JP2003087816A - White balance adjustment device, white balance adjustment program, white balance adjustment method, and digital camera - Google Patents

Abstract

(57) [Summary] [Problem] When there is a large portion of a specific color in an image or a captured image under a light source whose spectral distribution is biased toward a specific color, white balance adjustment is performed by the influence of these specific colors. Will cause color feria to fail. SOLUTION: A luminance component of an image is acquired and divided into bands,
A white balance correction coefficient for adjusting a white balance is calculated based on color component distribution information of an image within a predetermined band. Therefore, white balance adjustment can be performed without being affected by a specific color in the image or a light source biased to the specific color.

Description

Detailed Description of the Invention

[0001]

BACKGROUND OF THE INVENTION 1. Field of the Invention The present invention relates to a white balance adjusting device, a white balance adjusting program, a white balance adjusting method, and a digital camera.

[0002]

2. Description of the Related Art The human eye adjusts color components by chromatic adaptation, and a white object can be recognized as white even if the light source changes. However, in an image device such as a digital camera, since the image pickup device itself does not have a color adaptation function, color balance is conventionally adjusted by a technique called auto white balance. For example, if the color image data is R (red), G (green), B
When it is composed of three components of (blue), the gradation values of each of the three components are integrated in the entire image, and the respective integration ratios are set to R: G: B = 1: 1: 1. The gradation value of is corrected.

[0003]

In the above-mentioned conventional digital camera, the average color of the entire image is grayed out based on the hypothesis that when all the color components of the subject are added, it becomes an achromatic color. Since the correction is performed, if there are large areas of a specific color in the image, or if the image is captured under a light source with a spectral distribution biased toward the specific color, white balance adjustment will fail due to the effects of these specific colors. A color ferria will occur. The present invention has been made in consideration of the above problems, and a white balance adjustment device, a white balance adjustment program, and a white balance adjustment program capable of performing white balance adjustment without being affected by a specific color in an image or a light source biased to the specific color. A balance adjustment method and a digital camera are provided.

[0004]

In order to achieve the above object, the invention according to claim 1 is to divide an image into a plurality of reference regions composed of a plurality of parts, and to divide the image by the image dividing means. And a color component distribution information acquiring unit that acquires color component distribution information for a predetermined area that is a reference area and has less bias in color component distribution in the area compared to other reference areas, and the acquired color component distribution. A white balance control means for controlling the white balance by correcting the color component of the image based on the information is configured. In the invention according to claim 1 configured as described above, the image is divided into a plurality of reference regions composed of a plurality of parts, and the color component distribution information on any or a combination of the reference regions is acquired. The white balance is controlled by correcting the color components of the image based on the color component distribution information. Here, the color component distribution information acquisition unit acquires the color component distribution information for an area in which the deviation of the color component distribution in the area is smaller than that in other reference areas.

That is, the white balance is adjusted on the basis of the color components in the region where the deviation of the color component distribution is small,
The overall balance is not adjusted based on the color components of the entire image. Therefore, even if there is a large monochromatic region in the image, this region can be excluded, and color ferria can be prevented without being greatly affected by this monochromatic region. Here, the region where the bias of the color component distribution is small is
This is a comparison with other reference areas, and an area in which color components are absolutely not unevenly distributed, that is, an achromatic area is not selected and its color component distribution information is not acquired. Further, the reference area does not have to be a continuous area, and may be composed of a plurality of parts extracted from the entire image. For example, a plurality of regions may be evenly extracted from the entire image, or a region having a luminance within a predetermined range may be extracted, and the reference region may be configured by the regions extracted based on various indexes. When the image is composed of three color components, the predetermined color may be any one of the color components or a predetermined color represented by a combination thereof.

According to a second aspect of the invention, when the image is represented by the first to third color components, the second color component and the third color component other than the reference first color component are used. Color component distribution information acquisition means for acquiring color component distribution information of an area in which color components are relatively evenly distributed compared to other areas in the image, and the image based on the acquired color component distribution information And a white balance control means for controlling the white balance by correcting the color component of.

In the invention according to claim 2 configured as described above, the image is represented by the first to third color components, and the first color component is used as a reference in controlling the white balance. Then, the color component distribution information is acquired for a region in which the other second color component and the third color component are relatively evenly distributed with respect to the reference first color component compared to other regions in the image. Then, the color component of the image is corrected based on the color component distribution information of this area.

That is, since the white balance is adjusted based on the color component distribution information of the area where the second color component and the third color component are not biased with respect to the reference first color component, the white balance is adjusted. It is possible to prevent color ferria without being greatly affected by its existence. Here, the first color component to the third color component can be realized by various color components such as RGB components and CMY components, but it is preferable that the RGB components are close to the color distribution that a human cone feels. Further, it is preferable to use the G component, which occupies most of the luminance component, as the reference color.

Further, the invention according to claim 3 obtains the brightness component acquisition means for acquiring the brightness component of the image, and the color component distribution information of the image of the portion of the image where the brightness component is within a predetermined band. The color component distribution information acquisition means and the white balance control means for controlling the white balance by correcting the color components of the image based on the acquired color component distribution information are configured.

In the invention according to claim 3 configured as described above, the luminance component acquisition unit acquires the luminance component of the image, and the color component distribution information acquisition unit acquires the luminance component within a predetermined band. The color component distribution information of the image of is acquired. Then, the white balance control means controls the white balance by correcting the color components of the image based on the acquired color component distribution information.

That is, since the color component distribution information of the image of the portion where the luminance component is within the predetermined band is used, the color ferria can be prevented without being greatly affected by the large specific color of the entire image. You can In addition, in all the above-mentioned inventions, since color component distribution information is acquired, it is preferable to apply it to an image device that handles digital image data because it facilitates processing, but of course, for an image device that handles analog data such as a television. The present invention can also be applied.

Here, the brightness component acquisition means is only required to be able to acquire the brightness component of the image, and various configurations can be adopted. As an example of the configuration, in the invention according to claim 4, the luminance component acquisition means divides the dot matrix image data into predetermined sections and acquires the average value of the color components of each section as the luminance component. is there. In other words, recent image devices can handle a very large amount of pixel data such as millions of pixels. In such image devices, the luminance component is referred to for all dot matrix image data, and all of them are referred to. It takes a lot of calculation time to process the color component distribution information of
It also requires a great deal of buffer memory.

Therefore, it is preferable to thin out the reference data as long as the overall white balance can be adjusted. In the present invention, the image data is divided into predetermined sections and the average value of the color components of each section is obtained. ing. Further, when the image data includes data as a luminance component, the data may be used. However, when the image data includes color component data, the luminance component can be generally calculated from the color component data. . There are various formulas for calculating the luminance component, but if the average value of the color components is calculated as in the present invention, the calculation is very simple and can be easily executed. Of course, the luminance component is, for example, Y = 0.30R + 0.59G based on the RGB component.
It is also possible to calculate it as + 0.11B or the like.

