JP2003092687A - Image processing apparatus, image processing method and medium for recording image processing program - Google Patents

Abstract

PROBLEM TO BE SOLVED: To provide an image processing apparatus, an image processing method and a medium for recording an image processing program capable of automatically correcting color reproducibility such as a so-called color slippage and totally correcting color balance. SOLUTION: At a step S102, a sample-count distribution of image data is found for each color component by applying a thinning technique on samples. At a step S116, a judgment as to whether or not analogy exists among the sample-count distribution of the color components is formed. A low degree of analogy is regarded as an indicator which suggests that characteristics recognized from the sample-count distributions shall naturally be made uniform among the color components. In this case, the characteristics are compensated for a color slippage included therein by correcting an offset, putting an emphasis on the contrast and correcting the brightness at steps S204 to S216 in order to produce a well pitched and good image from the image data with poor color reproducibility. In addition, since the compensation and correction work is automated, even an untrained user is capable of correcting the balance of color with ease. Furthermore, at a step S205, an offset quantity reflecting a degree of analogy is calculated for use in the compensation of the characteristics for a color slippage.

Description

Detailed Description of the Invention

[0001]

BACKGROUND OF THE INVENTION 1. Field of the Invention The present invention relates to an image processing apparatus, an image processing method and a medium recording an image processing program, and more particularly to an image processing apparatus and an image processing method for correcting color balance and a medium recording an image processing program. Regarding

[0002]

2. Description of the Related Art Heretofore, correcting the color balance often refers to correcting so-called color cast. That is, it is a correction process for a color shift in an image input device or the like.

For example, in a digital still camera or the like, image data is output as RGB (red green blue) gradation color data. In this case, depending on the characteristics of the lens and the CCD element, there is also seen a so-called color shift in which a specific color, for example, red is emphasized more than the actual color.

Conventionally, when it is desired to make some correction to image data having such a color shift, it is read into image processing software or the like, and an operator performs a predetermined operation to perform trial-and-error and the component value of the emphasized color. Is weakening.

[0005]

The conventional correction method described above has the following problems.

Since the operator makes corrections by trial and error, the accuracy is poor and it may be difficult for an unskilled person.

Further, since the predetermined component value is uniformly increased / decreased, it may not be said that all gradations are corrected correctly.

Furthermore, it cannot be said that efficient correction is possible in the case where an element other than simple color misregistration is included.

Further, it is basically impossible to judge whether or not the gradation color data for each component representing the color of each pixel is correct unless it is compared with a standard color. Considering that it may be natural that a certain component is emphasized by an external factor in some cases, it is extremely difficult to automatically determine whether or not there is a color shift. I have no choice.

The present invention has been made in view of the above problems, and an image processing apparatus and an image processing method capable of automating correction of color reproducibility such as so-called color misregistration and comprehensively correcting color balance. Another object of the present invention is to provide a medium recording an image processing program.

[0011]

In order to achieve the above object, the invention according to claim 1 is an image in which an image is represented as a set of pixels in a dot matrix by gradation gradation color data composed of substantially equal color components. This is an image processing device that performs conversion for correcting the color balance according to the relationship between input and output by inputting each component value of the same image data, performing a predetermined conversion process and outputting the data. And a characteristic equalizing means for determining the distribution of the gradation color data for each color component, determining the deviation between the color components, and equalizing the characteristics between the color components based on the determined deviation. It is as a configuration.

In the invention according to claim 1 configured as described above, when there is image data in which an image is represented as a set of each pixel in a dot matrix by gradation color data consisting of substantially equal color components. By inputting each component value of the same image data, performing a predetermined conversion process and outputting the same, the color balance is corrected by the relationship between the input and the output. At this time, the characteristic equalizing means is used. Seeks the distribution of gradation color data for each color component, finds the deviation between the color components, and equalizes the characteristics between the color components based on the found deviation.

Taking a digital still camera as an example, conventionally, to correct the color balance means to emphasize whether or not the conversion characteristics of the color components in each pixel match, and to emphasize the component value for each color. I adjusted it by judging whether there was any. On the other hand, in the present invention, the distribution of the gradation color data is obtained for each color component, the characteristics are found in the separated state for each color component, and such characteristics are made uniform. That is, regardless of the content of the image, when the characteristics of the distribution of the gradation color data for each color component are the same, it can be seen that a sharp image with no color shift is obtained as an image. Uniformity is sought based on the distribution.
In addition, the homogenization here does not mean the homogenization in a strict sense, but may be at least a degree of homogenizing the tendency.

In this sense, the distribution need only be such that the distribution status of each component in the image data can be found, and various statistical methods including frequency distribution can be adopted. For example, it is also possible to use secondary processing data such as a standard deviation or median derived from a frequency distribution, or a mode value. In this case, the frequency distribution has an advantage that the processing is easy and can be simplified.

In some cases, the characteristics of the distributions do not match. For example, in the evening,
There is no unnaturalness even if it is only reddish colors. In such a case, it is unnatural to make the characteristics determined from the distribution of each color component uniform. Therefore, in the invention according to claim 2, in the image processing apparatus according to claim 1, the characteristic equalizing means determines the similarity between the color components based on the distribution, and the similarity is small. In this case, a modification control means is provided to prevent the above modification.

In the invention according to claim 2 configured as described above, the correction control means of the characteristic equalizing means first determines the degree of similarity between the color components. If it is an evening scene when it consists of red (R) green (G) blue (B) color components, a biased distribution that is distributed only to the red component will be obtained. The distribution is extremely reduced. In such a case, it is natural that the characteristics of the distribution do not become uniform, the degree of similarity between the color components is extremely small, and the characteristics cannot be made uniform. On the other hand, if each color component appears on average, it is determined that the degree of similarity in distribution is large, and in such a case, the mutual characteristics are made uniform, and thus the bias unique to the image input device is eliminated. It can also be reduced.

In determining the degree of similarity of distributions, it is not always necessary to use frequency distributions at all gradations for individual comparison, and as a suitable example in such a case, the invention according to claim 3 is The image processing apparatus according to claim 2, wherein the correction control unit divides a gradation range that the gradation color data can have into a plurality of areas, and compares the distributions of the respective areas with each other to determine similarity between color components. It is configured to judge the condition.

In the invention according to claim 3 configured as described above, in determining the similarity of the distribution, the gradation range that the gradation color data can take is divided into a plurality of areas, and the distribution for each area is divided. Is compared between each color component. This allows
The effort of comparing the distributions for each gradation is reduced.

On the other hand, various methods can be adopted for determining the similarity of distributions. As an example, the invention according to claim 4 is the image according to claim 2 or claim 3. In the processing device, the correction control means is configured to determine the degree of similarity based on the inner product of vectors having the distribution of each color component as an element.

The dot product of the vectors can be easily performed in view of the mutual correlation. Therefore, in order to adopt the method, the distribution itself is considered in the same manner as the vector element, and the inner product is obtained between the respective colors. It becomes "1" when the degree of similarity is the largest, and approaches "0" when the degree of similarity is small. In considering the distribution as a vector element, it is not always necessary to make it the number of elements corresponding to the total number of gradations. In the case of the invention described in claim 3, the distribution in the area in which the entire gradation range is classified into some It should be an element.

When the degree of similarity is small, it is possible to compare the degree of similarity with a predetermined threshold value and branch the judgment based on the result of the comparison, as a technique for preventing the characteristics from being made uniform. . On the other hand, similarly, a method of controlling the effective value can be adopted as a method of making the characteristics uniform when the degree of similarity is large and not making the characteristics uniform when the degree of similarity is small. As such an example, in the invention according to claim 5, in the image processing device according to any one of claims 2 to 4, the characteristic equalizing means sets an effective value for equalizing the characteristics. In addition to being utilized, the correction control means is configured to substantially enable or disable the correction by changing the effective value and continuously change the effective value.

In the invention according to claim 5 configured as described above, the effective value for uniforming the characteristics is obtained,
Changing this effective value effectively enables or disables the modification. A window function or the like is effective for such control, and more specifically, it takes "1" to enable it and "0" to disable it.
The window function that becomes

On the other hand, in this example, "1" to "0"
If there is a discontinuous change in the area changing from “0” to “1” or “1”, the determination of the degree of similarity may change subtly depending on how the gradation color data is captured. Then, even if the images are the same, the determination result may be the opposite. Therefore, as a countermeasure against such a case, the effective value is continuously changed. Of course, the window function can be modified in various ways,
Depending on the factor, it is possible to make the effective value valid in a small area having a similar condition and to treat it in the opposite way in order to treat it exceptionally. Of course, the effective value here refers to a value that is widely involved in the correction, such as a correction amount, a correction amount, or an offset amount, and this window value is also calculated for the window function described above. It may be one variable used in the process.

