JP2010117884A - Image processing device, video display apparatus, imaging apparatus and image processing method - Google Patents

Abstract

<P>PROBLEM TO BE SOLVED: To provide an image processing device, a video display apparatus, an imaging apparatus and an image processing method for producing a natural image with inconspicuous boundaries even after different tone conversions are applied depending on image position. <P>SOLUTION: A tone conversion characteristic decision part 104 determines whether to apply a conversion based on tone conversion characteristics determined from statistics of an area where a pixel of interest belongs or apply conversion based on tone conversion characteristics determined from statistics of peripheral areas. A tone conversion part 105 applies tone conversion based on the determined better tone conversion characteristics. <P>COPYRIGHT: (C)2010,JPO&INPIT

Description

  The present invention relates to an image processing apparatus, a video display apparatus, an imaging apparatus, and an image processing method that perform gradation conversion.

Conventionally, video and images have been divided into a plurality of areas, and optimum gradation conversion has been performed for each divided area. However, when gradation conversion is performed for each area, there is a problem that the boundary between the areas is conspicuous.
With respect to this problem, in Patent Document 1, control is performed so that the correction value is continuously changed by weighting by distance at the boundary of the area so that the boundary is not conspicuous.
Further, in Patent Document 2, the gradation conversion characteristics are obtained from the statistics of an area wider than the area to be subjected to gradation conversion, so that there is not much difference in gradation conversion characteristics in adjacent areas.
JP 2004-287794 A JP 2008-92052 A

However, in Patent Document 1, since uniform control based on distance is performed, even if it is preferable not to perform weighting based on distance, there is a case where it is strongly influenced by the gradation conversion characteristics of the adjacent area. It was.
FIG. 13 is a diagram for explaining the problem of gradation conversion according to the conventional technique.
In the method of Patent Document 1, for example, the parasol part of the point P in FIG. 13 is a place near the boundary of the area, and thus is strongly influenced by the gradation conversion characteristics of the area on the left. In the example of FIG. 13, the area on the left is a single grassland area, and has gradation conversion characteristics suitable for grasslands. Therefore, when the gradation conversion characteristic suitable for the grassland strongly affects the point P that is a parasol part, there is a problem that the gradation conversion deviates from the gradation conversion characteristic suitable for the parasol part. .

In Patent Document 2, tone conversion characteristics are obtained from a statistic of an area wider than an area to be subjected to tone conversion. Therefore, if there is an object that is different from the object of the area to be converted in the area for which the statistic is obtained, it may be affected by the different object.
For example, when gradation conversion is performed based on the statistics of the broken line area around the point P in FIG. 13, grasses other than the parasol are included in the area, so that the gradation conversion characteristics suitable for the parasol part are included. There has been a problem that gradation conversion outside the range is performed.

  An object of the present invention is to provide an image processing device, a video display device, an imaging device, and an image processing method capable of obtaining a natural image without making the boundary noticeable even if gradation conversion is changed depending on the position in the image. It is.

  An image processing apparatus according to a first aspect of the present invention includes an area statistic calculation unit that divides an image into a plurality of areas and calculates a statistic regarding image data for each area, and the area statistic calculation unit includes: A gradation conversion characteristic calculation unit that calculates gradation conversion characteristics for each area using the calculated statistics for each area, and the area is further divided into a plurality of smaller blocks, and an image is obtained for each small block. Small block statistic calculating means for calculating statistics relating to data, and gradation conversion characteristics used for gradation conversion for each small block based on the statistics for each small block calculated by the small block statistic calculating means Is selected from one of the gradation conversion characteristics of the area to which the small block belongs and the gradation conversion characteristics of the area adjacent to the area to which the small block belongs. Comprising a determining unit, and a gradation conversion unit that performs gradation conversion for each of the small block using the gradation conversion characteristic gradation conversion characteristic determination means has determined for each of the small blocks.

  An image display apparatus according to a second aspect of the present invention includes an input unit that receives or inputs video data, and an image according to the first aspect of the present invention that performs image processing on the video data obtained by the input unit. A processing device; and display means for displaying video data processed by the image processing device.