Further, the luminance component acquisition means should be able to provide an index for extracting the pixels to be referred to for the white balance adjustment for each band. As a configuration therefor, in the invention according to claim 5, The brightness component acquisition means is configured to make a brightness component of an image into a histogram and divide the band into a predetermined brightness range.
That is, if the luminance component is formed into a histogram, the distribution of each level of the luminance component, for example, each gradation value can be grasped, and the band can be easily divided if the distribution of the luminance component is known. For example, it is possible to divide the band so that the number of pixels existing in each band is almost equal.

As the color component distribution information, various kinds of information that serves as a guideline for adjusting the white balance can be acquired. As an example, in the invention according to claim 6,
The color component distribution information acquisition means is configured to acquire a distribution ratio for each color component of the image. That is, when the image contains many specific color components, the white balance easily shifts. Therefore, if the distribution ratio is acquired for each color component, it becomes easier to compare the distribution ratios of the color components, and the white balance can be easily adjusted. can do.

Here, the color component distribution information is acquired for a predetermined reference area, or the second color information is obtained for the reference first color component.
If a region in which the color component and the third color component are relatively evenly distributed is acquired, or if it is acquired for each predetermined band, color ferria can be prevented without being significantly affected by the specific color of the entire image. can do. Also in this case, various color components such as RGB components and CMY components can be adopted as the color components, but it is preferable that the RGB components are close to the color distribution perceived by a human cone. Further, the distribution ratio may be any ratio as long as it reflects the distribution of color components, and various modes can be adopted. For example, it may be an integrated value of the gradation value or level of each color component, or an average value. Further, any ratio may be used as long as it is a ratio for color component distribution, and any color component may be a denominator or a numerator.

Further, in the invention according to claim 7, in the color component distribution information acquisition means, the image is the first color component to the third color component.
When expressed by color components, the first color component distribution information
"1" is acquired for the color component, the ratio of the distribution of the first color component and the distribution of the second color component is acquired for the second color component, and the distribution of the first color component and the third color for the third color component. It is configured to acquire the ratio with the distribution of the components.

That is, it is preferable to obtain the color component distribution information that can be used for adjusting the white balance as the distribution ratio, and to obtain the ratio of the distribution of the first color component and the second color component, the first The relative distribution of the second color component based on the color component can be grasped, and at the same time, the relative correction amount of the second color component can be grasped. The same applies to the third color component, and if the ratio between the distribution of the first color component and the amount of the third color component is acquired, the relative distribution of the third color component based on the first color component can be grasped. can do.

Therefore, "1" for each of the first color components in the entire image, "the ratio of the distribution of the first color component to the distribution of the second color component" for each of the second color components, the third
When each of the color components is multiplied by the “ratio of the distribution of the first color component and the distribution of the third color component”, the adjustment for reducing the bias of the distribution of the color components, that is, the white balance can be performed.

Further, in the invention according to claim 8, the white balance control means is configured to average the distribution ratios for at least two or more luminance bands and use it for determining a correction amount for each color component. That is, without referring to the color component distribution information for the entire image, reference is made to the color component distribution information for a predetermined reference region, a region where the distribution ratio of color components is uniform, or a region where the luminance component is within a predetermined band. In controlling the white balance, it is possible to adopt various guidelines indicating how to acquire the color component distribution information. As an example, by averaging the distribution ratios for at least two or more luminance bands, it is possible to compensate for the deviation of the color component distribution over the entire luminance range, and to use this distribution ratio as a coefficient for each color component. If the correction amount is determined by the white balance control, the white balance control can be performed while compensating the deviation of the color component distribution.

As a more specific example, there is a method of compensating for a color gamut shift of an image device. That is, when the color gamut of the image device is considered in the Luv space, the color gamut is generally substantially symmetrical with respect to the L axis, but the color gamut expands in the opposite direction in the high-luminance portion and the low-luminance portion. Therefore, by averaging the color component distribution ratios in the high-luminance region and the low-luminance region, it is possible to compensate for the deviation of the color gamut along the luminance axis. Further, it is not necessary to average the distribution ratio only once, and it is also possible to average the distribution ratios for many areas and adopt the most preferable average value to determine the correction amount for each color component. Is.

Furthermore, in the invention according to claim 9 as a concrete configuration example for averaging, the white balance control means, when the luminance band is divided into N, the distribution ratio of the band i and the band N + 1-i. The distribution ratio of and is averaged. That is, the luminance band is divided into N (N is a positive integer)
Then, the band number i
When (i is a positive integer) is given, if the distribution ratio of the band i and the distribution ratio of the band N + 1-i are averaged, the two bands at the both ends of the luminance band are averaged, and each of the two ends of the luminance band is averaged. The bands can be sequentially averaged in such a manner that the second two bands are averaged, and the color component distribution ratios of the color gamut of the image device are shifted to the opposite side with respect to the L axis. It can be averaged and the deviation of the color gamut can be compensated.

If the correction amount of the color component of the entire image is determined by using the color component distribution ratio as a coefficient for each color component as described above, the white balance control can be performed while compensating the deviation of the color component distribution. In the invention according to claim 10 as an example of a configuration suitable for obtaining a preferable color component distribution ratio for controlling the white balance, the white balance control means sets the value close to 1 in the distribution ratio to the white balance correction. Is obtained as a coefficient for

That is, the distribution ratio is a coefficient for adjusting the white balance, but when a plurality of distribution ratios are calculated by considering the divided bands, the distribution ratio becomes 1
If a value that is far from is used as the coefficient, the color balance will be changed significantly when multiplied as a coefficient of arbitrary image data.
Therefore, there is a great possibility that color ferria will be caused.
Therefore, if a distribution ratio close to 1 is adopted and the correction amount by the coefficient is suppressed, the color rarely occurs.

Also, selecting a distribution ratio close to 1 is equivalent to selecting a small correction amount, but even if the correction amount is small, it works well as a white balance adjustment. In many cases, it is a preferable coefficient.
Specifically, it is often the case that the value closest to 1 is obtained as the most preferable coefficient, but of course, the value closest to 1 is not essential, and may be the value closest to the second.
Various configurations can be adopted, such as acquiring a value close to 1 within a range not exceeding a predetermined threshold value. Further, it is preferable to acquire the distribution ratio averaged as described above that is close to 1.

Further, the invention according to claim 11 is arranged such that the white balance control means controls the white balance by multiplying each color component of the dot matrix image data forming the image by the coefficient. That is, as described above, the distribution ratio is a coefficient for adjusting the white balance, so when the image is represented by dot matrix image data,
By multiplying each color component of the image data by the above coefficient, the white balance can be easily controlled. Further, it is possible to prevent the occurrence of color ferria with a very high probability.

By the way, such a white balance adjusting device may exist as a single device or may be used in a state of being incorporated into a certain device. The idea of the invention is not limited to this, and various white balance adjusting devices may be used. Aspects are included.
Therefore, it can be appropriately changed, such as software or hardware. As an example of embodying the idea of the invention, it corresponds to a case where it is software of a white balance adjusting device, and the invention according to claims 12 to 14 has a configuration corresponding to each function for causing the computer to implement the white balance adjusting device. . Claim 4
It goes without saying that the program is also effective as a program corresponding to claim 11.

