 . 
     The peak detection unit  505  stores a peak position list representing the coordinates of the detected peak positions in the peak position list memory  506 . 
     In step S 305 , the luminance modulation function calculation unit  507  calculates the luminance modulation function based on the detected peak positions. Specifically, the luminance modulation function calculation unit  507  reads the peak position list from the peak position list memory  506 . The luminance modulation function calculation unit  507  calculates a monotonically increasing function passing through a start point of the cumulative histogram function, the peak position detected by the peak detection unit  505  and an end point of the cumulative histogram function as the luminance modulation function. For example, a monotonically increasing spline function passing through the start point of the cumulative histogram function, the peak position and the end point of the cumulative histogram function is calculated as the luminance modulation function. The luminance modulation function calculation unit  507  stores the calculated luminance modulation function in the luminance modulation function memory  508 . 
       FIG. 28  shows an example of the luminance modulation function  551  for the cumulative histogram function  362  of  FIG. 27 . In addition, the horizontal axis of  FIG. 28  denotes a luminance and the vertical axis thereof denotes a cumulative frequency. The luminance modulation function  551  is a smoothly monotonically increasing function passing through the start point and the end point of the cumulative histogram function, the peak position E 11  and the peak position P 12 . 
     In addition, the luminance modulation function may pass through the vicinity of the peak position and may not necessarily pass through the coordinates of the peak position. The luminance modulation function may not necessarily be a smooth curved line in the meaning in which differentiation is continued if it is a monotonically increasing continuous function. However, since the function passes through the vicinity of the peak position, a smooth curved line which is not meandered more than necessary is more preferable. In addition, if a spline function is used, it is possible to easily obtain a curve line having such a property. 
     In step S 306 , the inverse function calculation function  503  calculates the cumulative histogram inverse function. Specifically, the inverse function calculation unit  503  reads the cumulative histogram function of the target spatial block from the cumulative histogram memory  502 . In addition, the inverse function calculation unit  503  calculates the inverse function x=f −1 (y) of the cumulative histogram function y=f(x). The inverse function calculation unit  503  stores the calculated cumulative histogram inverse function in the cumulative histogram inverse function memory  504 . Thereafter, the processing progresses to step S 307 . 
     Meanwhile, if it is determined that the luminance modulation function and the cumulative histogram inverse function of the target spatial block are calculated in step S 301 , the processing of steps S 302  to S 306  is skipped and the processing progresses to step S 307 . 
     In step S 307 , the mapping unit  509  obtains the cumulative frequency for the representative value of the luminance value of the target bin using the luminance modulation function. Specifically, the mapping unit  509  reads the luminance modulation function of the target spatial block from the luminance modulation function memory  508 . The mapping unit  509  sets, for example, the central value of the luminance range of the target bin to the representative value of the luminance value of the target bin, assigns the representative value to the luminance modulation function, and obtains the cumulative frequency for the representative value. The mapping unit  509  supplies the obtained cumulative frequency to the mapping unit  510 . 
     In step S 308 , the mapping unit  510  obtains the luminance value for the cumulative frequency obtained from the luminance modulation function using the cumulative histogram inverse function. Specifically, the mapping unit  510  reads the cumulative histogram inverse function of the target spatial block from the cumulative histogram inverse function memory  504 . The mapping unit  510  assigns the cumulative frequency obtained by the mapping unit  509  to the cumulative histogram inverse function and obtains the luminance value for the cumulative frequency. 
     Now, a detailed example of the processing of steps S 307  and S 308  will be described with reference to  FIG. 29 . 
       FIG. 29  is a diagram in which two graphs are connected, the horizontal axis thereof denotes a luminance, and the vertical axis thereof denotes a cumulative frequency. A left side is a graph of the same luminance modulation function  551  as  FIG. 28  and a right side is a graph in which the cumulative histogram function  362  of  FIG. 27  is horizontally reversed. Accordingly, the direction of the horizontal axis of the left graph is opposite to the direction of the horizontal axis of the right graph. 
