JP2009272865A - Video converter, video conversion method, and video conversion program - Google Patents

Abstract

<P>PROBLEM TO BE SOLVED: To convert colors while suppressing excess conversion of contrast in color conversion of a video image. <P>SOLUTION: The video image is divided into areas of a plurality of objects by an area division part 22, histogram of each area is created by a conversion suppression parameter creation part 23, and a conversion suppression parameter for suppressing that the histogram is greatly converted when the histogram of each area is distributed to a target distribution range is created. Furthermore, a color conversion function is created using the conversion suppression parameter by an RGB conversion function creation part 25, and the color conversion function is applied to the video image by a video image conversion part 27. <P>COPYRIGHT: (C)2010,JPO&INPIT

Description

  The present invention relates to a technique for automatically converting the gradation of an image, and more particularly to a technique for converting the gradation of an input image in a digital image processing apparatus.

  The histogram of the video shows the distribution of light and dark values for each pixel in the video. Contrast stretching is a technique for improving the contrast of an image by widely distributing the light and dark values from a low value to a high value for an image with a skewed light and dark value distribution. Through contrast stretching, an image that is too dark becomes brighter, and an image that is too bright becomes darker to maintain an appropriate brightness value. That is, the overall contrast balance of the video is improved by correcting the luminance value distribution of the video.

  FIG. 16 is a block diagram showing a configuration of a conventional contrast stretching apparatus. As shown in the figure, the conventional contrast stretching apparatus has a distribution calculation unit 42 and a stretching unit 43.

  The distribution calculation unit 42 counts the brightness value from the input video to obtain a histogram value or a histogram function. The target value input unit receives the target minimum value and the target maximum value, and outputs the input values to the stretching unit 43. The stretching unit 43 obtains a light / dark value having a minimum value and a maximum value using the histogram function obtained by the distribution calculation unit 42, and a target value in which the minimum value and the maximum value of the brightness of the input video are input to the target distribution input unit. Transform the histogram to match the lowest and target highest values. In the processing of the stretching unit 43, if the target minimum value is 0 and the target maximum value is 255, the image has brightness values from 0 to 255, and the contrast of the image increases.

Further, the above-described histogram enlarging process is similarly applied to the R component, the G component, and the B component of the video, so that the contrast can be increased while maintaining the color. Alternatively, it is possible to create a video with an increased contrast with little color bias by performing histogram enlargement processing independently on the R component, the G component, and the B component. As prior art documents related to the present application, there are the following.
Japanese Patent Laid-Open No. 4-257082

  However, in the case of contrast stretching according to the conventional technique, the contrast is excessively improved, and the image quality may be adversely affected.

  The present invention has been made to solve the above-described problems, and an object of the present invention is to provide a method of converting colors while suppressing excessive contrast conversion during color conversion of an image. is there.

  According to a first aspect of the present invention, there is provided a video conversion device, a storage unit that stores an input video, a region division unit that reads the video, divides it into a plurality of target object regions, and stores the divided regions in a storage unit; Histogram creating means for creating a histogram of each region in the color component, target range designating means for designating a target distribution range for each histogram, and each histogram being distributed in the target distribution range Conversion suppression parameter creating means for creating a conversion suppression parameter for suppressing the contrast of the video from being larger than a preset level when converting the video, and color conversion using the conversion suppression parameter A color conversion function creating means for creating a function; and a video conversion means for converting the video by applying the color conversion function to the video. And butterflies.

  In the present invention, the image is divided into a plurality of object regions, a conversion suppression parameter is created to distribute the histogram of each region in the target distribution range, and a color conversion function is created using the conversion suppression parameter Then, applying the color conversion function to the video suppresses excessive conversion of the contrast.

  In the video conversion apparatus, the area dividing unit detects a face area.

  In the present invention, the contrast of the detected face area is prevented from being excessively converted.

  In the video conversion apparatus, the histogram creating means creates a histogram of R component, G component, and B component for each region.