In general, the degree of similarity is not a color shift due to the device. However, there are cases in which the degree of similarity changes extremely due to special lighting. Therefore, even if the degree of similarity is small, it may be effective to make the characteristics uniform depending on the cause.
Therefore, in general, the characteristics may not be made uniform when the degree of similarity is small, but the characteristics may be made uniform depending on the cause even if the degree of similarity is small.
For example, when the degree of similarity becomes extremely small, it is possible to make the characteristics uniform, and it is also possible to use a window function corresponding to the degree of similarity in order to realize this.

Note that the method of unifying the characteristics is not necessarily limited to the idea of unifying the characteristics based on the degree of similarity between the color components. Therefore, as a preferred example corresponding to the present idea, claim 18
According to the invention, the image data in which the image is represented as a set of pixels in a dot matrix by the gradation color data consisting of substantially equal color components is input with each component value of the image data and a predetermined conversion process is performed. An image processing apparatus that performs conversion to correct the color balance depending on the relationship between input and output by applying and outputting, and obtains the degree of similarity of the distribution of gradation color data for each color component, When the degree of similarity is small, the color balance is not corrected.

Further, in the image processing method of the present invention, the image data represented by a set of pixels in a dot matrix by the gradation color data having substantially equal color components is the same image data. By inputting each component value of the data, applying a predetermined conversion process and outputting it, when performing the conversion to correct the color balance in the relationship between the input and the output, the distribution of the gradation color data for each color component There is a configuration that finds the degree of similarity and does not correct the color balance when the degree of similarity is small,
In the invention of the medium in which the program for performing image processing by the computer according to claim 20 is recorded, the image data in which the image is represented as a set of pixels in a dot matrix by the gradation color specification data composed of substantially equal color components, By inputting each component value of the same image data, performing a predetermined conversion process and outputting it, when performing conversion for correcting the color balance in relation to the input and output, the gradation color data of each color component is The degree of similarity of distribution is obtained, and when the degree of similarity is small, the color balance is not corrected.

Claims 18 to 2 configured as described above
Also in the invention according to 0, the similarity of the distribution of the gradation color data is obtained for each color component, and when the similarity is small, the color balance is not corrected.

On the other hand, when obtaining the distribution as described above,
Not all pixels have to be treated equally. As an example thereof, the invention according to claim 6 is the image processing apparatus according to any one of claims 1 to 5, wherein the characteristic equalizing means approximates the gradation color data of each color component. It is configured such that the pixels that are subject to the above distribution are targeted.

Originally, a person can judge a color misregistration by looking at an image when an achromatic color appears. That is, when only looking at a red object, it is impossible to judge whether or not there is a color shift unless the original color is known. Therefore, it can be said that it is effective to examine the characteristic variation only for the achromatic pixels in order to examine the balance between the colors. For this reason, the characteristic equalizing means determines, for each pixel, whether or not the gradation color data of each color component are close to each other, and if they are close to each other, counts them as an object of distribution, and The characteristics are made uniform based on the obtained distribution.

Whether or not the gradation color data of the color components in each pixel are similar to each other can be determined by obtaining the maximum value and the minimum value and judging from the difference. It is also possible that the extreme value data is not handled because it is highly possible that it is saturated.

By the way, as an example for achieving uniform characteristics, the invention according to claim 7 is the above-mentioned claim 1 to claim 6.
In the image processing device described in (1), the characteristic equalizing means determines the characteristic from a predetermined position in the distribution, obtains an offset amount corresponding to the deviation between the components so that the characteristic becomes uniform, and calculates each component value. Is to be modified.

In the invention according to claim 7 configured as described above, some positions such as an upper end and a lower end, an average value, a median and a mode value are obtained, including a process of obtaining a distribution for each color component. However, it is possible to find out the characteristics of each component from these. Then, it is considered that the shift between these respective components is a characteristic that can be a factor of a specific color shift, so that the offset amount corresponding to the same shift is obtained and each component value is corrected.

As an example of a specific position, the invention according to claim 8 is the image processing apparatus according to claim 7, wherein the characteristic equalizing means sets the end position of the range of the distribution to the distribution position. It is configured to be judged as a characteristic.

In the invention according to claim 8 configured as described above, the shift between the respective components is obtained by regarding the upper and lower end positions, which are the end positions of the distribution range, as the characteristics of the distribution.

Looking at the individual distributions, extremely diverse distributions are easily determined uniformly at the upper end position or the lower end position, and the characteristics can be easily obtained.

As another example, the invention according to claim 9 is the image processing apparatus according to claim 7 or 8, wherein the characteristic equalizing means is provided at a substantially central position of the distribution. Is determined as the characteristic of the distribution.

In the invention according to claim 9 configured as described above, the deviation between the respective components is obtained by regarding the positions such as the average value, the median and the mode which are the substantially central positions of the distribution as the characteristics of the distribution. .

Similarly, extremely diverse distributions are easily determined uniformly at positions such as an average value, a median, and a mode value, which are substantially central positions, and characteristics can be easily acquired.

In a general image, although there are differences in the absolute value of each component, the spread of the distribution does not change much in many cases. Therefore, it can be said that it is more natural for each component to have a uniform spread amount. Therefore, the invention according to claim 10 is the image processing apparatus according to any one of claims 1 to 9, wherein the characteristic equalizing means substantially equalizes the spread of the distribution for each color component. It is as a configuration.

In the invention according to claim 10 configured as described above, the spread of the distribution is obtained for each component value, and the conversion processing is performed so as to make the distribution substantially uniform.

The idea of the spread of the distribution can be grasped in various ways and is not limited to a particular one. As an example, the invention according to claim 11 is the image processing apparatus according to claim 10. In the above, the characteristic equalizing means is configured to expand both ends of the spread of the distribution within an effective gradation range.

In the invention according to claim 11 configured as described above, since the width of the distribution is known at both ends of the skirt assuming that the distribution has a mountain shape, this width is expanded within the effective gradation range. Convert to. For example, if the width of the distribution is only a part of the width of the effective gradation, it can be said that the image has low contrast. In such a case, the distribution should be spread over the entire width of the effective gradation range. Expand. If the distribution is expanded so as to spread over the entire width of the effective gradation range for each color component, the characteristic of the distribution width is made uniform. The width of the distribution in this case is not limited to the width of the actual distribution, and may be such that the error range is omitted by cutting both end portions by a predetermined amount.

As another example, in the invention according to claim 12, in the image processing apparatus according to claim 10, the characteristic equalizing means has a range in which the distribution density is large based on the spread amount of the same distribution. While a large number of gradations are given to the above, a small number of gradations is assigned to a range having a small distribution density.

According to the twelfth aspect of the invention configured as described above, the characteristic homogenizing means obtains the spread amount of the distribution of each component, and based on the obtained distribution, the distribution density of each component is set to a large range. While giving a large number of gradations, a small number of gradations is assigned to a range having a small distribution density. The concept of the spread amount corresponds to the distribution of the distribution as well as the width of the distribution, and may be a standard deviation, a variance, or a kurtosis in mathematical expression. When the characteristics such as the spread amount are approached so as to match each component, when the portion with a large distribution density is converted to spread within the allowable range, the spread amount that has been shifted for each component is matched. It will be possible to see, and the distribution will be distributed using the effective range as much as possible without being concentrated in a part of the range.
The overall contrast will be emphasized. Of course, the spread amount may be other than the standard deviation, the variance, or the kurtosis, and these do not require calculation in the strict sense, and the calculation can be simplified.

Further, the invention according to claim 13 is the image processing apparatus according to any one of claims 1 to 12, wherein the characteristic equalizing means uniformizes the brightness based on the distribution of each component. It is configured to be converted.

According to the thirteenth aspect of the present invention configured as described above, since the brightness of the distribution of each component is non-uniform, it may look like a color shift. The brightness based on the distribution of each component is made uniform. The brightness here represents the overall position of the distribution. For example, if the distribution is located on the overall bright side, it is considered bright, and if it is located on the overall dark side, it is considered dark.

Of course, the method for judging the overall brightness can be changed as appropriate, and various correction methods for brightening or darkening can also be adopted as appropriate. According to a fourteenth aspect of the present invention, in the image processing apparatus according to the thirteenth aspect, the characteristic equalizing means is
The lightness and darkness of the image is determined by comparing the approximate center position of the distribution with a predetermined gradation.