Of course, the recording medium of the software is
It may be a magnetic recording medium or a magneto-optical recording medium, and the same can be applied to any recording medium developed in the future. In addition, the duplication stage of the primary duplication product, the secondary duplication product, and the like is absolutely the same. In addition, although the medium is not the above-mentioned medium, the present invention is used even if the communication line is used as a supply method. Further, even when a part is software and a part is realized by hardware, there is no difference in the idea of the invention, and a part is stored on a recording medium and appropriately stored as necessary. It may be in a form that can be read.

In addition, such a white balance adjusting program naturally has an invention in its procedure in advancing the processing in accordance with such control, and is applicable as a method. Easy to understand. Therefore, the invention according to claims 15 to 17 has a configuration corresponding to the white balance adjusting method implemented by the white balance adjusting apparatus. That is, there is no difference in that the method is not limited to the actual device and is effective as the method. Of course, the above claim 4
~ It is also effective as a method corresponding to claim 11. Further, the present invention can be applied to a specific device, and the present invention can be realized as a digital camera equipped with each unit capable of executing white balance adjustment as in claim 18.

[0030]

As described above, claim 1, claim 1
In the second aspect of the invention, it is possible to provide a white balance adjusting device, a white balance adjusting program, and a white balance adjusting method capable of preventing color ferria without being significantly affected by a single color portion. In addition, claim 2, claim 13, claim 16
According to the invention, it is possible to provide a white balance adjusting device, a white balance adjusting program, and a white balance adjusting method capable of preventing color ferria without being greatly affected by the presence of a specific color.

Further, claim 3, claim 14, claim 1
7. In the invention according to claim 18, a white balance adjusting device, a white balance adjusting program, a white balance adjusting method, and a digital device capable of preventing color ferria without being greatly affected by a large specific color of the entire image. A camera can be provided. Further, according to the invention of claim 4, it is possible to reduce resources.

Further, according to the invention of claim 5,
The luminance component can be easily band-divided. Further, according to the invention of claim 6, the white balance can be easily adjusted by comparing the distribution ratios of the respective color components of the image. Further, according to the invention of claim 7, it is possible to acquire the color component distribution information that can be directly used for adjusting the white balance. further,
According to the invention of claim 8, it is possible to adjust the white balance while compensating for the deviation of the color component distribution over the entire luminance range.

Further, according to the invention of claim 9,
The color gamut shift can be easily compensated. Further, according to the invention of claim 10, it is possible to adjust the white balance while significantly reducing the occurrence rate of color ferria. Further, according to the invention of claim 11, white balance can be easily controlled by multiplication of coefficients.

[0034]

BEST MODE FOR CARRYING OUT THE INVENTION Here, embodiments of the present invention will be described in the following order. (1) Schematic configuration of digital still camera: (2) Schematic of image processing: (3) Configuration for white balance measurement and control: (4) White balance measurement and control process: (4-1) Luminance component acquisition process : (4-2) Color component distribution information acquisition process: (4-3) White balance control process: (4-3-1) Correction corresponding to light source type:

(1) Schematic configuration of digital still camera: FIG. 1 is a block diagram showing the schematic configuration of a digital still camera to which the white balance control of the present invention is applied. The digital still camera 10 is composed of an optical unit 20 and a control unit 30 having a CPU 32 as a core. The optical unit 20 includes an optical lens system 21, an autofocus mechanism 22, a distance measuring unit 23, and an autofocus controller 24. The autofocus controller 24 controls the distance measuring unit 23 based on a control signal from the controller 25. While measuring the distance to the subject, the optical lens system 21 is driven by the autofocus mechanism 22 to focus. A subject image is formed by the optical lens system 21 on the image pickup surface of the CCD 26 as an image pickup element.
The CCD 26 is composed of a single plate having 1800 × 1200 pixels. Since it is composed of a single plate, green (G), magenta (M), yellow (Y), and cyan (C) color filters are sequentially formed for 2 × 2 pixels. The shutter is provided with a mechanical shutter as well as an electronic shutter, but the mechanical shutter is not shown.

In addition to this, the optical section 20 is also provided with a strobe 27, and emits a predetermined amount of light and a predetermined number of times in response to a drive signal from the controller 25. This allows
It has achieved shooting such as red-eye prevention and slow sync. An automatic gain controller (AGC) 2 that amplifies the analog electric signal output from the CCD 26
8 is provided, and an A / D converter 29 that converts the amplified analog electric signal into a digital value and outputs the digital value is provided. Of course, the optical unit 20 described above is merely an example of a general configuration of the digital still camera 10, and various modifications can be made.
For example, the optical lens system 21 may be provided with a zoom lens, or may be an inexpensive one that does not have an autofocus function as a fixed focus.

The control unit 30 is also provided with a bus 31, and the bus 31 has a CPU 32, a ROM 33, and a RAM 3.
4 and the A / D converter 29 are connected. CP
The U 32 executes the firmware written in the ROM 33 to execute control of the optical unit 20 and calculation of image processing. At that time, the RAM 34 functions as an image area 34a for storing image data and performs calculation processing. Or function as a work area 34b. CPU32
When controlling the optical unit 20, the control unit 25 outputs a control signal to the controller 25, and the controller 25 generates and outputs an appropriate control signal to each component circuit and the like.

The control section 30 also has a function as a man-machine interface, and is provided with an operation panel 35 on which operation buttons and the like are arranged, and an LCD panel 36 for displaying photographed images and operation instructions, and the CPU 32 is operated. The operation of the panel 35 is monitored, and corresponding control is executed while receiving the operation as appropriate. The display on the LCD panel 36 is R
When the CPU 32 writes predetermined data to the video RAM area 34c assigned to the AM 34, the display controller included in the LCD panel 36 appropriately reads the data and displays it. Then, the image data that is photographed and generated through predetermined image processing is written to the flash memory card 37b, which is an external memory, via the I / O 37a, and is read from the flash memory card 37b as necessary.

The configuration of the control unit 30 is also a typical example of this type of digital still camera 10, and various changes can be made. For example, the type of the external memory can be easily replaced with another type, and even if the interface with the removable memory is not provided, the U
The image data may be output to the computer via the SB interface. LCD as display
Another LCD display may be provided in association with the panel 36, and the CPU 32 may have a plurality of CPs with different control targets.
U may be provided.

The image picked up by the optical unit 20 is the CCD 26
After being output as an analog electric signal from the AGC 28
After that, it is converted into digital data by the A / D converter 29, and is further recorded in the image area 34a of the RAM 34 via the bus 31. Then, the CPU 32 executes predetermined image processing to convert the JPEG image data into the JPEG image data, which is written in the flash memory card 37b.

(2) Outline of image processing: FIG. 2 shows the main flow of this image processing. Although detailed processing is executed as will be described later in detail, the processing is generally performed according to this flow. First, this outline will be described. In step S102, defective pixel interpolation is executed. A defective pixel is unavoidable in the CCD 26 having approximately 2 million pixels, and if the defective pixel has a predetermined reference value or less, it is supplied as a good product. For this reason, for defective pixels within the range of non-defective products, only 5 × 5 pixels based on the same color
Compensation is performed using a pixel median filter. That is, assuming that the defective pixel is a cyan pixel, the output values of the 5 × 5 cyan pixels located around the defective pixel are arranged in order, and the median value is set as the defective pixel value. The defective pixel itself has already been detected by the optical unit 20.