  In the present invention, a conversion suppression parameter is generated for each of the R component, G component, and B component histograms for each of the divided regions, a color conversion function is generated using the conversion suppression parameter, and the color conversion function is By applying it to the video, it is possible to suppress excessive conversion of contrast.

A video conversion method according to a second aspect of the present invention is a video conversion method performed by a video conversion device,
A step of storing the input video in a storage unit; a step of reading the video, dividing the video into a plurality of target object regions and storing it in the storage unit; and a step of creating a histogram of each region in a desired color component Specifying a target distribution range for each histogram, and a degree to which the contrast of the video is set in advance when converting the video so that each histogram is distributed in the target distribution range Generating a conversion suppression parameter for suppressing a larger conversion; creating a color conversion function using the conversion suppression parameter; and converting the image by applying the color conversion function to the image And a step of performing.

  According to a third aspect of the present invention, there is provided a video conversion program for processing a storage unit to store an input video by a video conversion device, reading the video, dividing it into a plurality of object areas, and storing the division into a storage unit. Processing, processing for creating a histogram of each region in a desired color component, processing for designating a target distribution range for each histogram, and distributing each histogram in the target distribution range A process for creating a conversion suppression parameter for suppressing the contrast of the video from being converted larger than a preset level when converting the video, and creating a color conversion function using the conversion suppression parameter And a process of converting the video by applying the color conversion function to the video.

  According to the present invention, it is possible to suppress excessive contrast conversion at the time of color conversion. For example, it is possible to suppress a large conversion of contrast at the time of conversion of brightness and color of a face area. It becomes possible.

  FIG. 1 is a block diagram showing an overall configuration of a video conversion apparatus according to this embodiment. As shown in FIG. 1, the video conversion apparatus includes an input device 1, a central processing control device 2, and a storage device 3. The input device 1 includes a video signal input unit 11, a shutter input unit 12, and a target distribution selection unit 13. The central processing control device 2 includes a frame extraction unit 21, a region division unit 22, a conversion suppression parameter creation unit 23, a target. The storage device 3 includes an RGB distribution creation unit 24, an RGB conversion function creation unit 25, a conversion LUT (look-up table) creation unit 26, and a video conversion unit 27. The storage device 3 stores information output by each unit, An RGB distribution storage unit 31 and a conversion LUT storage unit 32 are included.

  The video conversion apparatus may be configured as dedicated hardware, or may be configured using a general-purpose computer so that the processing of each unit is executed by a computer program. The results processed by each unit are stored in the storage unit so as to be readable.

  When the video signal is input, the video signal input unit 11 outputs the video signal to the frame extraction unit 21 and the video conversion unit 27. Here, the video output from the video signal input unit 11 is referred to as an input video.

  The input video handled in the present embodiment is a moving image composed of a plurality of frames, and each frame is a still image composed of a plurality of pixels. When the width of the input video is represented by width and the height is represented by height, each frame is composed of width × height pixels. Each pixel is data representing a color in the RGB color model, and is composed of an x coordinate value, a y coordinate value, an R value, a G value, and a B value in the frame. The x coordinate value is an integer value of 1 or more and width or less. Yes, the y-coordinate value is an integer value between 1 and height, and the R value, G value, and B value are integer values between 0 and 255, respectively.

  The target distribution selection unit 13 outputs the target RGB distribution name selected by the user to the target RGB distribution creation unit 24.

  The target RGB distribution creation unit 24 receives the target RGB distribution name and the RGB distribution information group, creates target RGB distribution information, and the created target RGB distribution information is output to the conversion suppression parameter creation unit 23. Note that the RGB distribution information group is input by reading from the RGB distribution storage unit 31.

  When a shutter operation is performed by the user, the shutter input unit 12 outputs a shutter signal that tells the frame extraction unit 21 of the shutter operation.

  The frame extraction unit 21 receives a video signal and a shutter signal and outputs a still image. The still image output from the frame extraction unit 21 is a one-frame still image extracted from the input video.