According to the fourteenth aspect of the invention configured as described above, the substantially central position of the distribution is compared with a predetermined gradation in the effective gradation range, and the lightness or darkness of the image is judged based on the comparison result.

For example, the median obtained when obtaining the distribution has a condition as the center position of the distribution, but the overall brightness depends on whether the median is larger or smaller than the median value in all the gradation numbers. It is possible to determine whether is bright or dark.

According to a fifteenth aspect of the present invention, in the image processing apparatus according to the thirteenth or the fourteenth aspect, the characteristic equalizing means sets γ for each component based on the determination result of the brightness of the image. It is configured to make the lightness and darkness of the image uniform by correction.

According to the fifteenth aspect of the invention configured as described above, after determining the lightness / darkness of the image by various methods, the lightness / darkness of the image is made uniform by γ correction for each component based on the determination result. Let For example, if the image looks dark γ
When the γ correction is set to <1, it is corrected to be bright as a whole, and the same median approaches the median value of all gradations. If it is bright, the γ correction is set to γ> 1 to be corrected to be dark as a whole. That is, the same median comes close to the median of all the gradation numbers.

As described above, the method of judging the deviation of the color balance based on the comparison of the distributions of the respective color components is not necessarily limited to the actual apparatus. As an example, the invention according to claim 16 By inputting each component value of the image data representing the image as a set of pixels in a dot matrix with gradation color data consisting of equal color components, applying a predetermined conversion process and outputting , When performing conversion to correct the color balance based on the relationship between input and output, find the distribution of the gradation color data for each color component, find the deviation between each color component, and calculate each color based on the found deviation. It is configured to make the characteristics of the components uniform.

That is, there is no difference in that the method is not limited to a substantial device and is effective as a method.

By the way, such an image processing device may exist as a single device or may be used in a state of being incorporated in a certain device. The idea of the invention includes various modes. Further, it can be changed as appropriate such as software or hardware.

As an example thereof, a printer driver for converting image data corresponding to printing ink based on dot matrix image data composed of gradation color data for color components which are substantially equal to each other and printing the image data on a predetermined color printer. Also in this case, it is possible to obtain a distribution of gradation color data for each color component, make the characteristics of each component uniform from the distribution, and perform printing based on the image data thus made uniform. it can.

In this case, the printer driver converts the input image data in correspondence with the printing ink. At this time, the distribution is obtained for each color component in the image data, and the characteristics found from the distribution are obtained. Is printed so that each color component becomes uniform. That is, by comparing the distributions obtained for the respective color components and making a correction so that the distributions match, the color balance as a whole is adjusted and good color development is achieved even for the individual colors of each pixel.

When it comes to software of an image processing apparatus as an example of embodying the idea of the invention, it must be said that it naturally exists on a recording medium recording such software and is used.

As an example thereof, the invention according to claim 17 is a medium in which a program for image processing by a computer is recorded, wherein an image is formed of pixels in a dot matrix by gradation gradation color data consisting of substantially equal color components. For the image data represented as a set, each component value of the same image data is input, subjected to a predetermined conversion process and output, so that the color balance is corrected in the relation between the input and the output. The distribution of the gradation color data is calculated for each color, the deviation between the color components is calculated, and the characteristics of the color components are made uniform based on the calculated deviation.

Of course, the recording medium may be a magnetic recording medium or a magneto-optical recording medium, and any recording medium developed in the future can be considered in exactly the same manner. In addition, the duplication stage of the primary duplication product, the secondary duplication product, and the like is absolutely the same. In addition, the present invention is still used even when a communication line is used as a supply method. In this case, the side that provides the software using the communication line functions as a software providing device, and it must be the same as the present invention.

Further, even if a part is software and a part is realized by hardware, there is no difference in the idea of the invention, and it is necessary to store a part on a recording medium. It may be in such a form that it is read as appropriate. Further, it goes without saying that the present invention can also be applied to image processing apparatuses such as color facsimile machines, color copiers, color scanners, digital still cameras, digital video cameras and the like.

[0061]

As described above, according to the present invention, it is possible to automate the judgment of color shift, which is basically difficult to judge, by the concept of uniform distribution of each component, and to adjust the color balance more easily. It is possible to provide an image processing device capable of performing the above.

Further, according to the second aspect of the invention, when the degree of similarity of distribution is small, the characteristics are not made uniform, so that the characteristics are made uniform when the color balance is naturally deviated. It is possible to prevent an unnatural image from being generated, and according to the invention of claim 3, the gradation range is divided into a plurality of areas to obtain the distribution, so that comparison is facilitated. According to the invention described above, the degree of similarity is determined based on the inner product of the vectors having the distribution of each color component as an element, which facilitates the determination. Further, according to the invention of claim 5, the characteristics are uniform. By continuously changing the effective value for achieving the conversion, it is possible to prevent discontinuity in the image processing result.

Further, according to the invention of claim 6,
Pixels that are originally similar in gradation color data are picked up and used to determine the characteristics, which is effective in obtaining the variations in the characteristics.

Further, according to the invention of claim 7,
Since the shift of the distribution position is used, the offset amount between each component can be determined relatively easily.

Further, according to the invention of claim 8,
The end of the distribution is easy to determine uniformly, and the determination is easy.

Further, according to the invention of claim 9,
Since the shift at the position where the distribution density is high is used, the error in correcting the total shift is small.

Further, according to the tenth aspect of the invention, since the spread of the distribution is made uniform, it is possible to efficiently develop the color when the effective number of gradations for each color component is not effectively utilized. You can do it, and you can make the difference.

Further, according to the eleventh aspect of the present invention, the distribution is expanded by using both ends of the spread of the distribution that can be easily judged, so that the structure for making the judgment becomes easy.

According to the twelfth aspect of the invention, since the spread amount by a mathematical method is used,
More accurate judgment is possible.

Further, according to the thirteenth aspect of the present invention, by making the brightness of each component uniform, it is possible to eliminate a color shift in which only one component is projected.

Further, according to the fourteenth aspect of the present invention, the brightness is judged by using the approximate median value of the distribution, which is easy.

Further, according to the fifteenth aspect of the present invention, the brightness is made uniform by changing the lightness and darkness by the γ correction, and the γ correction is widely used, so that the configuration is easy.

Further, according to the sixteenth aspect of the invention, the concept of uniform distribution of each component makes it possible to automate the determination of the color shift, which is basically difficult to determine, and adjust the color balance more easily. It is possible to provide a possible image processing method.

Further, according to the seventeenth aspect of the present invention, it is possible to provide a medium in which a program for performing such image processing is recorded.

In the eighteenth to twentieth aspects of the present invention, regardless of the method of uniformizing the characteristics, when the degree of similarity in distribution is small, the characteristics are not made uniform, and the color balance is set. It is possible to prevent the occurrence of an unnatural image by homogenizing the characteristics when the deviation is natural.

[0076]

BEST MODE FOR CARRYING OUT THE INVENTION Embodiments of the present invention will be described below with reference to the drawings.

FIG. 1 is a block diagram showing an image processing system according to an embodiment of the present invention, and FIG. 2 is a block diagram showing a concrete hardware configuration example.

In the figure, the image input device 10 outputs image data to the image processing device 20 by picking up an image, etc., and the image processing device 20 performs image processing such as equalizing the characteristics and outputs the image. The image is output to the device 30, and the image output device 30 displays the image in which the contrast is enhanced.

Here, a specific example of the image input device 10 corresponds to the scanner 11, the digital still camera 12 or the video camera 14, and a specific example of the image processing device 20 corresponds to a computer system including a computer 21 and a hard disk 22. However, a specific example of the image output device 30 corresponds to the printer 31, the display 32, or the like. Of course, in addition to these, it can be applied to a color copying machine, a color facsimile machine, and the like.

Since this image processing system is intended to correct an image having poor color reproducibility represented by color shift, image data obtained by taking a photograph with the scanner 11 as the image input device 10 is used. For example, image data captured by the digital still camera 12, moving image captured by the video camera 14, or the like is subjected to processing, and is input to the computer system as the image processing apparatus 20. The calculation speed of the input image of the video camera 14 may not be in time. In such a case, it can be dealt with by performing the first condition setting requiring a calculation time for each shooting scene and only performing image conversion of each frame under the same condition setting during shooting.