     As described above, in step S 307 , the cumulative frequency for the representative value of the luminance value of each bin of the target spatial block is assigned based on the luminance modulation function  551 . For example, in the right graph of  FIG. 29 , the start point of each of the upward arrows arranged at the same interval in the luminance direction represent the representative value of the luminance value of each bin of the target spatial block. An intersection between a leftward arrow having a point in which the upward arrow collides with the luminance modulation function  551  as a start point and the axis in a cumulative frequency direction represents a cumulative frequency assigned to the representative value of the luminance value of each bin. 
     In addition, as described above, in step S 308 , the reference luminance value Lg(nl)(p) is assigned to the cumulative frequency assigned by the luminance gray scale function  551  based on the cumulative histogram inverse function. For example, in the left graph of  FIG. 29 , an intersection between a downward arrow having a point which a leftward arrow collides with the cumulative histogram function  362  as a start point and an axis of the luminance direction represents the reference luminance value Lg(nl)(p) assigned to each cumulative frequency. 
     As shown in  FIG. 29 , while the upward arrows are arranged at the same interval in the right graph, the downward arrows are densely arranged in the vicinities of the peak level  1  and the peak level  2 . That is, if the luminance value (representative value in the left graph of the luminance value of each bin) before correction is close to the peak level  1  or the peak level  2 , the reference luminance value Lg(nl)(p) having substantially the same value is assigned. In contrast, if the luminance value before correction is distant from the peak level  1  and the peak level  2 , the reference luminance value Lg(nl)(p) is assigned so as to become close to the peak level  1  or the peak level  2 . In addition, the reference luminance value Lg(nl)(p) is concentrated on the vicinities of the peak level  1  and the peak level  2 . 
     In step S 309 , the mapping unit  510  outputs the obtained luminance value to the coefficient calculation unit  244  as the reference luminance value Lg(nl)(p) of the target bin. 
     Thereafter, the reference luminance value calculation processing is finished and the processing progresses to step S 145  of  FIG. 16 . 
     In the third embodiment of the present invention, the processing other than the above-described reference luminance value calculation processing is equal to that of the first embodiment and the description thereof is repeated and thus will be omitted. 
     Effects of Third Embodiment 
     In the third embodiment of the present invention, similarly to the first embodiment and the second embodiment, the reference luminance value Lg(nl)(p) of the target bin becomes a value obtained by correcting the representative value of the luminance value of the target bin in a direction in which the frequency count of the block histogram of the target spatial block is increased when viewed from the target bin. 
     In addition, in the first embodiment of the present invention, it is necessary to perform one division operation for each bin of each block histogram, in order to calculate weighted average of the luminance value, in the calculation of the reference luminance value Lg(nl)(p). In contrast, in the third embodiment of the present invention, in the calculation of the reference luminance value Lg(nl)(p), a division operation is not necessary. Accordingly, according to the third embodiment of the present invention, it is possible to reduce the computational complexity as compared with the first embodiment. 
     4. Modified Example 
     Modified Example of Gray Scale Correction Characteristic Function 
     Although the example of calculating the gray scale correction characteristic function using only the block histogram of the corresponding spatial block for each spatial block is described in the above description, the block histogram of the adjacent spatial block may be used. For example, the gray scale correction characteristic function of the spatial block may be calculated using a block histogram obtained by synthesizing the block histogram of the corresponding spatial block and the block histogram of the adjacent spatial block. Accordingly, it is possible to increase the continuity of the gray scale correction characteristic between the corresponding spatial block and the adjacent spatial block. 
     Alternatively, the same gray scale correction characteristic function may be used in all or a part of spatial blocks. For example, a gray scale correction characteristic function which is shared among all spatial blocks may be calculated using a block histogram obtained by synthesizing block histograms of all spatial blocks. Alternatively, a fixed gray scale correction characteristic function which is prepared in advance may be used without referring to the block histogram. 