The area dividing unit 22 divides the input image into a plurality of object areas.
The conversion suppression parameter creation unit 23 creates a histogram of each region in the R component, G component, and B component, receives the target value output from the target RGB distribution creation unit 24 as an input, and creates a conversion suppression parameter.

  The RGB conversion function creation unit 25 receives the conversion suppression parameter, and the R conversion function and the G conversion function corresponding to each histogram so that the histograms of the R component, the G component, and the B component are in a distribution range that matches the conversion suppression parameter. And B conversion function.

The region dividing unit 22 receives a still image and outputs a region information group. The area information group includes a plurality of area information, and each area information includes an area name R and an area set Region _R. If the x coordinate value of pixel p is xp and the y coordinate value is yp, the region set Region _R is

  It is expressed. The area dividing unit 22 may detect the entire area information, the face area information, and the black eye area information when the subject of the still image is a person. Alternatively, when the subject of the still image is an object, the entire region information, the subject region information, and the background region information may be detected. Hereinafter, an example in which the entire area information, the face area information, and the black eye area information are detected when the subject is a person will be described.

Here, the whole area information is composed of the area name WHOLE and the whole area set Region _WHOLE , the face area set is composed of the area name FACE and the face area set Region _FACE , and the black eye area set is the area name EYE and the black eye area set Region _EYE. It shall be comprised by. The whole region set Region _WHOLE is a set including all pixels included in the input still image.

  FIG. 2 is a block diagram showing a configuration of the area dividing unit 22 for detecting face area information and black eye area information and dividing an input image into a face area and a black eye area. The region dividing unit 22 includes an image scale converting unit 221, a detection target region extracting unit 222, a spatial frequency analyzing unit 223, a face candidate determining unit 224, a face candidate selecting unit 225, a skin region extracting unit 226, And a black eye region extraction unit 227.

  Selection of face candidates in the region dividing unit 22 uses Haar-type feature values, and uses an AdaBoost learning method and a method using a cascade structure detector (see, for example, JP-A-2006-293720).

  The detection target region cutout unit 222 cuts out a region having a predetermined size from the scale-converted image. However, the shape of the region handled here may be an arbitrary shape.

  The spatial frequency analysis unit 223 performs spatial frequency filtering on the cut out region. For example, an AdaBoost learning method and a Haar type feature amount detection method having a cascade detector structure may be used. This method is detailed in P. Viola, M. Jones, "Rapid object Detection using a Boosted Cascade of Simple Features", In Proc. IEEE Conf. On Computer Vision and Pattern Recognition, kauai, USA, 2001. The detailed explanation is omitted.

  The face candidate determination unit 224 determines whether the region can be a face region candidate based on the output value of the spatial frequency analysis unit 223.

  The face candidate selection unit 225 selects and outputs a face area candidate having the maximum area among face area candidates determined to be face candidates.

The skin area extraction unit 226 receives a face area candidate and outputs a face area set Region _FACE . The face area set Region _FACE is composed of pixels included in face area candidates whose pixel color is skin color. In other words, the Region _FACE has xp as the x coordinate value of pixel p, yp as the y coordinate value, rp as the R value, gp as the G value, and bp as the B value.

  It is expressed. Note that the RGB values handled as skin color are set in advance. For example, in the YUV color model, a method is used in which the Cb value is -40 or more and -20 or less and the Cb value is 15 or more and 35 or less as skin color.

The black eye region extraction unit 227 outputs a black eye region set Region _EYE with the face region candidate as an input. The black eye region set Region _EYE is composed of pixels with low luminance values among the pixels included in the face region candidate. More specifically, among the pixels included in the face area candidate, a 1% pixel group is set as a black eye area set in order of increasing luminance value. FIG. 3 is a diagram illustrating an example of processing when the area dividing unit 22 detects a human face.