The image processing apparatus 20 includes at least a frequency distribution detecting means for obtaining a frequency distribution for each color component, a similarity degree determining means for determining a similarity degree of the detected frequency distribution for each color component, and a frequency distribution. From the offset amount correction means for determining the deviation of each color component from the
Contrast correction means that corrects the deviation of the contrast of each color component from the frequency distribution to make it uniform, and brightness that makes the correction to make uniform by judging the deviation of the brightness of each color component from the frequency distribution. And a correction means. Of course, the image processing apparatus 20 may also be configured as a color conversion unit that corrects a color difference depending on the model or a resolution conversion unit that converts a resolution corresponding to each model. . In this example, computer 21 is RA
While using M or the like, each image processing program stored in the internal ROM or hard disk 22 is executed. Further, in recording these programs, as shown in FIG.
In addition to a portable medium such as a CD-ROM 42, it is installed in the hard disk 43, or an IC having a ROM 44 and a RAM 45.
It may be a card, or the communication line 46a may be used as a medium via a modem 46b or the like. In this case, the file server 46c is connected to the end of the communication line 46a, and the file server 46c provides the predetermined software.

The execution result of this image processing program is obtained as sharp image data whose color reproducibility is corrected as described later, and is printed by the printer 31 which is the image output device 30 based on the obtained image data. Alternatively, it is displayed on the display 32 which is the same image output device 30. Note that this image data is more specifically RGB (green,
The gradation data of "256" gradation is set for each of blue and red, and the image is configured as dot matrix data arranged in a grid in the vertical direction (height) and the horizontal direction (width).

In this embodiment, a computer system is incorporated between image input / output devices to perform image processing. However, such a computer system is not necessarily required, and as shown in FIG. An image processing device for correcting color reproducibility and the like is incorporated into the digital still camera 12a, and the converted image data is used for displaying on the display 32a or the printer 31a.
It may be a system for printing on. Further, as shown in FIG. 4, in the printer 31b that inputs and prints image data without passing through a computer system, image data input through the scanner 11b, the digital still camera 12b, the modem 13b, or the like is automatically detected. It is also possible to modify the color reproducibility.

Of the image processing executed by the computer 21, the processing corresponding to the frequency distribution detecting means and the similarity determining means is shown in FIG. 5, which is executed when the similarity for each color component is not small. The processing corresponding to the offset amount correction means, the contrast correction means, and the brightness correction means is shown in FIG.
Is shown in. It can be said that the similarity determination unit also constitutes a control unit that controls the effectiveness of the subsequent processing based on the similarity in a broad sense.

FIG. 5 mainly corresponds to the process of detecting the frequency distribution of each color component. First, the pixels to be distributed will be described.

The frequency distribution may be obtained for all pixels, but since the purpose is to determine the tendency of characteristics, it is not always necessary to obtain the frequency distribution for all pixels. Therefore, it is possible to perform thinning to the extent that it falls within a certain error range. In the present embodiment, the thinning-out process for thinning out the target pixel is executed as shown in step S102 of FIG. According to the statistical error, the error with respect to the sample number N can be expressed as approximately 1 / (N ** (1/2)). However, ** represents the power. Therefore, in order to perform processing with an error of about 1%, N = 10000.

As shown in FIG. 7, a bitmap image is formed as a two-dimensional dot matrix consisting of predetermined dots in the vertical direction and predetermined dots in the horizontal direction.
This bitmap screen is (width) x (height
Since the number of pixels is t), the sampling period ratio
Is ratio = min (width, height) / A + 1 (1). This min (width, height) is w
whichever is smaller of idth and height, A
Is a constant. In addition, the sampling cycle ra here
tio represents the number of pixels to be sampled, and the pixels marked with a circle in FIG. 8 are sampling cycles ratio =
The case of 2 is shown. That is, one pixel is sampled every two pixels in the vertical direction and the horizontal direction, and sampling is performed every other pixel. The number of sampling pixels in one line when A = 200 is as shown in FIG.

As can be seen from the figure, when the width is 200 pixels or more, the number of samples is 100 pixels or more at least, except for the case where the sampling period ratio = 1 which is not sampled. Therefore, when the number of pixels is 200 or more in the vertical and horizontal directions, (100 pixels) × (100 pixels) = (10000 pixels)
Is ensured and the error can be reduced to 1% or less.

Here, min (width, hei
ght) is used as a reference for the following reason.
For example, assuming that width >> height, as in the bitmap image shown in FIG. 10A, when the sampling period ratio is determined by the longer width, as shown in FIG. In addition, it may happen that pixels are extracted only in the upper and lower lines in the vertical direction. However, min (wi
If the sampling period ratio is determined based on the smaller one as dth, height), it is possible to perform thinning including the intermediate portion in the smaller vertical direction as shown in FIG. Become.

Of course, as for the pixels sampled in this way, as shown in FIG. 11, array variables (CNT_) prepared corresponding to all gradations of RGB are provided.
R, CNT_G, CNT_B) are counted for each component value to obtain the frequency distribution.

In this example, the frequency distribution is obtained by thinning out pixels in the vertical and horizontal directions at accurate sampling periods. This is suitable for processing while sequentially thinning out pixels that are input. However, when all the pixels are input, the coordinates may be randomly specified in the vertical direction and the horizontal direction to select the pixels. In this way, if the minimum required number of pixels, such as 10,000 pixels, is determined, 100
The process of random extraction is repeated until the number of pixels becomes 00, and the extraction may be stopped when the number of pixels becomes 10,000.

Further, it is also possible to perform the thinning-out in which the target pixel is specified in the simple thinning-out process in which the thinning-out is performed without specifying the target pixel. That is, it can be said that if the difference between the RGB components of each pixel is small, it is close to gray, and if only the gray pixels are extracted and the frequency distributions are compared, it is easy to determine the color characteristics of the input device. is there. FIG. 12 shows a process of extracting and thinning out such gray pixels. In step S302, the maximum component is specified among the components in the target pixel, and in step S304, the minimum component is specified. Then, in step S306, the maximum component difference, which is the difference between the maximum value and the minimum value, is calculated, and in step S308, it is determined whether or not the maximum component difference is within a range smaller than a predetermined threshold value. To do. As an example, as long as the pixel is close to gray,
It is assumed that the maximum component difference is within “52” gradation. Then, for pixels within such gradation, step S31
Count the frequency distribution at 0. On the other hand, if it exceeds such a gradation, it is not gray and is not used for counting. After that, the target pixel is moved to the next pixel in step S312, and the above-described processing is repeated until it is determined to be the final pixel in step S314.

Although the frequency distribution is obtained for the pixels selected by thinning out in this way, it may not always be appropriate to correct the image using this frequency distribution. Therefore, the following three points are checked.

The first is when the image is a binary image such as a monochrome image. For binary images including black and white images, the concept of correction of color reproducibility is inappropriate. FIG.
If there is a black-and-white image as shown in Fig. 14, the frequency distribution for each color component for this image is concentrated at both ends within the range of gradation number allocation as shown in Fig. 14. Basically, it also concentrates on the gradation “0” and the gradation “255”.

Therefore, when the black and white check is performed in step S104, the gradation "0" and the gradation "25" are set for each color component.
It can be determined whether or not the sum of the number of pixels of "5" matches the number of pixels selected by thinning out. Then, in the case of a monochrome image, a non-process is executed in step S106 to interrupt the process without executing the following process. In the present embodiment, the process is roughly divided into the first stage process for obtaining the frequency distribution and the second stage process for correcting the image data. Therefore, in this non-process, a flag is set so that the second stage luminance conversion process is not executed. Then, the distribution extraction process is completed.

The binary data is not limited to black and white, but may be colored binary data. In such a case as well, the process for correcting the color reproducibility is not necessary, and if the distribution state is checked and the distribution has two colors, the process may be interrupted as binary data. For example, in the case of two colors of a certain intermediate color and black, the distribution is concentrated on two gradations even in the frequency distribution for each color component. On the other hand, in the case of two colors of blue and black, the distribution is concentrated on two gradations only in the frequency distribution of the blue component, and the distribution is concentrated only on gradation “0” in the red component and the green component. That is, since the concentration of the distribution is less than or equal to two gradations in the case of binary data, a binary image is obtained by scanning the array variable and counting how many gradations other than “0” exist. It can be determined whether or not.

The second is to consider whether the image is a business graph or a natural image such as a photograph. Needless to say, it is necessary to correct the color reproducibility in natural images, but it is natural that there is a bias in the colors that are originally used in business graphs and paintings. It is unreasonable to perform the process for equalizing the characteristics of each color component from the frequency distribution of the image. Therefore, in step S108, it is checked whether the image is a natural image.