  An imaging apparatus according to a third aspect of the present invention includes an imaging unit that captures a subject image, and an image processing apparatus according to the first aspect of the present invention that performs image processing on image data obtained by the imaging unit. Is provided.

  An image processing method according to a fourth aspect of the present invention divides an image into a plurality of areas, calculates a statistic regarding image data for each area, and uses the calculated statistic for each area. A gradation conversion characteristic is calculated every time, the area is further divided into a plurality of small blocks, a statistic about image data is calculated for each small block, and based on the calculated statistic for each small block The gradation conversion characteristic used for gradation conversion for each small block is selected from one of the gradation conversion characteristic of the area to which the small block belongs and the gradation conversion characteristic of the area adjacent to the area to which the small block belongs. The gradation conversion is performed for each small block by using the gradation conversion characteristics determined and selected for each small block.

  According to the present invention, the area is divided into smaller blocks, and it is determined for each block whether the correction is performed using the correction value of the own area or the correction value of the surrounding area. Therefore, even if it is near the boundary, if it is desirable to correct with the correction value of the own area, the correction is performed with the correction value of the own area. A natural image can be obtained.

The best mode for carrying out the present invention will be described below with reference to the drawings.
In addition, each figure shown below is the figure shown typically, and in order to make an understanding easy, the magnitude | size and shape of each part are exaggerated suitably.
In the following description, specific numerical values, configurations, operations, and the like are shown and described, but these can be changed as appropriate.

(First embodiment)
FIG. 1 is a diagram showing a first embodiment of a video display device using an image processing device according to the present invention.
The video display apparatus according to the present embodiment is a television receiver including a video input unit 1, an image processing unit 100, a system control unit 2, an operation unit 3, a recording unit 4, and a display unit 5.
The video input unit 1 includes a broadcast receiving unit 1a and an external input unit 1b.
The broadcast receiving unit 1a receives a television broadcast wave transmitted from a broadcast station via an antenna (not shown), decodes it as necessary, and converts it into a video signal that can be processed by the image processing unit 100. The video signal acquired by the broadcast receiving unit 1a is sent to the image processing unit 100.
The external input unit 1b is acquired from a video reproduction device (not shown) or the like, and converts it into a video signal that can be processed by the image processing unit 100 as necessary. The video signal acquired by the external input unit 1b is sent to the image processing unit 100.
The video input unit 1 switches between a video signal from the broadcast receiving unit 1 a and a video signal from the external input unit 1 b in accordance with an instruction from the system control unit 2, and sends the video signal to the image processing unit 100.

The image processing unit 100 performs image processing on the video data acquired by the video input unit 1 in response to an instruction from the system control unit 2.
In the present embodiment, the image processing unit 100 corresponds to the image processing apparatus according to the present invention.
The system control unit 2 is a control unit that controls the entire video display device, and includes a microcomputer.
The operation unit 3 includes operation buttons for performing various settings, and a signal corresponding to the operation is sent to the system control unit 2.
The recording unit 4 records video data on a recording medium such as a hard disk device or a memory card.
The display unit 5 displays characters, images, and the like according to instructions from the system control unit 2. The display unit 5 is configured by, for example, an LCD (Liquid Crystal Display).
In FIG. 1, image data and the like are shown to be sent to the recording unit 4 and the display unit 5 via the system control unit 2, but these are sent directly to each unit according to instructions from the system control unit 2. You may be made to do.

FIG. 2 is a block diagram showing the image processing unit 100 in more detail.
The image processing unit 100 of the present embodiment includes an area statistic calculation unit 101, a gradation conversion characteristic calculation unit 102, a small block statistic calculation unit 103, a gradation conversion characteristic determination unit 104, and a gradation conversion unit 105, and an image Of the 8 bits of each of the signals Y, Cb, and Cr, gradation conversion is performed on the Y signal. Here, Y is a luminance signal, and Cb and Cr are color difference signals.