  The target RGB distribution creation unit 24 receives the target RGB distribution name and the RGB distribution information group and outputs target RGB distribution information. The RGB distribution information group is read from the RGB distribution storage unit 31. The RGB distribution information group includes a plurality of RGB distribution information, and each RGB distribution information has an RGB distribution name and includes a plurality of RGB distribution records. FIG. 4 is a diagram illustrating an example of RGB distribution information. Each RGB distribution record includes a record number, a region name, a cumulative ratio value, a minimum expansion / contraction rate, a maximum expansion / contraction rate, and a target color. Here, the cumulative ratio value is a decimal value between 0 and 1. Here, the minimum expansion ratio and the maximum expansion ratio are small values of 0 or more.

  The color components handled in this embodiment will be described. Luminance (luminance: Y component in the YCbCr color model), Cb (Cb component in the YCbCr color model), Cr (Cr component in the YCbCr color model), black (in the CMYK color model) K component), red (R component in RGB color model), green (G component in RGB color model), blue (B component in RGB color model), hue (H component in HSV color model), saturation (HSV color model) S component) and brightness (V component in the HSV color model), and the minimum value is 0 and the maximum value is 255 in all color components.

  The conversion suppression parameter creation unit 23 receives a still image, a region information group, and target RGB distribution information, and creates and outputs a conversion suppression parameter. The conversion suppression parameter is created by the following procedure.

First, a color value calculation function that returns the color value of the pixel p in the color component Component

And Where g (Component, p, x)

If the pixel p included in the region set Region _R in the pixel set P is extracted, the probability that the color value of the pixel p in the color component Component is x, that is, color (p, Component) = x. Probability density function (histogram function) frequency (P, Region _R , Component, x)

  Is defined.

For example, region information of whole image region information (region name WHOLE, region set Region _WHOLE ), face region information (region name FACE, region set Region _FACE ), and black eye region information (region name EYE, region set Region _EYE ) If the set of pixels representing an image is Py and the luminance color component is Y, the luminance histogram of the entire area is expressed as frequency (Py, Region _WHOLE , Y, x). In addition, the luminance histogram of the face region is represented as frequency (P, Region _FACE , Y, x), and the luminance histogram of the black eye region is represented as frequency (P, Region _EYE , Y, x).

The cumulative distribution function cumulative (P, Region _R , Component, x) derived from the probability density function frequency (P, Region _R , Component, x) representing the histogram of the color component Component is

  It is expressed.

If the probability density function is represented by the curve of FIG. 5, the cumulative distribution function is a curve represented as shown in FIG. Furthermore, the inverse function of the cumulative distribution function with respect to x is

It is expressed. Here, y is a value satisfying the relationship y = cumulative (P, Region _R , Component, x).

Here, the control value calculation function control (P, Region _R , Component, A) is defined as follows using the cumulative distribution function cumulative.

However, here, the set of pixels included in the input still image is P, the region name in the target RGB distribution record is R, the region set corresponding to the region name R created by dividing the still image into regions is Region _R , The color component is Component, and the cumulative ratio value is A.

By applying this control value calculation function to the cumulative ratio value A, as shown in FIG.

  Thus, the control value C corresponding to the cumulative ratio value A is calculated.

  A control value for a certain color component is calculated corresponding to each RGB distribution record using the control value calculation function control. For each RGB distribution record, the target value T can be set by calculating the target color value for a certain color component. FIG. 8 shows an example in which the control value and the target value are calculated corresponding to each RGB distribution record in the R component.

Here, the stretch rate calculation function stretch when the range of the control values C ₁ and C ₂ is converted into the range of the target values T ₁ and T ₂ is expressed as

It is defined as That is, the stretch rate calculation function stretch (C ₁ , C ₂ , T ₁ , T ₂ ) calculates how many times the distance between T ₁ and T ₂ is stretched or contracted between the distances between C ₁ and C ₂ .