     [Modified Example of Method of Calculating Primary Differential Coefficient Function] 
     Although, in the second embodiment of the present invention, the example of calculating the primary differential coefficient function of the luminance direction using only the block histogram of the corresponding spatial block for each spatial block is described, the computation of the primary differential coefficient function is not limited to the luminance direction. For example, in order to increase the continuity of the reference luminance values of the corresponding spatial block and the adjacent spatial block, a primary differential coefficient function may be calculated in the spatial direction. In this case, the primary differential coefficient calculation unit  401  may calculate a three-dimensional primary differential coefficient vector in which the luminance direction and the spatial direction coincide and then may supply that obtained by projecting the vector to the luminance direction to the luminance correction amount calculation unit  402 . 
     The method of using the three-dimensional primary differential coefficient is equivalent to the calculation of the primary differential coefficient of the luminance direction after the block histograms of the corresponding spatial block and the adjacent spatial block are synthesized using a smoothing filter of the spatial direction. Accordingly, the smoothing of the block histogram in the spatial direction may be first performed and then the reference luminance value calculation processing above-described with reference to  FIG. 22  may be performed. 
     Although the Sobel operator is described as the detailed example of the operator used to calculate the primary differential coefficient function in the above description, other differential operators may be employed. For example, a filter convoluting a smoothing filter in the Sobel operator may be used and a primary differential coefficient function may be calculated while smoothing the block histogram in the luminance direction. 
     [Case of Performing Gray Scale Correction Processing with Respect to Image Signal after YC Transform Processing] 
     Although gray scale correction processing is performed with respect to the RGB image in the above description, gray scale correction processing may be performed with respect to the image signal after the YC transform processing. In this case, the DSP  116  has, for example, the configuration shown in  FIG. 30 . In addition, in  FIG. 30 , the parts corresponding to the case of  FIG. 3  are denoted by the same reference numerals and the description thereof will be appropriately omitted. 
     The DSP  116  shown in  FIG. 30  is different from the DSP  116  of  FIG. 3  in that the gray scale correction processing unit  153  is not provided between the demosaic processing unit  152  and the gamma correction processing unit  154  and the gray scale correction processing unit  601  is connected to the YC transform processing unit  155 . The gray scale correction processing unit  601  performs gray scale correction processing with respect to the Y image and the C image (Cb component and Cr component) supplied from the YC transform processing unit  155  and supplies the Y image and the C image subjected to gray scale correction processing to the LCD driver  118  or the CODEC  120  as necessary. 
     In addition, hereinafter, the pixel value of the pixel position p of the image data subjected to gray scale correction processing is denoted by [Yu(p), Cu(p)]. Here, Yu(p) denotes the value of the luminance component of the Y image and Cu(p) denotes the value of the color difference component of the C image. Hereinafter, in particular, the Cb component of the pixel value Cu(p) of the C image is denoted by Cbu(p) and the Cr component thereof is denoted by Cru(p). 
     The gray scale correction processing unit  601  of  FIG. 30  has, for example, the configuration shown in  FIG. 31 . That is, the gray scale correction processing unit  601  includes a luminance gray scale correction unit  631 , a Cb gray scale correction unit  632  and a Cr gray scale correction unit  633 . 
     The luminance gray scale correction unit  631  performs the same processing as the luminance gray scale correction unit  183  of  FIG. 5  and corrects (compresses) the gray scale of the pixel value Y(p) of the Y image from the YC transform processing unit  155 . The luminance gray scale correction unit  631  supplies the pixel value Yu(p) obtained by gray scale correction to the LCD driver  118  or the CODEC  120  as necessary and supplies the pixel value Yu(p) to the Cb gray scale correction unit  632  and the Cr gray scale correction unit  633 . 
     The Cb gray scale correction unit  632  performs the same processing as the gray scale correction unit  185  of  FIG. 5  using the pixel value Y(p) of the Y image from the YC transform processing unit  155  and the pixel value Yu(p) of the Y image from the luminance gray scale correction unit  631 , corrects the gray scale of the pixel value Cb(p) of the C image from the YC transform processing unit  155 , and supplies the pixel value Cbu(p), the gray scale of which is corrected, to the LCD driver  118  or the CODEC  120  as necessary. 