In the next step S104, the white balance is measured. Type of light source at the time of shooting and CCD2
The white balance may be shifted depending on the combination of 6 and the color filter. In the present embodiment, the white balance is measured in step S104 for each shooting, and the result is reflected to reduce the deviation. In step S110, white balance is controlled. When measuring the white balance, the image data of 113 × 75 pixels selected in the thinning process is used to measure the bias of the R component and the bias of the B component based on the G component in the RGB color system. The CCD 26 is provided with a color filter of a complementary color system, and is controlled on the basis of the complementary color system.

In this embodiment, three types of images are generated.
The first is a confirmation image, the second is a thumbnail image, and the third is a main image. The confirmation image is an image to be displayed on the LCD panel 36 immediately after shooting.
It is composed of image data of × 240 pixels. The thumbnail image is recorded as image data together with the main image, and is composed of image data of 160 × 120 pixels. The main image can be selected from three sizes depending on the operator's selection, and is an E-mail image of 720 × 480 pixels,
1800 × 1200 pixels called Print image or Hy
It is one of the high resolution 2160 × 1440 pixels called pict2.

In FIG. 2, steps S106 to S114 are displayed surrounded by a broken line, and at least these processes are performed for each of the three types of generated images. In step S106, data interpolation is performed. CC
D26 is composed of a single plate and has 2 million pixels, but the color information is not completed in all the pixels. That is, only cyan information about a pixel, magenta information about a pixel, yellow information about a pixel, and green information about a pixel. This data is called mosaic data. Data interpolation is a process for completing color information for each pixel by compensating for information about other colors that are insufficient for each pixel. The lacking information is the average value of the color information included in the 8 pixels surrounding the target pixel. For example, two yellow pixels (Y1, Y2), two magenta pixels (M1, M2), and four green pixels (G1 to G4) around the cyan pixel (C1).
Exists.

Therefore, the color information (C,
M, Y, G) is calculated as C = C1 M = (M1 + M2) / 2 Y = (Y1 + Y2) / 2 G = (G1 + G2 + G3 + G4) / 4.

In step S108, R is changed by changing the color system.
Convert to GB image data. There is also a general formula for conversion, but instead of using a simple general formula,
Conversion is performed using a tuned lookup table or a determinant. After conversion into RGB image data, in step S110, as described above, R
Control for reducing the bias of the R component and the bias of the B component in the GB color system is executed. In the next step S112, tone curve correction is performed. Tone curve correction is also called so-called γ correction. At this time, while converting 10-bit image data into 8-bit image data for each component, noise reduction and gradation in the white side region and the black side region are further performed. We also adjust the γ curve itself to maintain The tone curve correction itself uses a lookup table, and conversion is performed by referring to a 10-bit → 8-bit lookup table created by tuning in advance.

After that, since the JPEG image compression technique is used, color conversion is performed from the RGB color system to the YUV color system.
In order to distinguish the brightness from the yellow, the brightness will be displayed as Ym and the brightness directly derived from the mosaic color data will be displayed as the simple brightness Ys as described later. Color conversion from the RGB color system to the YUV color system is performed by using a general formula or a determinant. The general formula for converting the mosaic color data to the YUV color system is: Ys = 0.25 × (C + M + Y + G) U = C + M− (Y + G) V = −C + M + Y−G. The tuning and white balance control as described above are performed, and the correspondence relationship is not adopted.

Steps S116 and S118 are processes executed only when creating the main image. Noise removal is performed by applying a so-called low-pass filter only to UV components as color components. That is, if the color change is too steep, it acts slightly. For edge enhancement, a non-linear function value (for example, a coring function value) is calculated from the edge amount based on the brightness component only for the brightness component, and the non-linear function value is weighted to the original brightness component for calculation. Therefore, when the brightness value changes between adjacent pixels, the edge amount is calculated, and if the absolute value of the edge amount is large, the change amount is reflected in the brightness value greatly, and if the absolute value is small, the original brightness is reduced. The value is almost unchanged.

The above is the outline of the image processing. In the above procedure, however, the arithmetic processing time can be shortened by simultaneously performing a plurality of processes in the actual arithmetic processing. The photographing is completed by executing the above-mentioned image processing for each of the above-mentioned three kinds of images, and finally writing the JPEG-compressed image of the main image including the thumbnail image in the flash memory card 37b. In addition to this, in this digital still camera, it is possible to read image data from the flash memory card 37b and display it on the LCD panel 36, delete data, and execute various settings. The description is omitted because it is applicable.

(3) Configuration for white balance measurement and control: The white balance measurement in step S104 of the image processing and the white balance control reflecting the measurement result will be described in detail below. Figure 3
FIG. 3 is a block diagram showing a white balance measurement system and a control system realized by the configuration shown in FIG. 1. The luminance component acquisition module 33a, the color component distribution information acquisition module 33b, and the white balance control module 33c shown in FIG. 8 are executed by the CPU 32 by appropriately reading the program modules previously recorded in the ROM 33. Further, each module appropriately uses the data recorded in the image area 34a and the work area 34b of the RAM 34, and writes intermediate data as necessary to determine a matrix for controlling the white balance. White balance control is realized by multiplying the image data by this matrix.

The brightness component acquisition module 33a acquires the RAW data of the CCD recorded in the image area 34a and acquires the brightness component. Here, the luminance component acquisition module 33a uses the thinning process or G for reducing the calculation amount.
The MYC Raw data is converted into RGBYs data, the histogram processing and the band division processing of the histogram are performed, and the thinned data 34b1 and the RGBYs data 34 that are generated in each processing and are used as appropriate.
b2, histogram data 34b3, band data 34b
4 is recorded in the work area 34b of the RAM 34. Each processing will be described in detail later. If it is a digital still camera equipped with a primary color filter, GMY
Conversion from C data to RGB data is not necessary, but C
A correction process that approximates the RGB data of the CD to the characteristics of the sRGB data is necessary.

The color component distribution information acquisition module 33b
Integral data 34b5 of RGB color components and distribution ratio data 34b6, which is the ratio thereof, are acquired as the color component distribution information within the divided bands and suitable as an index for adjusting the white balance. From the obtained distribution ratio data 34b6, the white balance control module 33c uses correction coefficients R ′ _c and G that are coefficients used for white balance adjustment and that relatively correct the R component with respect to the G component. A correction coefficient B ′ _c (34b7) for relatively correcting the B component with respect to the component is obtained. Then, the correction coefficient is converted into a coefficient in the color space by the three physiological primary colors, and correction is performed in the color space to calculate the correction coefficients R _c and B _c , and the final correction coefficient is calculated based on the correction coefficient. The white balance control matrix 34b9 is calculated.