The area statistic calculation unit 101 divides one screen into 15 areas of 5 × 3, and acquires a luminance histogram for each divided area.
The gradation conversion characteristic calculation unit 102 obtains a black side local MAX gradation and a white side local MAX gradation for each area from the luminance histogram acquired for each area. The black side local MAX gradation is the peak gradation on the lowest gradation side in the luminance histogram, and the white side local MAX gradation is the peak gradation on the highest gradation side. Here, the terms “low gradation” and “high gradation” respectively indicate that the signal value when the image signal (here, the Y signal because it is a Y signal) is divided into gradations is low (here, low luminance). And high (here, high luminance). Therefore, these do not indicate a low gradation with a small number of gradations and a high gradation with a large number of gradations.

A specific method for acquiring the black side local MAX gradation and the white side local MAX gradation will be described.
First, a gradation that satisfies the local MAX determination condition is searched. The local MAX determination condition includes the following three conditions, where hist [n] is the frequency of the gradation n and the threshold value lim1 and the adjustment value mag are constants.
Condition 1: hist [n]> lim1
Condition 2: hist [n] is the largest among hist [n-4] to hist [n + 4] Condition 3: (hist [n-4] + hist [n + 4]) × mag> hist [n]

Condition 1 is a condition for not detecting a low-frequency peak. Condition 2 is a condition for detecting a peak. Condition 3 is a condition for excluding a solid peak as in the graphic. When the subscript of “hist” becomes smaller than 0 or exceeds 255 under the conditions 2 and 3, the determination is performed by substituting with the frequency of the folded gradation.
A gradation satisfying these three conditions is a local MAX gradation.
Of these local MAX gradations, the local MAX gradation that is in the range of gradations 0 to 128 and is on the lowest gradation side is defined as the black-side local MAX gradation. When there is no local MAX gradation in the range of gradation 0 to 128, the gradation 128 is set as the black-side local MAX for convenience.
Further, the local MAX gradation that is in the range of gradations 192 to 255 and that is on the highest gradation side is defined as the white-side local MAX gradation. When there is no local MAX gradation in the range of the gradations 192 to 255, the gradation 192 is set as the white side local MAX for convenience.
FIG. 3 is a diagram illustrating an example of a luminance histogram for explaining a method of acquiring the black side local MAX gradation and the white side local MAX gradation.
For example, in the luminance histogram as shown in FIG. 3, the gradation 24 is the black local MAX gradation and the gradation 232 is the white local MAX gradation.

  When the black side local MAX gradation and the white side local MAX gradation are obtained, gradation conversion characteristics are obtained based on them. In the first embodiment, 256 black-side conversion curves for specifying conversion characteristics of 0 to 128 gradations, white side for specifying conversion characteristics of 192 to 255 gradations in a ROM or nonvolatile RAM (not shown) in advance. 256 conversion curves are stored. The black side local MAX gradation value corresponds to the black side conversion curve number, and the white side local MAX value corresponds to the white side conversion curve number. Therefore, the black side conversion curve of the black side local MAX gradation value and the white side conversion curve of the white side local MAX value are the gradation conversion characteristics of the area. The conversion characteristics of 129 to 191 gradations that are not specified are obtained by linearly interpolating a black side conversion curve and a white side conversion curve by a gradation conversion characteristic determination unit 104 described later.

FIG. 4 is a diagram illustrating an example of a black side conversion curve and a white side conversion curve.
For example, when the black side local MAX gradation is 24 and the white side local MAX gradation is 232, the black side conversion curve corresponding to the black side local MAX gradation 24, the white side local MAX gradation 232, as shown in FIG. Select the white side conversion curve corresponding to.

The small block statistic calculation unit 103 further divides the area further to obtain an evaluation value. The number of divisions in the present embodiment is 100 small blocks of 10 × 10. An average luminance is obtained for each small block, and is used as an evaluation value.
The gradation conversion characteristic determination unit 104 determines gradation conversion characteristics for each small block. At this time, the gradation conversion characteristic determination unit 104 includes a black-side conversion curve and a white-side conversion curve, a black-side local MAX gradation, a white-side local MAX gradation, and an area obtained by the gradation conversion characteristic calculation unit 102. The evaluation value for each small block obtained by the small block statistic calculation unit 103 is used.

FIG. 5 is a flowchart showing the gradation conversion characteristic determination operation for each small block.
A method for determining gradation conversion characteristics for each small block will be described with reference to the flowchart of FIG. In FIG. 5, dc means a black side conversion curve, and wc means a white side conversion curve.