     The Cr gray scale correction unit  633  performs the same processing as the gray scale correction unit  185  of  FIG. 5  using the pixel value Y(p) of the Y image from the YC transform processing unit  155  and the pixel value Yu(p) of the Y image from the luminance gray scale correction unit  631 , corrects the gray scale of the pixel value Cr(p) of the C image from the YC transform processing unit  155 , and supplies the pixel value Cru(p), the gray scale of which is corrected, to the LCD driver  118  or the CODEC  120  as necessary. 
     For example, the gray scale correction performed by the Cb gray scale correction unit  632  and the Cr gray scale correction unit  633  is performed by multiplying the ratio of the pixel value Yu(p) of the Y image, the gray scale of which is corrected, to the pixel value Y(p) of the Y image by the pixel value (pixel value Cr(p) or the pixel value Cb(p)) of the C image, similarly to the computation shown in Equation (1). That is, a value obtained by dividing the pixel Yu(p) by the pixel value Y(p) is multiplied by the pixel value of the C image. 
     Since the Y image and the C image, that is, the luminance signal and the color difference signal are input to the gray scale correction processing unit  601 , the gray scale correction processing unit  601  may not generate the luminance image. In addition, since the Y image and the C image supplied to the gray scale correction processing unit  601  are already gamma-corrected, the Y image and the C image may not be non-linearly transformed. Accordingly, the blocks corresponding to the luminance calculation unit  181 , the non-linear transform unit  182 , the non-linear transform unit  184  and the non-linear inverse transform unit  186  of the gray scale correction processing unit  153  of  FIG. 5  are not provided in the gray scale correction processing unit  601 . 
     In this way, by performing the gray scale correction processing with respect to the Y image and the C image, it is possible to simply perform gray scale correction. 
     [Case of Performing Gray Scale Correction Processing with Respect to RAW Data] 
     In addition, gray scale correction processing may be performed with respect to RAW data, that is, the mosaic image. In this case, the DSP  116  has, for example, the configuration shown in  FIG. 32 . In addition, in  FIG. 32 , the parts corresponding to the case of  FIG. 3  are denoted by the same reference numerals and the description thereof will be appropriately omitted. 
     The DSP  116  shown in  FIG. 32  is different from the DSP  116  of  FIG. 3  in that the gray scale correction processing unit  681  is provided between the white balance processing unit  151  and the demosaic processing unit  152  in the DSP  116  shown in  FIG. 32 . That is, while the gray scale correction processing unit  153  is provided between the white balance processing unit  151  and the gamma correction processing unit  154  in the DSP  116  of  FIG. 3 , the gray scale correction processing unit  681  corresponding to the gray scale correction processing unit  153  of  FIG. 3  is provided between the white balance processing unit  151  and the demosaic processing unit  152  in the DSP  116  of  FIG. 32 . 
     The gray scale correction processing unit  681  performs gray scale correction processing with respect to the mosaic image Mw from the white balance processing unit  151  and supplies the mosaic image Mu subjected to the gray scale correction processing to the demosaic processing unit  152 . This gray scale correction processing unit  681  has, for example, the configuration shown in  FIG. 33 . 
     Specifically, the gray scale correction processing unit  681  includes a luminance calculation unit  711 , a non-linear transform unit  712 , a luminance gray scale correction unit  713 , a phase synchronization unit  714 , a phase synchronization unit  715 , a non-linear transform unit  716 , a mosaic gray scale correction unit  717 , and a non-linear inverse transform unit  718 . 
     The luminance calculation unit  711  performs the same processing as the luminance calculation unit  181   FIG. 5  and generates a luminance image from the mosaic image Mw. That is, the luminance calculation unit  711  calculates the luminance value L(p) from several pixel values of the mosaic image Mw using the pixel value of the pixel position p of the mosaic image Mw from the white balance processing unit  151  as the pixel value Mw(p) of the target pixel and supplies the luminance value to the non-linear transform unit  712 . 