In the ROM 33, Estevez-
A conversion matrix A (33d1) for converting the RGB color components of the sRGB coordinate system into the RGB color components of the physiological three primary colors based on an adaptive transformation formula such as the Hunt-Pointer formula.
And an inverse transformation matrix A ⁻¹ for performing the inverse transformation of the transformation
(33d2) is recorded and used by the white balance control module 33c. The white balance control matrix 34 created as described above
By multiplying b9 by the CCD Raw data in the image area 34a converted into RGB color components, the white balance of the image can be controlled.

In this embodiment, the white balance correction coefficient is obtained in two steps. That is, the correction coefficients R ′ _c and B ′ _c are once obtained as the first step, and then the correction coefficients R _c and B _c are obtained as the second step. Since the correction coefficient is calculated in two steps in this way, the white balance control may fail due to the influence of a large monochromatic portion in the image, or the white balance control may fail when the light source is a specific light source. , Color ferria, etc. can be prevented, but each of the above steps functions as white balance control even if implemented independently, and each step is added to the white balance control of another method to adjust the white balance. It may be configured to control. For example, even the configuration in which the white balance control matrix 34b8 is calculated using the correction coefficients R ′ _c and B ′ _c calculated in the first stage functions as a white balance adjustment device.

(4) White balance measurement and control processing: The processing executed for measuring and controlling the white balance in the above configuration will be described in detail below. 4 and 5 are flowcharts showing white balance measurement and control. FIG. 4 corresponds to the first step, and FIG. 5 corresponds to the second step and the step of calculating the final white balance control matrix 34b9. Further, steps S200 to S220 of FIG. 4 correspond to the processing of the luminance component acquisition module 33a, and steps S225 to S2 of FIG.
50 corresponds to the processing of the color component distribution information acquisition module 33b, and steps S250 to S in FIGS.
275 and step S110 correspond to the processing of the white balance control module 33c. Here, a series of processes will be described while being divided into processes for each module.

(4-1) Luminance component acquisition processing: When the luminance component acquisition module 33a acquires the CCD Raw data in the image area 34a in step S200, thinning processing is performed on the CCD Raw data in step S205. . That is, the CCD Raw data is data having 1800 × 1200 pixels as described above, and it takes a very long time to execute the subsequent processing for all the pixels, and the RAM 34 has a very large free storage capacity. Therefore, pixels are thinned out as follows.

FIG. 6 is an explanatory diagram for explaining the thinning process. The square on the right side of the drawing is a schematic diagram of CCD Raw data, which is GMYC mosaic data of 1800 × 1200 pixels. In this embodiment, image data having a size of 1/16 of this mosaic data is used. First, the 16 × 16 pixel group (Gr1) at the upper left of the mosaic data in the figure is acquired. Pixel group Gr1
64 pieces of each color component data of GMYC are included therein, and the gradation value average of 64 pieces of data is taken for each color component. Then, these averages are used as the color component data for one pixel.
Let G | ₁ , | M | ₁ , | Y | ₁ , | C | ₁ .

Thereafter, similarly, a process of sequentially obtaining a pixel group of 16 × 16 pixels from the CCD Raw data and averaging the same is performed. As a result, the CCD Raw data is 1 /
It has 16 sizes, that is, gradation value data of 113 × 75 pixels. The gradation value data is stored in the work area 34b as thinned data 34b1. Of course, in this thinning-out process, the compression rate 1/16 is not an indispensable value, and can be appropriately changed according to the RAM 34, processing load, and the like. Further, although it is preferable that all the information of the CCD Raw data can be taken into account when the averaging is performed as described above, the vertical and horizontal directions of the CCD Raw data are 16
Various other methods can be adopted, such as thinning out so that one pixel for each color is left for each pixel.

In the present embodiment, the white balance adjustment is considered in the RGB space, and the above step S20 is performed.
Thinned-out data 34b1 after thinning processing is performed in 5.
Is the RGB component and the simple luminance Ys in step S210.
Is converted to. The conversion from the GMYC component to the RGB component uses the same conversion formula as the conversion formula in step S108. Since the conversion formula is tuned as described above, color conversion can be performed with high accuracy. It can be carried out. FIG. 7 shows the pixel data (RGBYs data 34b2) after the conversion in step S210. The pixel data after conversion has 11
3 × 75 pixels and RGBYs for each pixel
It is composed of four component gradation value data.

The brightness component acquisition module 33a acquires the brightness component of the pixel at this point. However, in this embodiment, the simple brightness Ys of each pixel is histogrammed for each gradation value in step S215 for later processing. The histogram data 34b3 is generated. FIG. 8 is a graph showing an example of this histogram. In the figure, since the gradation value pitch is fine, it is shown as a continuous curve, but the actual histogram is a discrete value. In the graph of the following histogram, which is also shown as a curve, it is actually a discrete value.

When the histogram is created in step S215, the histogram is created in step S220.
It is divided into luminance bands. FIG. 9 shows the same step S220.
FIG. 4 is an explanatory diagram illustrating a state of band division in FIG. The table in the upper part of the figure shows the work of determining the band, and the graph in the lower part shows the histogram in which the band is divided. When dividing the band, as shown in the table at the top, the histogram is integrated from the gradation value of the simple luminance Ys of “0”, and the integrated value exceeds 847 (about 113 × 75/10) Moves to the next band and repeats integration.

When such integration is repeated for 10 bands, the number of pixels belonging to each band becomes almost equal. That is, the areas of the respective bands shown in the graph of FIG. 9 are almost equal. The data after the band division is stored in the work area 34b as band data 34b4 capable of discriminating which pixel belongs to which band. Here, since the histogram is a discrete value, the number of pixels belonging to each band does not necessarily become exactly the same, but since there is a distribution ratio calculation process by the color component distribution information acquisition module 33b described later, the number of pixels The rigor of does not matter.
When the integrated value exceeds 847, the pixel having the gradation value may be included in only one band or may be included in both bands. Further, in this band division, it suffices that a suitable luminance band can be specified later by being used for white balance adjustment, and the number of divisions is 1
It is not essential that the number is 0, and the division method is not limited to the above.

(4-2) Color component distribution information acquisition processing: The color component distribution information acquisition module 33b uses the band data 3
Based on 4b4, color distribution information is acquired from a band preferable for white balance control. For this purpose, first, in step S225, the RGB components of the pixels belonging to the same band are integrated for each of the divided bands, and stored as RGB integrated data 34b5 in the work area 34b.
In step S230, as the distribution ratio, "integral value of G component / integral value of R component" and "integral value of G component / B" for each band.
The integral value of the component "is obtained and stored in the work area 34b as distribution ratio data 34b6. FIG. 10 is an explanatory diagram for explaining how to calculate the integral value and the distribution ratio. Since it is possible to determine which pixel belongs to which band in the band data 34b4, it is possible to easily know the RGB components of the pixels belonging to each band as shown in FIG.

From this RGB component value, the integral value is calculated by the following equation (1),

[Equation 1] The distribution ratio is calculated by the following formula (2).