  First, in STEP 401, the evaluation value of the small block is confirmed. When the evaluation value is a black side gradation of 0 to 144, the process proceeds to STEP 402 in order to determine the black side conversion curve. If the evaluation value is other than 0 to 144, the process proceeds to STEP 406.

In STEP 406, when the evaluation value is 176 to 255, the process proceeds to STEP 407 in order to determine the white side conversion curve. If the evaluation value is other than 176 to 255, the gradation conversion characteristic of the own area is used as it is, and the process proceeds to STEP 411.
Here, the self area indicates an area to which a certain small block belongs.

  In STEP 402, the evaluation value is compared with the black-side local MAX gradation of the own area. If the evaluation value is within the black side local MAX gradation ± 16 of the own area, it is desirable to use the black side conversion curve of the own area as it is. If it is not within the above range, proceed to STEP 403.

In STEP 403, the evaluation value is compared with the black local MAX gradation in the peripheral area.
FIG. 6 is a diagram illustrating the relationship between the self area, the peripheral area, and the small blocks.
As shown in FIG. 6, the peripheral areas are three areas excluding the own area where the distance from the center of the small block to the center of the area is short.
Returning to FIG. 5, if the evaluation value is within the black local MAX gradation of ± 16 in the peripheral area in STEP 403, it is desirable to use the black side conversion curve of the peripheral area, and the process proceeds to STEP 405. If it is not within the above range, proceed to STEP 404 and use the black-side conversion curve of its own area.

  In STEP 407, the evaluation value is compared with the white-side local MAX gradation of the own area. If the evaluation value is within the white-side local MAX gradation ± 16 of the own area, it is desirable to use the white-side conversion curve of the own area, and the process proceeds to STEP 409. If it is not within the above range, proceed to STEP 408.

  In STEP 408, the evaluation value is compared with the white side local MAX gradation in the peripheral area. If the evaluation value is within the white local MAX gradation of ± 16 in the peripheral area, it is desirable to use the white side conversion curve of the peripheral area, and the process proceeds to STEP 410. If it is not within the above range, proceed to STEP 409 and use the white-side conversion curve of its own area.

In STEP 404, both the black-side conversion curve and the white-side conversion curve are selected in the own area.
In STEP 405, as the black side conversion curve, the black side conversion curve of the peripheral area that satisfies the conditions in STEP 403 is selected. If a plurality of areas satisfy the condition, the black side conversion curve of the area where the evaluation value and the black side local MAX gradation are closest is selected. The white side conversion curve is selected from the local area.

In STEP 409, the black side conversion curve and the white side conversion curve are selected for the local area.
In STEP 410, the black side conversion curve is the same as that of the own area. As the white side conversion curve, the white side conversion curve of the peripheral area that satisfies the condition in STEP 408 is selected. If a plurality of areas satisfy the condition, the white side conversion curve of the area where the evaluation value and the white side local MAX gradation are closest is selected.
In STEP 411, both the black side conversion curve and the white side conversion curve are selected in the own area.

  When the black side conversion curve and the white side conversion curve are determined, the intermediate gray level conversion characteristics of the gray levels 129 to 191 are obtained by linearly interpolating the 128 gray level of the black side conversion curve and the 192 level of the white side conversion curve. These black-side conversion curve, white-side conversion curve, and intermediate gradation conversion characteristics become the gradation conversion characteristics of the small block.

The gradation conversion unit 105 receives the gradation conversion characteristic for each small block from the gradation conversion characteristic determination unit 104 and performs gradation conversion based on the received gradation conversion characteristic. The gradation conversion uses gradation conversion characteristics of four adjacent small blocks.
FIG. 7 is a diagram for explaining gradation conversion.
When gradation conversion is performed on the target pixel illustrated in FIG. 7, the small block (i, j), the small block (i, j + 1), the small block (i−1, j), and the small block whose block center is close to the target pixel. The conversion characteristics of four small blocks (i−1, j + 1) are used. The target pixel is subjected to gradation conversion with the conversion characteristics of each small block, and the result is interpolated by weighting according to the distance to obtain a gradation conversion result.