Natural images have an extremely large number of colors, including shadows, but some paintings such as business graphs and draw-types often have a limited number of colors. Therefore, if the number of colors is small, it can be determined that the image is not a natural image. RGB
When each of the components has 256 gradations, 16.7 million colors can be represented.
In order to determine how many of the 6.7 million colors are used, array variables corresponding to the number of colors need to be prepared, which is not realistic. On the other hand, since the frequency distribution has already been obtained, it can be determined whether or not the number of colors is that of the natural image by determining how many gradations are effectively used for each color component.

FIGS. 15A to 15C show an example of the frequency distribution in the case of a business graph, and FIGS.
(F) shows an example of the frequency distribution in the case of a natural image.
As is clear from this example, the non-natural image has a linear frequency distribution. In the processing in the computer 21, the number of gradations whose frequency is not "0" is counted over all the gradations for each color component, and they are added together. If it is a natural image, it is considered that it is distributed almost uniformly over all gradations, and the count value is often “768 (= 256 × 3)”. Even if the gradation of "20" is used, the count value is "60".
(= 20 × 3) ”. Therefore, if "200" is set as the threshold value for determining whether or not the image is a natural image, if it is "200" or less, it may be determined as a non-natural image. If it is determined that the image is a non-natural image, non-processing is executed in step S106. Of course, the threshold value "200" can be changed as appropriate.

It is also possible to judge whether or not the frequency distribution is a linear spectrum by the adjacency ratio of the gradation values whose frequency is not "0". That is, it is determined whether or not the gradation value whose frequency is not “0” and adjacent gradation values have a distribution. If at least one of the two adjacent gradation values is adjacent, nothing is done, and if both are not adjacent, counting is performed, and as a result, the number of gradation values that are not "0" and the count value You can make a judgment based on a percentage. For example, the number of gradation values that are not “0” is “64” and the number of non-adjacent gradation values is “6”.
If it is "4", it is understood that it is distributed in a linear spectrum.

Further, when the image processing program is being executed via the operating system, it is possible to judge by the extension of the image file. Of the bitmap files, in particular, a photographic image or the like is subjected to file compression, and an implicit extension is often used to indicate the compression method. For example, "JP
If the extension is "G", it is understood that the file is compressed in the JPEG format. Since the operating system manages the file name, if you send an inquiry to the operating system from the printer driver side, etc., the extension of the file will be answered. If so, the following processing may be executed. Further, if it is an extension such as “XLS” that is peculiar to the business graph, it can be determined that it is a non-natural image, and it is sufficient to select non-processing as described above.

The third consideration is whether or not there is a frame portion around the image as shown in FIG. If such a frame is white or black, its frequency distribution is shown in FIG.
As shown in, the linear spectrum appears at both ends in the gradation number allocation range, and also appears as a smooth frequency distribution inside other than the ends corresponding to the natural image inside.

Of course, it is more appropriate not to include the frame part in the frequency distribution. Therefore, in the check of the frame part in step S108, the sum of the pixel numbers of the gradation "0" and the gradation "255" is sufficiently large, and , It is determined whether the number of pixels does not match the number of pixels selected by thinning out, and if affirmative, it is determined that there is a frame portion and step S
At 112, frame processing is performed. In this frame processing, the number of pixels of gradation “0” and gradation “255” in the frequency distribution is set to “0” in order to ignore the frame. As a result, in the following processing, it can be handled in the same manner as that without a frame.

In this example, a white or black frame portion is targeted, but there may be a case where there is a frame of a specific color. In such a case, a protruding linear spectrum-like thing appears in the originally smooth curve drawn by the frequency distribution. Therefore,
A line spectrum having a large difference between the adjacent luminance values may be considered as a frame and may not be the target of the frequency distribution. In this case, the color may be used outside the frame portion, and therefore the average of the frequencies of the tone values on both sides may be calculated and assigned.

After taking the above-mentioned consideration into consideration, both ends of the frequency distribution are obtained in step S114 when not being processed. The frequency distribution in a natural image often appears in a mountain shape as shown in FIG. Of course, there are various positions and shapes. The base of such a frequency distribution is
From a statistical point of view, it will continue to approach "0" without limit. Therefore, in order to specify a certain frequency distribution, it is important to specify both ends of the mountain, but if the condition that the frequency is "0" is actually imposed, any distribution characteristic It could be the same.

For this reason, the portions that are inside by a certain distribution ratio from the side with the highest brightness and the side with the lowest brightness in the distribution range are the both ends of the distribution. In the present embodiment, FIG.
As shown in, the distribution ratio is set to 0.5%. Of course, this ratio can be changed appropriately. In this way, by cutting the upper and lower ends by a certain distribution ratio, it is possible to ignore white points and black points caused by noise and the like. On the contrary, if such a process is not performed, even if there is a white point or a black point, it becomes the both ends of the frequency distribution, so in most cases, the lowermost end is the gradation "0" and the uppermost end. Is the gradation "2
55 ”. However, such a situation is eliminated by defining the end portions as portions that are inward by 0.5% of the number of pixels from both end portions.

In the actual process, 0.5% of the number of pixels to be processed (the total number of pixels selected in the thinning process or the total number obtained by deleting the number of pixels corresponding to the frame portion) is calculated, and the upper end side in the frequency distribution is calculated. Then, the respective frequencies are accumulated in order from the lower end side toward the inner side, and a gradation value having a value of 0.5% is obtained. The upper end side is called Rmax, Gmax, Bmax for each color component of RGB, and the lower end side is R
Called min, Gmin, Bmin.

As described above, it may be more natural for the frequency distribution of each color component to be non-uniform. And
In such a case, the color reproducibility should not be modified. Looking at this from the results, it is judged that the frequency distribution should be uniform on the contrary when the frequency distribution of each color component is similar to some extent, and it should not be uniform if the frequency distribution is not similar. it can.

Therefore, in the present embodiment, the similarity of the frequency distribution for each color component is checked in step S116. Now, assuming that the frequency distribution for each color component is as shown in FIG. 19, the entire gradation range is divided into four areas ([0
~ 63], [64-127], [128-192],
[193 to 255]) and consider a feature vector whose elements are frequencies belonging to each region. Taking the red component as an example, the frequencies in each region are r64, r128, r192, r
255, and assuming that the total number of effective pixels is r_pixel, the feature vector of the red component can be expressed by Equation 1.

[0110]

[Equation 1]

The same processing is executed for the green component and the blue component, and then the inner product of the feature vectors between the color components is obtained. That is, the inner product corr_rg of the feature vector between the red component and the green component is obtained as shown in Equation 2.
Inner product corr_ of feature vectors between green and blue components
gb is obtained as shown in Equation 3, and the inner product corr_br of the feature vectors between the blue component and the red component is obtained as shown in Equation 4.

[0112]

[Equation 2]

[0113]

[Equation 3]

[0114]

[Equation 4]

It can be said that the inner product of the vectors represents the degree of similarity between the two vectors, and the value thereof is "0" to "1". Therefore,
Assuming that the threshold value CORR is "0.7", the inner products corr_rg, corr_gb, cor
Any one of r_br has this threshold value CORR.
If there is the following, it is determined that the degree of similarity is low, and the non-processing of step S106 is executed.

In the present embodiment, the processing based on the inner product of the feature vectors constitutes the similarity determining means, and if the inner product is calculated based on the feature vector, the method is established and the determination is easy. However, it is not limited to this example. For example, the entire gradation range is divided into four areas, but it is possible to set more than these areas, and the approximation can be made using statistical methods such as the positions of both ends of the frequency distribution, standard deviation, and kurtosis. It is also possible to find the degree.

Here, a specific method for obtaining the degree of approximation using such a statistical method will be described. An example of a statistical method is one that uses a representative value of distribution. Now, as the variables, the absolute value of the difference between the average value, the median value, and the standard deviation (variance) is the red component and the green component, the green component and the blue component,
It is calculated between the blue component and the red component. Then, regarding the average value and the standard deviation, the absolute value of the difference between the red component and the green component is Ave.
_Rg and Std_rg, the evaluation function between the red component and the green component is h (rg) = (1-Ave_rg / 255) * (1-S
td_rg / 255). Similarly, as an evaluation function between the green component and the blue component, h (gb) = (1-Ave_gb / 255) * (1-S
td_gb / 255), and as an evaluation function between the blue component and the red component, h (br) = (1-Ave_br / 255) × (1-S
td_br / 255). If the distributions are similar, the mean, median,
The standard deviations (variances) are almost the same, and the differences between the variables are almost "0", so that the value of the evaluation function h is close to "1". When the distributions are different, the difference is large and the value of the evaluation function h is small. Therefore, it becomes possible to determine whether or not to perform the correction by comparing the evaluation function with the threshold value obtained by the experiment. Of course, in this case it is possible to use the median instead of the average,
The specific example of the evaluation function is not limited to this.