[Equation 2] Here, R _ij , G _ij , and B _ij are gradation values for each color component, “i” corresponds to a band number, and “j” is a convenient number for distinguishing pixels. Also, Iri, Ig
i and Ibi are integral values for each band and each component, and Ri and
c and Bic are distribution ratios for each band.

Here, since the integrated value is the integrated value of the gradation values of the color components, it is a value that reflects how much the gradation value of each value is distributed in each band.
Further, calculating the distribution ratio using this integral value is equivalent to calculating the distribution ratio using the average gradation value for each color component for each band. The average gradation value for each color component of each band is obtained by adding the gradation values of the predetermined color components of the pixels belonging to the band and dividing by the number of pixels, and the number of pixels for each color component is the same in each band. However, when the distribution ratio is used, the number of pixels is canceled by the denominator and the numerator, and it is only the component obtained by adding the gradation values that contributes to the distribution ratio.

The distribution ratio Ric is "average value of G component for each band / average value of R component for each band", and distribution ratio Bic is "average value of G component for each band / average value of B component for each band" This distribution ratio is a coefficient having the property of adjusting the white balance. That is, when each of the RGB color components of a pixel is equal to the average gradation value of each color component of the band to which the pixel belongs, multiplying the distribution ratio Ric by the gradation value of the R component cancels out the denominator and averages the G component. It becomes equal to the value, and even if the distribution ratio Bic is multiplied by the gradation value of the B component, the denominator cancels out and becomes equal to the average value of the G component. Therefore, all the color components of the pixel are equal, and the pixel is achromatic.

In this way, the distribution ratio Ric and the distribution ratio Bic
Acts on the R component and B component of each pixel, and relatively changes the R component and B component so as to approach the reference while using the average value of the G component of the band as a reference, so that the white balance is It can be said that this is a coefficient for adjustment.
Therefore, it can be said that the white balance correction coefficient for each band is calculated up to step S230. In the present invention, the white balance of the entire image is adjusted with reference to the distribution ratio of the predetermined band, and the correction for obtaining a more preferable result is added regardless of the difference in the image content or the light source.

[0068] calculated upon obtaining a distribution ratio data at step S230, the average value R _'nc, B' of the set of distribution ratio according to the following formula in the color component distribution acquisition module 33b Step S235 (3) the _nc To do.

[Equation 3] Here, n is a group number of the band and is an integer of "1 to 5".

FIG. 11 is an explanatory diagram for explaining a manner in which two bands are combined for the average value _R'nc of the combination.
The upper graph shown in the figure shows a histogram divided into 10 bands as described above. In the above equation (3), since the averages for the nth band and the (11-n) th band are calculated, the bands at the both ends (1st and 10th) are combined as shown in FIG. And the second bands (2nd and 9th) from both ends are paired one after another. The same applies to the concept of the combination for _B'nc .

FIG. 12 is an explanatory diagram for explaining the concept of averaging by combining distribution ratios, and this figure shows the color gamut of the RGB color space mapped on the U-Y plane of the YUV color space. . In the figure, the color gamut of the RGB color space is biased in the negative U direction in the high luminance region and biased in the positive U direction in the low luminance region. The same applies to the case of mapping on the V-Y plane, in which the image is biased in the negative V direction in the high luminance region and is biased in the positive V direction in the low luminance region. That is, the high-luminance region and the low-luminance region have opposite properties. Considering this property with the above distribution ratio, a large difference occurs between the high-luminance region and the low-luminance region. On average, you can offset the difference,
The distribution ratio can be evaluated by removing the bias of the distribution ratio due to the shape of the color gamut. Note that the idea of averaging the distribution ratio so as to remove this bias is based on the RGB color space and YUV.
It is generated due to the relationship with the color space, and can be used in white balance adjustment for many image devices.

In step 235, the average value of the set of distribution ratios
Is calculated, R ′ is calculated in step S240._ncBut
It is determined whether it is smaller than "1", and the same step S240 is performed.
At R ' _ncWhen is determined to be less than "1"
R'in step S245_ncAgainst R '_ncof
Substitute the reciprocal. This step S240, step S2
The determination process of 45 is based on the determination result of step S240.
Nonetheless, for all of the group numbers 1 to 5, B '
_ncIs also independently executed. Processing here
Is R '_ncAnd B ’_ncFor all of the following, less than "1"
Otherwise, it is converting to a number larger than "1".
And the following evaluation function e_nConversion for discrimination by
And the original value is restored after the evaluation function
Be done.

[0072] (4-3) White balance control: In step S250, evaluation for the function _{e n,} substituted and each pair of R _'nc and B' _nc, the white balance control module 33c is the evaluation function The values of R ′ _nc and B ′ _nc of the set number _n that minimizes _en are determined as the white balance correction coefficients R ′ _c and B ′ _c of the first stage. The first-stage white balance correction coefficient also has the same property as the distribution ratio described above, and is a coefficient that can be a predetermined component of the white balance control matrix 34b8 shown in FIG. That is, R of any pixel in the image
The white balance of the image can be controlled by multiplying the matrix having the GB component by the white balance control matrix 34b8.

Here, the process of selecting the one having the smallest evaluation function is the process of selecting the one having the smallest difference between each of R ′ _nc and B ′ _nc and “1”. Will select the smallest one. In other words, in white balance adjustment, it is important to prevent color ferria, and if a large correction amount is used as the white balance correction coefficient, color correction often occurs due to large correction, but a small correction amount is used. If selected, the probability of occurrence of color ferria can be made extremely small. It can also be said that an area with a small correction amount is selected and an area with less biased color component distribution compared to other areas is selected. In this case, the luminance component acquisition module 33a corresponds to the image dividing means, and the process of selecting the one having the smallest evaluation function in the color component distribution information acquisition module 33b is performed in the region where the bias of the color component distribution is small. It corresponds to selecting. Further, the process of selecting the one having the smallest evaluation function is a process of selecting a region in which the R component and the B component are relatively evenly distributed with respect to the G component compared to other regions. Can also be said.

The Applicant has confirmed that the white balance correction coefficient determined by the above-described processing can very effectively adjust the white balance, but here, if color ferria can be prevented. Of course, the evaluation function can be appropriately changed by, for example, selecting the second evaluation function close to "1". At this time, for example, color ferria can be prevented by taking measures such as selecting the second and third closest ones within a predetermined range. Further, in the present invention, the white balance correction coefficient is calculated based on the data belonging to a part of the luminance band of the image, and the one with a small correction amount is selected as described above. Compared with the white balance adjustment that reflects the RGB integrated values of the entire image as described above, it is very unlikely that color ferria will occur.

(4-3-1) Correction corresponding to light source type:
As described above, it is possible to perform the white balance control with the white balance correction coefficient in the first step, but in the present embodiment, in order to make the appearance more similar, the processing after step S255 in FIG. 5 is further executed. There is. In step S255, the white balance correction coefficient of the gradation value "128" above with the following equation so that the white balance correction coefficient relative to the median first stage of the G component R _'c and B' _c To convert. R = 128 / R ' _c , G = 128, B = 128 / B' _c

In step S260, the coefficient RGB after the conversion is Estevez-Hu according to the following equation (4).
In the nt-Pointer formula, the space R'of the physiological three primary colors
Convert to G'B '.