Furthermore, the control series is {C _k } _{k = 1,2, ..., n} (where C ₁ ≤C ₂ ≤ ... ≤C _n ), and the target value sequence is {T _k } _{k = 1,2, ..., n} , The initial expansion rate sequence {d _k } _{k = 1 , where} {a _k } _{k = 1,2, ..., n} and the maximum expansion rate sequence {b _k } _{k = 1,2, ..., n , 2, ..., n} , control expansion / contraction rate sequence {e _k } _{k = 1,2, ..., n} , conversion suppression control value sequence {L _k } _{k = 1,2, ..., n} , conversion suppression target value sequence { M _k } _{k = 1,2, ..., n}

Ask for. However,

  And

The control value sequence {C _k } _{k = 1,2, ..., n} , the target value sequence {T _k } _{k = 1,2, ..., n} , the minimum expansion / contraction rate sequence {a _k } _{k = 1, 2, ..., n} , conversion suppression control value for calculating conversion suppression control value sequence {L _k } _{k = 1,2, ..., n} from maximum expansion / contraction rate sequence {b _k } _{k = 1,2, ..., n} The column calculation function restrainControl and the conversion suppression target value sequence {M _k } _{k = 1,2, ..., n} are calculated respectively.

  It is defined as

The conversion suppression control value sequence {L _k } _{k = 1, 2,..., N} and the conversion suppression target value sequence {M _k } _{k = 1, 2,.}

Meet. That is, the k-th expansion rate e _k, k + 1 th stretch ratio _{stretch (C k, C k +} 1, T k, T k + 1) does leave alter translation suppression control value L _k and conversion The suppression target value _Mk is calculated.

For example, as shown in FIG. 9, L _k-1 = 60, M _k-1 = 90, C _k = 80, T _k = 160, C _{k + 1} = 240, T _{k + 1} = 240, a _k = 0.5 , B _k = 2.0, the initial expansion ratio d _k and the suppression expansion ratio e _k are

It becomes. Here, when L _k and M _k are _obtained by the procedure shown above,

It becomes. L _k and M _k obtained here are

The expansion ratio between L _{k-1 and} L _k is suppressed to the suppression expansion ratio e _k = 2.0, and the expansion ratio between L _k -C _{k + 1} is the expansion / contraction between C _k -C _{k + 1} before suppression. The rate stretch (C _k , C _{k + 1} , T _k , T _{k + 1} ) = 0.5 is set to the same value.

  FIG. 10 shows an example in which the conversion suppression control value and the conversion suppression target value for a certain color component are calculated in accordance with the above procedure, corresponding to each RGB distribution record.

Here, the color conversion function convert ({L _k } _{k = 1, 2,..., N} , {M _k } _{k = 1, 2,..., N} , x) for converting the color value x is

  It is defined as

Here, for example, the luminance histogram of the still image is distributed as shown in FIG. 11, and the conversion suppression control values {L ₁ , L ₂ , L ₃ , L ₄ } and the conversion suppression target values {M ₁ , M ₂ , M ₃ , M ₄ } is defined as shown in the figure, the color conversion function convert ({L ₁ , L ₂ , L ₃ , L ₄ }, {M ₁ , M ₂ , M ₃ , M ₄ }, x FIG. 12 shows the histogram after the luminance value is converted by applying (). As shown in the figure, the color conversion function convert converts the histogram so that the conversion suppression control value matches the conversion suppression target value.

  Here, as shown in FIG. 13, the R value of the target color is set as the R target value, the G value as the G target value, and the B value as the B target value for each target RGB distribution record.

Also, P a set of still image pixels, AREA the area name of a target RGB distribution records Record, when the cumulative percentage value A, using the control value calculation function Control, R control value C _R is

Calculated to the total RGB distribution records R control value column {C _{R, k}} by calculating the control values for _{k = 1, 2, ...,} calculates _n (provided that _{C 1 ≦ C 2 ≦ ... ≦} C n) it can.