  As described above, in the present embodiment, tone conversion characteristics are obtained from statistics for each area, and evaluation values are obtained from small blocks obtained by further subdividing the area. Then, based on the evaluation value, it is determined whether to use the gradation conversion characteristic of the own area or the conversion characteristic of the surrounding area. Thus, if it is desirable to perform gradation conversion using the gradation conversion characteristics of the surrounding area, the gradation conversion characteristics between the areas can be recognized by performing gradation conversion using the gradation conversion characteristics of the peripheral area. It becomes possible to make it difficult. In addition, if it is desirable to perform tone conversion with the tone conversion characteristics of the local area, tone conversion is performed with the tone conversion characteristics of the local area, so even if there are different objects in the surrounding area, It is possible to perform gradation conversion without being affected.

(Second Embodiment)
The second embodiment is a form using a gradation conversion characteristic calculation method different from that of the first embodiment.
In addition, the same code | symbol is attached | subjected to the part which fulfill | performs the same function as 1st Embodiment mentioned above, and the overlapping description is abbreviate | omitted suitably.
FIG. 8 is a block diagram of the image processing unit 200 of the second embodiment.
In addition to the area statistic calculation unit 101 and the gradation conversion unit 105 similar to those of the first embodiment, the image processing unit 200 of the second embodiment includes a gradation conversion characteristic calculation unit 701, a small block statistic calculation unit 702, An area average value calculation unit 703 and a gradation conversion characteristic determination unit 704 are provided.

The gradation conversion characteristic calculation unit 701 first obtains a cumulative luminance histogram from the luminance histogram acquired for each area. The cumulative luminance histogram is different from the luminance histogram in which the frequency of the pixels of the gradation is plotted, and is a plot of the frequency obtained by totaling the pixels having the gradation or less.
FIG. 9 is a diagram illustrating an example of a cumulative luminance histogram.
FIG. 10 is a diagram showing the gradation of the cumulative luminance histogram shown in FIG. 9 as the input gradation and the normalized frequency as the output gradation.
For example, FIG. 9 shows a luminance histogram as shown in FIG. 2 as a cumulative luminance histogram. Then, the frequency of the obtained cumulative histogram is normalized to 255, and gradation conversion characteristics are calculated with the gradation as an input gradation and the normalized frequency as an output gradation as shown in FIG.

In the small block statistic calculating unit 702, the evaluation value is obtained by further dividing the area. Here, the number of divisions is 100 small blocks of 10 × 10. For each small block, an average value of Y, an average value of Cb, and an average value of Cr are obtained and set as a Y evaluation value, a Cb evaluation value, and a Cr evaluation value.
The area average value calculation unit 703 obtains a Y average value, a Cb average value, and a Cr average value for each area.

  A gradation conversion characteristic determination unit 704 determines gradation conversion characteristics for each small block. At this time, the gradation conversion characteristic for each area obtained by the gradation conversion characteristic calculation unit 701, the Y evaluation value, the Cb evaluation value, the Cr evaluation value, and the area average for each small block obtained by the small block statistic calculation unit 702 The Y average value, Cb average value, and Cr average value for each area obtained by the value calculation unit 703 are used.

FIG. 11 is a flowchart showing the gradation conversion characteristic determination operation for each small block.
A method for determining gradation conversion characteristics for each small block will be described with reference to the flowchart of FIG.

  First, in STEP 1001, the evaluation value of the small block is compared with the average value of the own area. When the Y evaluation value is within the Y average value ± 16, the Cb evaluation value is within the Cb average value ± 16, and the Cr evaluation value is within the Cr average value ± 16, the gradation conversion characteristic of the own area is used as it is. Since it is desirable, go to STEP1003. If any one is not within the above range, go to STEP1002.

  In STEP 1002, the evaluation value is compared with the area average value of the surrounding area. As shown in FIG. 6, the peripheral areas are three areas excluding the own area where the distance from the center of the small block to the center of the area is short. If the Y evaluation value, Cb evaluation value, and Cr evaluation value are within ± 8 of the Y average value, Cb average value, and Cr average value of the surrounding area (the range is narrowed to avoid erroneous determination), Since it is desirable to use the black side and white side conversion curves, the process proceeds to STEP 1004. If even one is not within the above range, the process proceeds to STEP 1003 to use the gradation conversion characteristics of the own area.