     Since each pixel of the mosaic image Mw has, for example, only the pixel value of any one of R, G (Gr or Gb) and B components as shown in  FIG. 34 , the luminance calculation unit  711  calculates the luminance value by referring to not only the supplied pixel value Mw(p) of one pixel but also the pixel values of the pixels adjacent to that pixel. 
     In  FIG. 34 , pixels having the R component, G component and the B component are arranged in the Bayer array and one rectangle denotes one pixel of a mosaic image. In the figure, characters “R”, “G” and “B” in the rectangles indicates that the rectangles are the pixels having the pixel values of the R component, the G component and the B component. 
     Using that the pixel values of the components including the R component, the G component (Gr component and Gb component) and the B component are obtained when a total of four pixels of mutually adjacent 2 pixels×2 pixels is a processed unit, the luminance calculation unit  711  obtains the luminance value of the position represented by a circle in the figure based on the pixel values of four pixels. That is, in the figure, the luminance signal is generated as a sum of the RGB signal of four pixels adjacent to the position of the circle. 
     In the example of  FIG. 34 , the circle of the figure is located on the center of the region including the R pixel, the B pixel and two G pixels. In addition, the position where the luminance value is obtained may be a deviated position from the position of the pixel of the mosaic image by a half pixel and the interval between positions where the luminance value is obtained may not be one pixel unit. However, such a position (phase) difference is not problem in the correction of the gray scale of the luminance value. 
     Returning to  FIG. 33 , the non-linear transform unit  712  performs the same processing as the non-linear transform unit  182  of  FIG. 5 , non-linearly transforms the luminance value L(p) from the luminance calculation unit  711 , and supplies the luminance value L(nl)(p) obtained by non-linear transform to the luminance gray scale correction unit  713  and the phase synchronization unit  714 . 
     The luminance gray scale correction unit  713  performs the same processing as the luminance gray scale correction unit  183  of  FIG. 5 , compresses the gray scale of the luminance value L(nl)(p) from the non-linear transform unit  712 , performs the gray scale correction of the luminance value L(nl)(p), and supplies the luminance value Lu(nl)(p) obtained by the gray scale correction to the phase synchronization unit  715 . 
     The phase synchronization unit  714  performs an interpolation processing using several luminance values of the position adjacent to the target pixel among the luminance values from the non-linear transform unit  712  and calculates the luminance value of the position of the target pixel. The interpolation processing performed by the phase synchronization unit  714  is, for example, a binary interpolation processing using a total of four luminance values of the pixels of 2×2 positions adjacent to the target pixel. 
     The phase synchronization unit  714  supplies the obtained luminance value to the mosaic gray scale correction unit  717 , if the luminance value of the target pixel is obtained. 
     The phase synchronization unit  715  performs an interpolation processing using several luminance values of the position adjacent to the target pixel among the luminance values from the luminance gray scale correction unit  713  and calculates the luminance value, the gray scale of which is corrected, of the position of the target pixel. The interpolation processing performed by the phase synchronization unit  715  is the same processing as the interpolation processing performed by the phase synchronization unit  714  and is, for example, a binary interpolation processing or the like. 
     The non-linear transform unit  716  performs the same processing as the non-linear transform unit  184  of  FIG. 5 , non-linearly transforms the pixel value Mw(p) of the target pixel from the white balance processing unit  151 , and supplies the non-linearly transformed pixel value to the mosaic gray scale correction unit  717 . 
     The mosaic gray scale correction unit  717  performs the same processing as the gray scale correction unit  185  of  FIG. 5  using the luminance value from the phase synchronization unit  714  and the luminance value, the gray scale of which is corrected, from the phase synchronization unit  715 , corrects the gray scale of the pixel value from the non-linear transform unit  716 , and supplies the pixel value, the gray scale of which is corrected, to the non-linear inverse transform unit  718 . For example, in the same manner as calculated in the above-described Equation (1), the mosaic gray scale correction unit  717  multiplies a value obtained by dividing the luminance value, the gray scale of which is corrected, from the phase synchronization unit  715  by the luminance value from the phase synchronization unit  714  by the pixel value from the non-linear transform unit  716 , thereby correcting the gray scale of the pixel value. Accordingly, the gray scale of the pixel value of the target pixel, that is, the pixel value of the R, G or B component, is corrected. 