Wherein further C _R, the R target value in the RGB distribution records to calculate the _k T _{R, k,} a minimum stretch ratio of a _k, R target sequence up stretch ratio as _{b k {T R, k}} k = 1 _{, 2,..., N define} a minimum expansion / contraction rate {a _k } _{k = 1, 2,..., N} and an R maximum expansion / contraction rate {b _k } _{k = 1, 2,.}

Then, using the conversion suppression control value sequence calculation function restrainControl and the conversion suppression target value sequence calculation function restrainTarget, the R conversion suppression control value sequence {L _{R, k} } _{k = 1, 2,.} The sequence {M _{R, k} } _{k = 1,2, ..., n} is

  Is calculated.

Similarly, the G control value C _G of Record is

If the G control value sequence is {C _{G, k} } _{k = 1,2,..., N} and the G target value sequence is {T _{G, k} } _{k = 1,2} ,. The control value sequence {L _{G, k} } _{k = 1, 2,..., N} and the G conversion suppression target value sequence {M _{G, k} } _{k = 1, 2,.}

Is calculated. The B control value C _B of Record is

If the B control value string is {C _{B, k} } _{k = 1,2,..., N} and the B target value string is {T _{B, k} } _{k = 1,2} ,. The control value sequence {L _{B, k} } _{k = 1, 2,..., N} and the B conversion suppression target value sequence {M _{B, k} } _{k = 1, 2,.}

  Is calculated.

  The R conversion suppression control value, the R conversion suppression target value, the G conversion suppression control value, the G conversion suppression target value, the B conversion suppression control value, and the B conversion suppression target value are calculated for each distribution record by the above procedure, and the R conversion suppression control is performed. FIG. 14 shows a table in which the values are arranged in ascending order. It should be noted that other than the R conversion suppression control value and the R conversion suppression target value are abbreviated. The conversion suppression parameter creation unit 23 outputs the table created here as a conversion suppression parameter.

  The RGB conversion function creation unit 25 receives the conversion suppression parameter and outputs an R conversion function, a G conversion function, and a B conversion function.

Here, the R conversion suppression control value sequence {L _{R, k} } _{k = 1, 2,..., N} (where L _{R, 1} ≦ L _{R, 2} ≦ ... ≦ L _{R, n} ) and the R conversion suppression target value R conversion function convert _R (x) for converting R values using the sequence {M _{R, k} } _{k = 1, 2} ,.

G conversion suppression control value sequence {L _{G, k} } _{k = 1,2, ..., n} and G conversion suppression target value sequence { _{MG, k} } _{k = 1,2, ..., n} Use G conversion function convert _G (x) to convert G value using

B value using B conversion suppression control value sequence {L _{B, k} } _{k = 1,2, ..., n} and B conversion suppression target value sequence {MB _{, k} } _{k = 1,2, ..., n} B conversion function convert _B (x)

  It is defined as

The RGB conversion function creation unit 25 outputs the R conversion function convert _R (x), the G conversion function convert _G (x), and the B conversion function convert _B (x).

  The conversion LUT creation unit 26 receives the R conversion function, the G conversion function, and the B conversion function as inputs, and outputs an R conversion LUT, a G conversion LUT, and a B conversion LUT. Specifically, an LUT as shown in FIG. 15 is created in which R conversion functions, G conversion functions, and B conversion functions are respectively applied to all possible R, G, and B values. And stored in the conversion LUT storage unit 32.

  The video conversion unit 27 receives the input video and the color conversion LUT and outputs a converted video. Specifically, with reference to the color conversion LUT for each pixel of each frame of the input video, the R value, the G value, and the B value are converted to create a converted frame, and the generated converted frame is sequentially output to finally A typical converted video.

  As described above, according to the present embodiment, a conversion suppression parameter is created to divide an image into a plurality of object regions, and to distribute a histogram of each region in a target distribution range. By creating a color conversion function using and applying the color conversion function to an image, it is possible to suppress excessive conversion of contrast.

  Further, by detecting the face area, it is possible to prevent the contrast of the detected face area from being excessively converted.

  Also, a conversion suppression parameter is created for each of the R component, G component, and B component histograms for each divided region, a color conversion function is created using the conversion suppression parameter, and the color conversion function is applied to the video. Thus, excessive conversion of the contrast of the R component, G component, and B component is suppressed.