If a certain degree of similarity is found in the frequency distribution obtained for each color component by the above processing, it can be determined that the characteristic of each component can be found from each frequency distribution. Aim for uniformity. In addition, as a result of the above-described various determinations, non-processing may be executed and the flag may be set in step S106.
In such a case, since the same flag is referred to in step S202, this processing ends without performing the following equalization processing.

In the process of equalizing the characteristics, the offset is calculated and corrected first. The offset, which corresponds to the correction of color misregistration in a so-called narrow sense, and which aims to make the characteristics uniform, constitutes an effective value in the present embodiment. Originally, as shown in FIG. 20, there must be a direct proportional relationship between the color component of the subject and each of the RGB components, but the conversion characteristics are different for each component due to the characteristics of the image sensor and the like. Sometimes.
Conventionally, such a shift could not be determined from a normal image. However, if the frequency distributions of the respective color components are approximately similar to each other, it can be determined that they should be the same, so that the offset amount, which is the deviation between the respective components, can be detected.

In this embodiment, step S204.
In step S206, a table used to correct the color misregistration is created in consideration of the offset amount.

When the RGB gradation data (Rp, Gp, Bp) is used, the overall luminance yp is used in a television or the like.
Is calculated as yp = 0.30Rp + 0.59Gp + 0.11Bp (2). That is, the green component has the greatest effect on the brightness, and in that sense, correcting the deviation of other color components from the green component has the advantage of not changing the overall image.

On the other hand, in order to obtain the deviation of the frequency distribution for each color component, it is desirable to consider the characteristic part of this frequency distribution. Therefore, in the present embodiment, the upper end (Rmax, Gmax, B of the frequency distribution subjected to the above-described end processing is applied.
max) and the median on the frequency distribution (Rmed, G
med, Bmed). The positions at both ends are effective in determining the distribution. However, the lower end position is intentionally omitted because it is difficult to understand the influence of the deviation. As a result, it becomes possible to make a correction by emphasizing only the deviation obtained in a range where the influence of the deviation is large. The median at the center of the distribution can indicate the position of the peak in the frequency distribution even if there are extreme pixels. In this case, such a mountain portion is also a portion that greatly affects the image of the image, and is effective in understanding the characteristics.

Deviations of the other color components from the upper end (Rmax, Gmax, Bmax) and the median (Rmed, Gmed, Bmed) of the green, green, and red obtained in this way with respect to the green component are dRmax, dBmax, dRme.
d and dBmed are calculated based on the following equation.

DRmax = Gmax-Rmax (3) dBmax = Gmax-Bmax (4) dRmed = Gmed-Rmed (5) dBmed = Gmed-Bmed (6) Then, with reference to these, the offset dR for the red component And the blue component offset dB are obtained by the following equation.

DR = (dRmax + dRmed) / 2 (7) dB = (dBmax + dBmed) / 4 (8) However, -12 <dR, dB <12. The reason for limiting in this way is to prevent the frequency distribution from being largely corrected by this example only when the color distribution cannot correct the complete color reproducibility. Of course, an appropriate value may be set for this range based on experimental experience. Further, the difference in the denominator corresponds to (2) the difference in influence based on color, and can be appropriately changed based on experiments and the like.

Since these are nothing but offset amounts, the actual statistical values are modified based on the following equation to obtain a new upper limit Rma.
x2, Bmax2, median Rmed2, Bmed2 and lower ends Rmin2, Bmin2.

Rmax2 = Rmax + dR (9) Rmed2 = Rmed + dR (10) Rmin2 = Rmin + dR (11) Bmax2 = Bmax + dB (12) Bmed2 = Bmed + dB (13) Bmin2 = Bmin + dB (14) Note that this correspondence relationship is obtained. It is clear that is only the offset amount of the red component and the blue component with respect to the green component, and does not change depending on the gradation value. Therefore, it is sufficient to uniformly add the offset amount to the actual image data in each pixel.

However, as will be described later, in the present embodiment, the image data is also corrected on the basis of other factors, and it is disadvantageous to individually correct it because it requires a calculation time. Therefore, in order to improve the efficiency of calculation, in the present embodiment, the RGB gradation data (R2, G2, B2) after conversion with respect to the RGB gradation data (R1, G1, B1) before conversion in step S206. The table representing the correspondence relation is formed, and the image data is actually corrected only once at the end.

On the other hand, in the above-described embodiment, whether or not the similarity is obtained based on the inner product of the vectors and the similarity is compared with the threshold value CORR (“0.7”) to make the characteristics uniform or not. Have decided. Therefore, when the inner product of the vector and the threshold value are substantially equal to each other, different judgment results may be made due to the influence of the bits added to the surroundings even in the same image.

Of course, if it is determined that the characteristics are made uniform, the offset amount as described above is added, and if it is determined that the characteristics are not made uniform, the offset amount as described above is not added. Therefore, a large difference occurs across the threshold value.

It is effective to use a window function that continuously changes as a method of preventing the result from being greatly changed in this way.

X = min (corr_rg, corr_
gb, corr_br) (141), and the window function f (x) is as follows: when x <0.5 f (x) = 0 When 0.5 ≦ x ≦ 0.7 f (x) = 5 · When x−2.5 0.7 <x, f (x) = 1 and the threshold value CORR is set to “0.5”. FIG. 30 shows the change situation of the window function f (x), where x <0.
5 becomes a constant “0”, and when 0.5 ≦ x ≦ 0.7, it linearly increases from “0” to “1.0”, and becomes 0.
When 7 <x, it becomes a constant “1.0”. And
The red component offset dR and the blue component offset dB are found in the equations (7) and (8).
Change to the product of (x). That is, dR = f (x) · (dRmax + dRmed) / 2 (7) ′ dB = f (x) · (dBmax + dBmed) / 4 (8) ′. In the flowcharts shown in FIGS. 5 and 6, if the similarity does not exceed the threshold value, the processing is not performed. However, when the offset is multiplied by the window function, the flowchart shown in FIG. 31 is executed. . That is, the processing is not performed based on the similarity, the offset amount is calculated using the window function in step S205, and the calculated offset amount is used. Of course, as described above, when x <0.7, the window function f (x)
Rapidly approaches "0", the image near the threshold value does not change significantly due to the processing, and the offset is almost "0" for the one having a low degree of similarity, which has no adverse effect. . Further, equations (9) to (14) are also calculated based on the offset amounts dR and dB calculated in this way.

Obviously, the window function f (x) is not limited to the above-mentioned function. In the above-mentioned example, the value of the window function is changed based on the minimum value of the correlation coefficient, but it is not limited to the minimum value. For example, f (corr_rg, corr_gb, corr_b
r) (142) Further, more generally, it may be f (statistical amount). In this case, the statistic has minimum, maximum,
Median, standard deviation, etc. are applicable.

On the other hand, the window function functions so as to validate the calculated value in one area and invalidate the calculated value in another area as the window is opened and closed. Now, when classifying color casts according to their causes, there are two cases, one is due to the hardware performance of the image input device 10 described above, the other is intentional color casts such as sunset, and color casts due to special illumination using a tungsten lamp or the like. Can be classified.
As described above, it is not necessary to unify the characteristics for intentional color cast due to the sunset, but in the case of color cast due to special lighting using a tungsten lamp, etc., the characteristics should be unified. It is also meaningful to plan.

The intentional color cast due to a sunset or the like and the color cast due to special illumination using a tungsten lamp or the like can be judged from the above-mentioned correlation coefficient. That is, a photograph having an extremely low correlation coefficient is often caused by such special illumination, and the window function is transformed as shown in FIG.

That is, the region of x <0.5 is deformed, and when x <0.1 f (x) = 1.0 When 0.1 ≦ x <0.3 f (x) = − 5. When x + 1.5 0.3 ≦ x0.5, f (x) = 0 was set. As a result, the window function f ′ (x) rises linearly again when x becomes “0.3” or less, and the window function f ′ (x) stays constant in the region where x is “0.1” or less. 1.0 ". Of course, when the window function becomes “1.0”, the large offset amount calculated based on the distribution state of each component is applied as it is, and the influence of illumination is canceled to make the characteristics uniform.

The window function does not need to change linearly in the transition region, and may be a curve that monotonically increases or decreases.