In STEP 1003, the gradation conversion characteristics of its own area are selected.
In STEP 1004, the gradation conversion characteristics of the peripheral area that satisfies the conditions in STEP 1002 are selected. If a plurality of areas satisfy the condition, the gradation of the area with the smallest sum of absolute values of the difference between the Y evaluation value and the Y average value, the Cb evaluation value and the Cb average value, and the Cr evaluation value and the Cr average value. Select conversion characteristics.
The selected gradation conversion characteristic becomes the gradation conversion characteristic of the small block.

  In this way, by performing the operation shown in FIG. 11, the comparison between the small block statistics and the average value of the area to which the small block belongs, and the area adjacent to the area to which the small block statistics and the small block belong. Comparison with the average value of. Then, the gradation conversion characteristic of the area where the compared values are close to each other is selected, and the gradation conversion characteristic for each small block is determined.

  As described above, in the present embodiment, an area average value is acquired, and based on the area average value, it is determined which area gradation conversion characteristic is used for the small block conversion characteristic. Thus, even with the gradation conversion characteristic calculation method that does not obtain the local MAX gradation in the gradation conversion characteristic calculation process as in the first embodiment, gradation conversion can be performed with the gradation conversion characteristics of a more desirable area.

(Third embodiment)
FIG. 12 is a diagram showing an imaging apparatus which is a third embodiment of video equipment using the image processing apparatus according to the present invention.
In the third embodiment, the image processing apparatus according to the present invention is used in an imaging apparatus such as a video camera or a digital still camera.
In addition, the same code | symbol is attached | subjected to the part which fulfill | performs the same function as 1st Embodiment mentioned above, and the overlapping description is abbreviate | omitted suitably.

The imaging apparatus according to the present embodiment includes a photographic lens 11, an imaging element 12, an image processing unit 100, a system control unit 2, an operation unit 3, a recording unit 4, and a display unit 5.
The photographing lens 11 is a photographing optical system that forms a subject image on the image sensor 12.
The imaging element 12 is a photoelectric conversion unit that converts an optical image into an electrical signal, and functions as an imaging unit that captures a subject image. The image sensor 12 of the present embodiment uses a CCD (Charge Coupled Device) image sensor. The image sensor may be another type of image sensor such as a CMOS (Complementary Metal Oxide Semiconductor) image sensor.

In the present embodiment, the image processing unit 100 performs image processing on video data acquired by the image sensor 12 in response to an instruction from the system control unit 2.
The operation of the image processing unit 100 is the same as in the first embodiment.
According to the present embodiment, more natural gradation conversion can be performed in the imaging apparatus.

(Deformation)
The present invention is not limited to the embodiment described above, and various modifications and changes are possible, and these are also within the scope of the present invention.

(1) In each embodiment, the peripheral area is three areas close to the own area, but the present invention is not limited to this. For example, two areas excluding an oblique area may be set as peripheral areas, or all eight areas adjacent to the own area may be set as peripheral areas.

(2) In each embodiment, although the example which uses the image processing apparatus of this invention for a video display apparatus or an imaging device was shown, it does not restrict to these. For example, it may be a recording device using a hard disk device, an optical disk device, or the like, and can be widely used for devices that handle images, such as game machines and mobile phones.

(3) In each embodiment, an example in which the luminance signal Y and the color difference signals Cb and Cr are handled has been described. However, the present invention is not limited to this. For example, the luminance signal Y and the color difference signals Pb and Pr may be handled. It may be one that handles color signals.

  The first embodiment to the third embodiment and modified embodiments can be used in appropriate combination, but detailed description thereof is omitted. Further, the present invention is not limited by the embodiments described above.