[Equation 4]

Here, the matrix A is a matrix stored in the ROM 33, and converts the sRGB coordinates into the RGB coordinates of the physiological three primary colors. Further, the matrix A is generated by multiplying the matrix B and the matrix C, each matrix has the above-mentioned values, the matrix B is a conversion matrix for converting sRGB coordinates into XYZ coordinates, and the matrix C is It is a matrix for converting XYZ coordinates into RGB coordinates of physiological three primary colors.
In the above equation, italicized letters indicate a matrix. When the spatial data R′G′B ′ of the physiological three primary colors is obtained in step S260, the final white balance correction coefficient Rc,
Determine Bc. Rc = G '/ R', Bc = G '/ B'

In the processes of steps S255 to S265, the sRGB coordinates are once converted into the R'G'B 'coordinates of the physiological three primary colors, and the white balance correction coefficient is corrected in the space after the conversion (step S265). That is the case. By this processing, it is possible to bring the white balance closer to a good white balance according to the human appearance, and it is possible to perform white balance control that does not depend on the light source type. Figure 1
3, FIG. 14 is an explanatory diagram for explaining how the white balance correction coefficient is corrected by converting the physiological three primary colors.

FIG. 13 is a diagram showing the spectral distribution of an sRGB ideal light source (CIE standard light source D65), and FIG. 14 is a diagram showing the spectral distribution of physiological three primary colors. In the figure, the horizontal axis is the wavelength (nm) and the vertical axis is the relative value of the spectral distribution. 13 and 14, the curve having a peak near 450 nm is the B component and the curve having a peak near 540 nm is the spectral distribution of the G component.
A curve having a peak near 10 nm and around 580 nm in FIG. 14 is the spectral distribution of the R component. Since the white balance correction coefficient is multiplied by the R component and the B component respectively, the adjustment by the white balance correction coefficient is performed by the B component and the R component for the spectral distributions in FIGS.
Acts to enlarge or reduce the component curve.

Since the spectral distribution shown in FIG. 14 is a distribution for the three physiological primary colors, the way humans perceive colors is very close to this distribution, and the white balance correction coefficient is corrected in the RGB space having this distribution. Thus, the white balance can be controlled in a manner very similar to how humans perceive color. Specifically, in the spectral distribution shown in FIG. 13, the peak wavelength and peak intensity of the R component are larger than the peak wavelength and peak intensity of the R component of the spectral distribution shown in FIG.
Therefore, if the white balance is adjusted without performing the conversion in step S260, the correction for the R component becomes relatively large, and the R component is overemphasized or weakened too much. Therefore, by performing the conversion in step S260 and then correcting the white balance correction coefficient, it is possible to adjust the white balance so that the color becomes red closer to the appearance. Of course, the same applies to blue, which is close to what it looks like. Therefore, it is possible to determine the white balance correction coefficient so that the white balance is adjusted so that an appropriate white balance can be obtained even for an image captured with an incandescent lamp.

After the final white balance correction coefficients Rc and Bc are determined in step S265, step S2
70, the white balance correction coefficients Rc and Bc
Was the first row and the first column and third row third column components, the second row and second column of the component is "1", the inverse transformation matrix A ^-1 of the other components from the left matrix to "0" The white balance control matrix W (34b9) is generated by multiplying by the matrix A from the right, and the white balance control matrix W is multiplied by the RAM 34 in step S275.
It is saved in the work area 34b.

The inverse transformation matrix A ^-1 is expressed by the following equation (5).

[Equation 5] Also in the above formula, italicized letters indicate a matrix.

The calculation and storage of the white balance control matrix W corresponds to the white balance measurement, and as the processing of the digital still camera 10, thereafter, the processing returns to the flowchart shown in FIG. 2 and the processing of step S106 and thereafter is executed. Then, in step S110, CC
The white balance of each captured image is controlled by multiplying the image data obtained by converting the DRaw data into sRGB data by the white balance control matrix W.

That is, the white balance control matrix W is a matrix that acts to adjust the white balance of the image of the color image data represented by sRGB coordinates, and the input and output data are sRGB.
The data. The white balance correction coefficient is sRG
Since it is not a correction coefficient for B, the space is converted by the matrix A or the like so that the input / output data becomes sRGB data. FIG. 15 is an explanatory diagram for explaining the calculation by the white balance control matrix W, where R on the right side in the matrix calculation formula in the upper part.
GB is the input sRGB data, R ″ G ″ on the left side
B ″ is output sRGB data. Also, in the actual processing of the present embodiment, the white balance control matrix W is one 3 × 3 matrix obtained by pre-calculating three 3 × 3 matrices, but here, for convenience, three matrices are used. I will explain separately.

In FIG. 15, below the matrix arithmetic expression, the respective arithmetic stages by three matrices are shown. As described above, the input data is sRGB data. The matrix A is a matrix for converting the RGB color components of the sRGB coordinate system into the RGB color components of the physiological three primary colors. Therefore, when the input data is multiplied by the matrix A, the RGB color components of the physiological three primary colors are converted as shown in. It With respect to this conversion result, the white balance correction coefficients Rc, B
When the matrix including c as a component is multiplied, the white balance is adjusted with the RGB three color components of the physiological primary colors. When the RGB data after the adjustment is multiplied by the inverse conversion matrix A ⁻¹ , the image data after the white balance adjustment is converted into sRGB data as shown in. Therefore, in the image processing process of the digital still camera, the white balance is adjusted by multiplying the sRGB data by the white balance control matrix W, which is one 3 × 3 matrix.

As described above, in the present invention, the white balance correction for obtaining the luminance component of the image, dividing the band into bands, and adjusting the white balance based on the color component distribution information of the image within the predetermined band. Calculate the coefficient.
Therefore, the white balance adjustment can be performed without being affected by the specific color in the image or the light source biased to the specific color.

[Brief description of drawings]

FIG. 1 is a block diagram showing a schematic configuration of a digital still camera.

FIG. 2 is a flowchart showing a main flow of image processing.

FIG. 3 is a block diagram showing a white balance measurement system and a control system.

FIG. 4 is a flowchart showing white balance measurement and control.

FIG. 5 is a flowchart showing white balance measurement and control.

FIG. 6 is an explanatory diagram illustrating a thinning process.

FIG. 7 is a diagram showing pixel data after RGBYs conversion.

FIG. 8 is a graph showing an example of a histogram.

FIG. 9 is an explanatory diagram illustrating a state of band division.

FIG. 10 is an explanatory diagram illustrating a manner of calculating an integral value and a distribution ratio.

FIG. 11 is an explanatory diagram illustrating a manner in which two bands are combined.

FIG. 12 is an explanatory diagram for explaining the concept of averaging distribution ratios in combination.

FIG. 13 is a diagram showing a spectral distribution of an sRGB ideal light source.

FIG. 14 is a diagram showing spectral distributions of three physiological primary colors.

FIG. 15 is an explanatory diagram for explaining a calculation by a white balance control matrix.