Further, as shown in FIG. 5, step S116.
In step S106, if the degree of similarity is low, the flag is set by executing non-processing.
Alternatively, as shown in FIG. 31, the window function is used to calculate the offset amount in step S205 without executing step S116, so that the characteristics are not substantially equalized when the similarity is substantially low. Therefore, it can be said that the correction control means is configured by the software processing and the hardware for realizing the software processing.

On the other hand, if there is a difference in the spread of the frequency distribution, it is effective to make it uniform. In the present embodiment, the frequency distribution is made uniform and the frequency distribution is expanded within a possible range to emphasize the contrast for each component.

To emphasize the contrast, the gradation range is "0".
~ "255", each color component before conversion (R1,
G1 and B1) and the maximum values Rmax2 and Gmax of each component,
Bmax2 and minimum values Rmin2, Gmin, Bmin2
The color components (R2, G2, B2) of the conversion destination are obtained from the following equations.

R2 = far × R1 + fbr (15) G2 = fag × G1 + fbg (16) B2 = fab × B1 + fbb (17) However, far = 255 / (Rmax2-Rmin2) (18) fag = 255 / (Gmax) -Gmin) (19) fav = 255 / (Bmax2-Bmin2) (20) fbr = -far * Rmin2 or 255-far * Rmax2 (21) fbg = -fag * Gmin or 255-fag * Gmax2 ((21) 22) fbb = -fab * Bmin2 or 255-fab * Bmax2 (23) Further, if R2, G2, B2 <0 in the above conversion formula, R2, G2,
If B2 = 0 and R2, G2, B2> 255, then R2, G2, B
2 = 255. It can be said that far, fag, and fab are slopes and fbr, fbg, and fbb are offsets. According to this conversion formula, as shown in FIG. 22, a luminance distribution having a certain narrow width can be expanded to a reproducible range.
Note that, basically, when the distribution range of luminance is expanded, the number of pixels does not change, so the areas of the histograms match. However, when the reproducible range is used to the maximum extent in this way to expand the luminance distribution, the highlight portion may be white or the high shadow portion may be blackened. In order to prevent this, the gradation range to be expanded is limited in the present embodiment. That is, “5” is left as the gradation value as a range that does not expand to the upper and lower ends of the entire gradation range. As a result, the parameters of the conversion formula are as follows.

Far = 245 / (Rmax2-Rmin2) (24) fag = 245 / (Gmax-Gmin) (25) fav = 245 / (Bmax2-Bmin2) (26) fbr = 5-far × Rmin2 or 250-far × Rmax2 (27) fbg = 5-fag × Gmin or 250-fag × Gmax2 (28) fbb = 5-fab × Bmin2 or 250-fab × Bmax2 (29) And in this case, the floor Less than key "5" and tone "25"
No conversion is performed for 0 or more.

In the present embodiment, in order to hold the highlight portion and the high shadow portion, the range of the gradation value from the end of the gradation range is set to "5" as the non-enlarged area. The range may be narrowed if the image output device is capable of relatively easily reproducing the highlight part and the high shadow part, or may be made larger when the reproducibility is weaker. . Further, instead of not uniformly expanding, the expansion rate may be gradually limited in the border area.

Further, FIG. 23A shows the case where the luminance distribution of the image is narrow. However, as described above, the enlargement ratio of the luminance distribution (corresponding to far, fag, fab) is applied. In that case, a very large enlargement ratio may be obtained in accordance with the reproducible range. Then, it is natural that the contrast width from the brightest part to the darkest part is narrow in a dusk condition such as evening, but as a result of trying to greatly expand the contrast of this image, it is converted like a daytime image. It can happen. Since such a conversion is not desired, limit the enlargement ratio and set fa
Limit r, fag, fab so that it does not exceed 1.5 (~ 2). As a result, twilight will be expressed like twilight.

FIG. 23 shows a case where no restriction is placed on the enlargement ratio.
It is shown by the one-dot chain line in (a), and no extra portion remains in the reproducible range after conversion. However, in the case of limiting the expansion range, as shown by the chain double-dashed line in the same figure (b), there is a degree of freedom in where to bring the distribution after conversion, and in some cases it becomes bright as a whole. It can be too dark or too dark. Therefore, in such a case, the ratio of the residual areas remaining on the upper end side and the lower end side in the gradation range before conversion (m1: m2)
May be converted so as to match the ratio (n1: n2) of the remaining areas remaining on the upper end side and the lower end side after conversion.

As described above, the parameters far, fag, fab,
The process of obtaining fbr, fbg, and fbb is executed in step S208, and in the following step S210, a conversion table is created as in step S206. The conversion table at this time is calculated by using the converted component values (R1, G1, B1) shown in FIG. 21 as input, and replacing the converted component values (R1, G1, B1) in the conversion table. As a result, by referring to the conversion table, two processes, that is, the addition of the offset amount obtained in step S204 and the contrast enhancement process obtained in step S208, are simultaneously executed.

On the other hand, as the deviation between the color components of the frequency distribution, another factor called the overall brightness remains. Therefore, in step S214, γ correction is performed to make this brightness shift uniform, and thus γ is calculated in step S212. For example, the peak of the frequency distribution of the red component shown by the solid line in FIG. 24 is generally closer to the dark side, and the peak of the frequency distribution of the blue component shown by the alternate long and short dash line is generally closer to the bright side. If it does, it is advisable to correct and move it so that it is closer to the center as a whole like the peak of the frequency distribution of the green component shown by the chain line in the figure.

As a result of various experiments, in the present embodiment, the judgment is made based on the median in the frequency distribution, and when the median is less than “85”, it is judged as a dark image and the following γ value is satisfied. Brighten with γ correction.

[0149]   γr = Rmed2 / 85 (30)   γg = Gmed / 85 (31)   γb = Bmed2 / 85 (32) Alternatively,   γr = (Rmed2 / 85) ** (1/2) (33)   γg = (Gmed / 85) ** (1/2) (34)   γb = (Bmed2 / 85) ** (1/2) (35) And

In this case, even if γr, γg, γb <0.7, γr, γg, γb = 0.7. This is because, if such a limit is not set, the night image becomes like the daytime image. Note that if the image is too bright, the image tends to be whitish as a whole, and the image tends to have a low contrast. Therefore, it is preferable to perform processing such as enhancing the saturation.

On the other hand, when the median is larger than "128", it is determined that the image is bright and the image is made dark by the γ correction corresponding to the following γ value.

Γr = Rmed2 / 128 (36) γg = Gmed / 128 (37) γb = Bmed2 / 128 (38) Alternatively, γr = (Rmed2 / 128) ** (1/2) (39) γg = (Gmed / 128) ** (1/2) (40) γb = (Bmed2 / 128) ** (1/2) (41) In this case, even if γr, γg, γb> 1.3, γr, γg, γb = 1.3, and a limit is set so as not to be too dark. It should be noted that if the image is too dark, the color will be overloaded and a dark image will result, so processing such as weakening the saturation emphasis is also suitable. However, such a darkening process may adversely affect a subject in a bright background. This is because, for example, in a landscape image in which the sky occupies half of the image or in a commemorative photo on a sunny day, the face is often dark and crushed by the backlight. In these images, the dark part and the bright part are mixed, and therefore the standard deviation of each color component is often a relatively high value. Therefore, when such a standard deviation is larger than “70”, it is possible not to perform γ correction for darkening. FIG. 25 shows the correspondence relationship when γ correction is performed. If γr, γg, γb <1, the curve expands upward, and if γr, γg, γb> 1, the curve expands downward. Note that the correction of the brightness does not necessarily have to be based on the frequency distribution, and the brightness may be determined from other factors and corrected.

Γr, γg, γb determined as described above
For γ correction with respect to, the following equation is used. Grayscale values r1, G1, B1 after conversion with respect to grayscale values r0, g0, b0 before conversion
Is R1 = 255 * (r0 / 255) ** γr ... (42) G1 = 255 * (g0 / 255) ** γg ... (43) B1 = 255 * (b0 / 255) ** γb ... (44) Note that this γ correction is also executed on the conversion table shown in FIG. That is, as in the case of the contrast enhancement, the correspondence is calculated by inputting the converted component values (R1, G1, B1) shown in FIG. 21 and the converted component values (R1, G1, B1) in the conversion table are obtained. Replace. As a result, by referring to the conversion table, in addition to the offset amount addition and the contrast enhancement process, the brightness correction process is performed at the same time.

Finally, in step S216, the image data is converted, and the image data (rm, g
For m, bm), the process of referring to the conversion table shown in FIG. 21 and obtaining the converted image data (Rm, Gm, Bm) is repeated.