It is a figure which shows 1st Embodiment of the video display apparatus using the image processing apparatus by this invention. 2 is a block diagram illustrating the image processing unit 100 in more detail. FIG. It is a figure which shows the example of a luminance histogram explaining the acquisition method of a black side local MAX gradation and a white side local MAX gradation. It is a figure which shows the example of a black side conversion curve and a white side conversion curve. It is a flowchart which shows the determination operation | movement of the gradation conversion characteristic for every small block. It is a figure which shows the relationship between a self area, a peripheral area, and a small block. It is a figure explaining gradation conversion. It is a block diagram of the image processing part 200 of 2nd Embodiment. It is a figure which shows the example of a cumulative luminance histogram. FIG. 10 is a diagram illustrating gradations of the cumulative luminance histogram illustrated in FIG. 9 as input gradations and normalized frequencies as output gradations. It is a flowchart which shows the determination operation | movement of the gradation conversion characteristic for every small block. It is a figure which shows the imaging device which is 3rd Embodiment of the video equipment using the image processing apparatus by this invention. It is a figure explaining the problem of the gradation conversion by a prior art.

Explanation of symbols

DESCRIPTION OF SYMBOLS 1 Image | video input part 1a Broadcast receiving part 1b External input part 2 System control part 3 Operation part 4 Recording part 5 Display part 11 Shooting lens 12 Imaging element 100,200 Image processing part 101 Area statistic calculation part 102 Gradation conversion characteristic calculation part DESCRIPTION OF SYMBOLS 103 Small block statistic calculation part 104 Tone conversion characteristic determination part 105 Tone conversion part 701 Tone conversion characteristic calculation part 702 Small block statistic calculation part 703 Area average value calculation part 704 Tone conversion characteristic determination part

Claims (7)

An area statistic calculating means for dividing an image into a plurality of areas and calculating a statistic regarding image data for each area;
Gradation conversion characteristic calculation means for calculating gradation conversion characteristics for each area using the statistics for each area calculated by the area statistics calculation means;
A small block statistic calculating means for dividing the area into a plurality of smaller small blocks and calculating a statistic relating to image data for each of the small blocks;
Based on the statistics for each small block calculated by the small block statistic calculation means, the gradation conversion characteristics used for gradation conversion for each small block are the gradation conversion characteristics of the area to which the small block belongs, Gradation conversion characteristic determining means for selecting and determining any of gradation conversion characteristics of an area adjacent to the area to which the small block belongs;
Gradation conversion means for performing gradation conversion for each small block using the gradation conversion characteristics determined by the gradation conversion characteristic determination means for each small block;
An image processing apparatus comprising: The statistic calculated by the small block statistic calculating means is an average value of luminance signals;
The image processing apparatus according to claim 1. An area average value calculating means for calculating an average value related to image data for each area;
The gradation conversion characteristic determining means compares the statistics of the small block with the average value of the area to which the small block belongs, and the average of the area adjacent to the area to which the small block statistics and the small block belongs. Compare the values and select the tone conversion characteristics of the areas where the compared values are close to each other,
The image processing apparatus according to claim 1. The statistic calculated by the small block statistic calculating means is an average value of a luminance signal and an average value of a color difference signal or a color signal for each small block,
The statistic calculated by the area average value calculating means is an average value of luminance signals and an average value of color difference signals or color signals for each area,
The image processing apparatus according to claim 3. Input means for receiving or inputting video data;
The image processing apparatus according to any one of claims 1 to 4, wherein image processing is performed on video data obtained by the input unit;
Display means for displaying video data processed by the image processing device;
A video display device comprising: Imaging means for capturing a subject image;
The image processing apparatus according to any one of claims 1 to 4, wherein image processing is performed on image data obtained by the imaging unit.
An imaging apparatus comprising: Divide the image into a plurality of areas, calculate the statistics for the image data for each area,
Calculate the gradation conversion characteristics for each area using the calculated statistics for each area,
The area is further divided into a plurality of small blocks, and a statistic about image data is calculated for each of the small blocks.
Based on the calculated statistics for each small block, the gradation conversion characteristics used for gradation conversion for each small block are adjacent to the gradation conversion characteristics of the area to which the small block belongs and the area to which the small block belongs. Select from the gradation conversion characteristics of the area to be
An image processing method for performing gradation conversion for each small block using gradation conversion characteristics determined for each small block.

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