[Explanation of symbols]

10 ... Digital still camera 20 ... Optical part 21 ... Optical lens system 22 ... Auto focus mechanism 23 ... Distance measuring part 24 ... Auto focus controller 25 ... Controller 26 ... CCD 27 ... Strobe 28 ... Auto gain controller (AGC) 29 ... A / D converter 30 ... Control unit 31 ... Bus 32 ... CPU 33 ... ROM 33a ... Luminance component acquisition module 33b ... Color component distribution information acquisition module 33c ... White balance control module 33d1 ... Conversion matrix A 33d2 ... Inverse conversion matrix A- ¹ 34 ... RAM 34a ... Image area 34b ... Work area 34b1 ... Thinned data 34b2 ... RGBYs data 34b3 ... Histogram data 34b4 ... Band data 34b5 ... RGB integration data 34b6 ... Distribution ratio data 34b7 ... Correction coefficient R ' _c, B _{'c 34b8} ... white balance control matrix 34B9 ... white balance control matrix 34c ... video RAM area 35 ... operation panel 36 ... LCD panel 37a ... I / O

   ─────────────────────────────────────────────────── ─── Continued front page    F-term (reference) 5C065 AA03 BB02 CC01 DD01 GG21                       GG22 GG23 GG32                 5C066 AA01 CA05 CA13 CA17 EA14                       EA15 GA01 GA05 GB01 JA01                       JA02 KD06

Claims (18)

[Claims] 1. An image dividing unit that divides an image into a plurality of reference regions composed of a plurality of parts, and a reference region divided by the image dividing unit that is a color within the region compared with other reference regions. A color component distribution information acquisition unit that acquires color component distribution information for a predetermined area having a small deviation in component distribution, and a white that controls the white balance by correcting the color components of the image based on the acquired color component distribution information. A white balance adjusting device comprising: a balance control means. 2. When the image is represented by the first color component to the third color component, the second color component and the third color component other than the reference first color component are different from those in the image. A color component distribution information acquisition unit that acquires color component distribution information of a region that is relatively evenly distributed compared to the region, and corrects the color components of the image based on the acquired color component distribution information A white balance adjusting device comprising: a white balance control means for controlling the balance. 3. A brightness component acquisition unit for acquiring a brightness component of an image, and a color component distribution information acquisition unit for acquiring color component distribution information of an image of a portion of the image where the brightness component is within a predetermined band. A white balance adjusting device comprising: a white balance control unit that corrects a color component of the image based on the acquired color component distribution information and controls a white balance. 4. The brightness component acquiring means divides the dot matrix image data into predetermined partitions and acquires the average value of the color components of each partition as the brightness components. The white balance adjusting device described in. 5. The brightness component acquisition means converts the brightness component of an image into a histogram and divides the band into a predetermined brightness range.
The white balance adjusting device according to any one of 1. 6. The white balance adjusting apparatus according to claim 1, wherein the color component distribution information acquisition unit acquires a distribution ratio for each color component of the image. 7. The color component distribution information acquisition means acquires “1” for the first color component as color component distribution information when the image is represented by the first color component to the third color component,
Obtaining the ratio of the distribution of the first color component and the distribution of the second color component for the second color component, and obtaining the ratio of the distribution of the first color component and the distribution of the third color component for the third color component. The white balance adjusting device according to any one of claims 1 to 6, characterized in that. 8. The white balance control means according to claim 6, wherein the white balance control means averages the distribution ratios for at least two or more luminance bands and uses the distribution ratios for determining a correction amount for each color component. The white balance adjustment device as described in any one. 9. The white balance control means, when the luminance band is divided into N, the distribution ratio of the band i and the band N + 1.
9. The white balance adjusting apparatus according to claim 8, wherein the distribution ratio of −i is averaged. 10. The white balance control means acquires a value close to 1 in the distribution ratio as a coefficient for white balance correction, according to any one of claims 6 to 9. White balance adjustment device. 11. The white balance control means controls white balance by multiplying each color component of dot matrix image data forming the image by the coefficient. 1
The white balance adjusting device according to any of 0. 12. An image data acquisition function for acquiring image data including information of color components forming an image, and an image is divided into a plurality of reference regions composed of image data of a plurality of parts based on the acquired image data. Image division function to divide and color to acquire color component distribution information for a reference area divided by the image division function and having a smaller deviation in color component distribution in the area compared to other reference areas A white color characterized by causing a computer to realize a component distribution information acquisition function and a white balance control function for controlling the white balance by correcting the color component information of the image data based on the acquired color component distribution information. Balance adjustment program. 13. An image data acquisition function for acquiring image data represented by first to third color components, and a first color component serving as a reference in an image composed of the acquired image data. On the other hand, a color component distribution information acquisition function for acquiring color component distribution information of an area in which the other second color component and the third color component are relatively evenly distributed compared to other areas in the image, A white balance adjusting program for causing a computer to realize a white balance control function of correcting a color component of the image based on the acquired color component distribution information and controlling a white balance. 14. An image data acquisition function for acquiring image data including information of color components forming an image, a brightness component acquisition function for acquiring a brightness component of an image based on the acquired image data, A color component distribution information acquisition function for acquiring, based on the image data, color component distribution information of an image of a portion where the luminance component is within a predetermined band, and a color component of the image based on the acquired color component distribution information. A white balance adjustment program that allows a computer to realize a white balance control function for correcting white balance and controlling white balance. 15. An image division step of dividing an image into a plurality of reference areas each composed of a plurality of parts, and a color of the reference area divided by the image division step as compared with other reference areas. A color component distribution information acquisition step of acquiring color component distribution information for a predetermined region having a small component distribution bias, and a white for controlling the white balance by correcting the color components of the image based on the acquired color component distribution information. A white balance adjusting method comprising: a balance control step. 16. When an image is represented by a first color component to a third color component, a second color component other than a first color component serving as a reference is used.
A color component distribution information acquisition step of acquiring color component distribution information of an area in which the color component and the third color component are relatively evenly distributed compared to other areas in the image; and the acquired color component distribution information A white balance control step of controlling the white balance by correcting the color component of the image based on the above. 17. A brightness component acquisition step of acquiring a brightness component of an image, and a color component distribution information acquisition step of acquiring color component distribution information of an image of a portion of the image where the brightness component is within a predetermined band, And a white balance control step of controlling the white balance by correcting the color components of the image based on the acquired color component distribution information. 18. A color image data acquisition unit for acquiring color image data in a dot matrix form in which an image picked up by a CCD element is expressed by gradation values of predetermined element colors, and the color image data is stored in predetermined sections. Luminance component acquisition means for dividing and dividing the average value of the color components of each section as a luminance component, histogram-forming the luminance component, and dividing the band into N regions to obtain a predetermined luminance range; To obtain the distribution ratio of each color component of the color image data, and calculate the distribution ratio of the band i and the band N + 1-i.
Color component distribution information acquisition means for averaging the distribution ratios of the colors and a value close to 1 in the averaged distribution ratios as a coefficient for white balance correction, and the color to be corrected in the color image data. A digital camera characterized in that white balance is controlled by multiplying a component by the above coefficient.