     The non-linear inverse transform unit  718  performs non-linear inverse transform, which is inverse transform of non-linear transform of the non-linear transform unit  716 , with respect to the pixel value from the mosaic gray scale correction unit  717 . The non-linear inverse transform unit  718  supplies the pixel value Mu(p) obtained by non-linear inverse transform to the demosaic processing unit  152 . 
     In this way, if the gray scale correction processing is performed with respect to the mosaic image, the position of the luminance value calculated by the luminance calculation unit  711  is different from the position of the target pixel of the mosaic image. However, by performing phase synchronization using the phase synchronization unit  714  and the phase synchronization unit  715 , it is possible to obtain the luminance value of the position of the target pixel. 
     [Example of Application Range of the Invention] 
     As described above, the digital video camera of  FIG. 2  performs image processing of extracting the general structure of the image. Since the information extracted by such image processing may be used in processing of improving image quality or the like, as the apparatus in which a block for extracting the general structure of the image is mounted, other apparatuses may be used in addition to a digital video camera. For example, as such apparatuses, an imaging apparatus such as a digital camera, a display apparatus such as a printer, a display, or the like is considered. In addition, the present invention is applicable to an apparatus for processing or editing an image or a computer program. 
     [Configuration Example of Hardware of Computer] 
     The above-described series of processing may be executed by hardware or software. If the series of processes is executed by software, a program configuring the software is installed in a computer in which dedicated hardware is mounted or, for example, a general-purpose personal computer which is capable of executing a variety of functions by installing various types of programs, from a program recording medium. 
       FIG. 35  is a block diagram showing the hardware configuration example of a computer for executing the above-described series of processes by a program. 
     In the computer, a Central Processing Unit (CPU)  901 , a Read Only Memory (ROM)  902  and a Random Access Memory (RAM)  903  are connected to each other by a bus  904 . 
     An input/output interface  905  is connected to the bus  904 . An input unit  906  including a keyboard, a mouse, a microphone and the like, an output unit  907  including a display, a speaker and the like, and a recording unit  908  including a hard disk, non-volatile memory and the like, a communication unit  909  including a network interface and the like, and a drive  910  for driving a removable media  911  such as a magnetic disk, an optical disc, a magneto-optical disk and a semiconductor memory are connected to the input/output interface  905 . 
     In the computer having the above configuration, the CPU  901  loads and executes, for example, the program recorded on the recording unit  908  to the RAM  903  through the input/output interface  905  and the bus  904 , thereby performing the above-described series of processes. 
     The program executed by the computer (CPU  901 ) is recorded, for example, on the removable media  911  which is a package media including a magnetic disk (including a flexible disk), an optical disc (a Compact Disc-Read Only Memory (CD-ROM), a Digital Versatile Disc (DVD), or the like), a magnetooptical disk, a semiconductor memory or the like, or is provided through a wired or wireless transfer medium such as a local area network, the Internet and a digital satellite broadcast. 
     The program may be installed in the recording unit  908  through the input/output interface  905  by mounting the removable media  911  in the drive  910 . The program may be received by the communication unit  909  through the wired or wireless transfer medium and installed in the recording unit  908 . The program may be installed in the ROM  902  or the recording unit  908  in advance. 
     The program executed by the computer may be a program for performing a process in time series in the order described in the present specification or a program for performing a process at necessary timings such as upon calling or in parallel. 
     The present application contains subject matter related to that disclosed in Japanese Priority Patent Application JP 2010-053864 filed in the Japan Patent Office on Mar. 10, 2010, the entire contents of which are hereby incorporated by reference. 
     It should be understood by those skilled in the art that various modifications, combinations, sub-combinations and alterations may occur depending on design requirements and other factors insofar as they are within the scope of the appended claims or the equivalents thereof.