It is a block diagram which shows the whole structure of the video converter in one Embodiment. It is a block diagram which shows the structure of the area division part 22 for detecting face area information and black eye area information, and dividing an input image | video into a face area and a black eye area. It is a figure which shows an example of the process at the time of detecting a human face in the area | region division part. It is a figure which shows an example of RGB distribution information. It is a figure which shows an example of the probability density function (histogram function) showing the probability that the color value of the pixel p in a certain color component will be x. It is a figure which shows the cumulative distribution function about the probability density function shown in FIG. It is a figure which shows an example of the control value calculation function for calculating a control value from the accumulation ratio value A. It is a figure which shows what added the control value and the target value to each RGB distribution record of FIG. It is a figure which shows the specific example of the process which calculates | requires a conversion suppression control value and a conversion suppression target value from a control value and a target value. It is a figure which shows what added the conversion suppression control value and the conversion suppression target value to each RGB distribution record of FIG. It is a figure which shows the histogram before converting with a color conversion function. It is a figure which shows the histogram after conversion by a color conversion function. FIG. 5 is a diagram showing information obtained by adding R target value, G target value, and B target value information to each RGB distribution record of FIG. 4. R conversion suppression control value, R conversion suppression target value, G conversion suppression control value, G conversion suppression target value, B conversion suppression control value, B conversion suppression target value are arranged in ascending order for R conversion suppression control values. FIG. It is a figure which shows an example of the look-up table for each color conversion of R component, G component, and B component. It is a block diagram which shows the structure of the conventional contrast stretching apparatus.

Explanation of symbols

DESCRIPTION OF SYMBOLS 1 ... Input device 2 ... Central processing control device 3 ... Memory | storage device 11 ... Video signal input part 12 ... Shutter input part 13 ... Target distribution selection part 21 ... Frame extraction part 22 ... Area division part 23 ... Conversion suppression parameter creation part 24 ... Target RGB distribution creation unit 25 ... RGB conversion function creation unit 26 ... conversion LUT creation unit 27 ... video conversion unit 31 ... RGB distribution storage unit 32 ... conversion LUT storage unit 42 ... distribution calculation unit 43 ... stretching unit 221 ... image scale conversion Unit 222 ... detection target region extraction unit 223 ... spatial frequency analysis unit 224 ... face candidate determination unit 225 ... face candidate selection unit 226 ... skin region extraction unit 227 ... black eye region extraction unit

Claims (5)

Storage means for storing the input video;
An area dividing means for reading out the video and dividing it into areas of a plurality of objects and storing them in a storage means;
A histogram creating means for creating a histogram of each region in a desired color component;
Target range specifying means for specifying a target distribution range for each histogram;
Conversion suppression that creates a conversion suppression parameter for suppressing the contrast of the video from being converted to a degree greater than a preset level when converting the video so that each histogram is distributed in the target distribution range Parameter creation means;
A color conversion function creating means for creating a color conversion function using the conversion suppression parameter;
Video converting means for converting the video by applying the color conversion function to the video;
A video conversion apparatus comprising:   The video conversion apparatus according to claim 1, wherein the area dividing unit detects a face area.   The video conversion apparatus according to claim 1, wherein the histogram creation unit creates a histogram of R component, G component, and B component for each region. A video conversion method performed by a video conversion device,
Storing the input video in a storage means;
Reading the video, dividing it into a plurality of object areas and storing it in a storage means;
Creating a histogram of each region in a desired color component;
Designating a target distribution range for each of the histograms;
Creating a conversion suppression parameter for preventing the contrast of the video from being converted to be larger than a preset level when converting the video so that each histogram is distributed in the target distribution range; ,
Creating a color conversion function using the conversion suppression parameter;
Applying the color conversion function to the video to convert the video;
A video conversion method characterized by comprising:   A video conversion program for causing a computer as the video conversion device according to any one of claims 1 to 3 to execute processing performed by the video conversion device.