In this embodiment, the correction by the offset amount, the contrast enhancement, and the brightness correction are performed in this order, but it is not always necessary to perform all of them, and It is also possible to implement by appropriately changing each correction method.

For example, in the above-described embodiment, in the contrast enhancement processing, the correction is performed by the conversion equation having a linear correspondence relationship with the component value before conversion, but smoother conversion is performed. Therefore, the so-called S-curve conversion as shown in FIG. 26 may be performed. Further, in this case, it is also possible to use the concept of the standard deviation, which represents the degree of dispersion of the distribution, instead of determining the spread of the frequency distribution at both end positions. Hereinafter, an example in which the contrast is emphasized by the S-curve correspondence using the standard deviation will be described. Although the conversion is performed based on the frequency distribution of each component, the calculation method is common to all of the components, and therefore, the procedure for obtaining the brightness Y after conversion from the brightness y before conversion is shown because the calculation method is common.

There are two ways of thinking about the standard deviation, but in the present embodiment, calculation is performed based on the following equation.

[0158]

[Equation 5]

Yp: brightness of each pixel before conversion ym: average value of brightness of each pixel before conversion The standard deviation corresponds to the spread amount of the brightness distribution, but dispersion is used in the sense of the spread amount. May be. Further, since the total number of gradations is 256 as in the present embodiment, it is possible to obtain the spread amount from the kurtosis k of the distribution.

[0160]

[Equation 6]

The kurtosis of k = 3 here corresponds to the spread amount of the normal distribution.

On the basis of the standard deviation σ which is the spread amount of the brightness distribution thus obtained, the contrast enhancement gives a large number of gradations in the range where the distribution density is large, and a small number of gradations in the range where the distribution density is small. The number is also assigned, and as shown in FIG.
When it becomes a curved curve, the gradation range assigned before conversion
The gradation range RNG1 assigned after conversion with respect to rng0 is increased, and the number of assigned gradations is increased. On the other hand, regarding the range outside the gradation range rng0 on the low luminance side and the high luminance side in the input, the gradation range assigned after the conversion is reduced.

In the present embodiment, the central position ymid of the gradation range is set to "128", and γ1 is given below the central position ymid, and γ2 is given in the range larger than the central position ymid to perform γ correction. , This γ1,
γ2 is determined based on the standard deviation σ. That is, y ≦ 1
At 28, γ1 = (σstd_limit / σ) ** fc ... (47) At y> 128, γ2 = (σ / σstd_limit) ** fc ... (48), and these parameter calculations are executed in step S204. Here, σstd_limit and fc are parameters that are experimentally obtained and given in consideration of the conversion result, and in the present embodiment, σstd_limit is set to “128” and fc is set to “0.1”. Since the standard deviation σ is generally a value smaller than “128”, in these relational expressions, when the standard deviation σ is large, γ2 and γ1 are respectively “1”.
And the slope of the S-shaped curve becomes gentle. This is because when the spread amount is large, the gradation range RNG0 of the conversion destination is compared with the gradation range rng0 centered on the center position ymid.
Means that the brightness does not become so wide, and more specifically, it means that the conversion for expanding the brightness range is not performed when the brightness of the image data is widely distributed.
On the other hand, when the standard deviation σ is small, γ2 and γ1 are separated from “1”, and the slope of the S-shaped curve becomes steep. This means that the gradation range RNG1 of the conversion destination is expanded widely with respect to the gradation range rng0 centered on the center position ymid when the spread amount is small. When the brightness is distributed only in a narrow range, it means to perform conversion for expanding the brightness range.

In the present embodiment, the S-curve correspondence is established by γ correction.
Gradation "0", lower quadrant yq1, center position ymid, upper quadrant yq3, gradation "255" are used as reference points, and gradation "0", center position ymid, and gradation For "255", Y = y is set, and the conversion points at the lower quadrant yq1 and the upper quadrant yq3 are determined based on the standard deviation. Then, the correspondence relationship connecting these five points is obtained by spline interpolation calculation or Newton interpolation. Of course, the three points on the lower side and the three points on the upper side from the center position ymid may be obtained by spline interpolation calculation or Newton's interpolation, respectively.

That is, as shown in FIG. 28, the standard deviations σr, σg and σb are obtained for each component in step S230, γ1 and γ2 are obtained for each color component in step S232, and γ1, γ1 is obtained in step S234. A conversion table is created based on the correspondence using γ2. These steps S230 to S234 are executed instead of the processing of steps S208 to S214 in the flowchart shown in FIG.

Next, the operation of this embodiment having the above configuration will be described step by step.

If a picture is taken by the scanner 11 or the like, image data representing the picture as RGB gradation data is taken into the computer 21, and the CPU is processed by the CPU shown in FIGS.
The image processing program shown in is executed to execute processing for correcting the color reproducibility of image data.

First, in step S102, image data is thinned out within a predetermined error range, and a frequency distribution is obtained for each color component of the selected pixel. The frequency distribution as it is cannot be used. First, it is determined in step S104 whether the image is a binary image such as black and white, and in step S108 it is determined whether the image is a natural image. Except when it is a binary image or when it is not a natural image, in step S110 it is determined whether or not there is a frame portion in the image data, and if there is a frame portion, it is removed, and then in step S114, unclear areas at both ends of the frequency distribution. Get rid of. In this state, the feature vector is obtained for the frequency distribution of each color component, and the similarity of the frequency distribution is checked from the inner product of the feature vectors. When the frequency distributions of the respective color components are not very similar, this proves that the original image data is intentionally out of color balance, and the processing for equalizing the characteristics is not performed. However, if there is some similarity in comparison with a predetermined threshold value, it is determined that the color balance is out of order, and the following characteristics are made uniform.

That is, in step S204, the red component offset dR and the blue component offset dB with respect to the green component are obtained using the upper end of the frequency distribution and the median, and in step S206, the conversion table for the final image data conversion is formed. To do. Succeedingly, in a step S208, a parameter for enhancing the contrast while uniformizing the contrast is calculated for each color component, and a step S21 is performed.
At 0, a conversion table is created to match the balance of contrast for each color component while enhancing the contrast based on the same parameter. Then, step S21
In step 2, a γ correction parameter for equalizing the brightness of each color component is calculated, and a conversion table for performing such γ correction is created.

Finally, in step S216, the image data for all pixels is converted with reference to the conversion table created as described above.

In this case, the processing is divided in step S202 depending on whether or not there is similarity, but when executing the flowchart shown in FIG. 31, the effective value of the offset amount is changed according to the degree of similarity. , The characteristics may or may not be substantially uniformed.

Of course, as described above, such image processing is not performed when the image is not a binary image or a natural image. However, when the image processing of the present invention is performed, color misregistration or the like occurs in a photograph. Despite the image data having poor color reproducibility due to the input device, the color shift, contrast, and brightness are made uniform for each color component, and the contrast is emphasized to produce a good image with sharpness. It will be extremely easy to obtain.

In the above-described embodiment, some parameters are fixed, but the computer 2
On the first screen, the user may be allowed to select via a predetermined GUI.

As described above, the frequency distribution for each color component of the image data is obtained by thinning out the image data in step S102, and it is determined in step 116 whether or not there is a similarity between the frequency distributions, and the similarity is low. If not, it is determined that the characteristics originally found from the frequency distribution are the same, and in steps S204 to S216, the shift is corrected by the offset correction, the contrast enhancement, and the brightness correction, so that the image data having poor color reproducibility is obtained. Even if the image is clear and good, the work can be automated and the color balance can be easily corrected by an unskilled person.

[Brief description of drawings]

FIG. 1 is a block diagram of an image processing system to which an image processing apparatus according to an embodiment of the present invention is applied.

FIG. 2 is a block diagram showing a specific hardware configuration example of the image processing apparatus.

FIG. 3 is a schematic block diagram showing another application example of the image processing apparatus of the present invention.

FIG. 4 is a schematic block diagram showing another application example of the image processing apparatus of the present invention.

FIG. 5 is a flowchart corresponding to frequency distribution detection means and similarity determination means in the image processing apparatus of the present invention.

FIG. 6 is a flowchart corresponding to offset amount correction means, contrast correction means, and brightness correction means in the image processing apparatus of the present invention.

FIG. 7 is a diagram showing coordinates in a conversion source image.

FIG. 8 is a diagram showing a sampling cycle.

FIG. 9 is a diagram showing the number of sampling pixels.

FIG. 10 is a diagram showing a relationship between a conversion source image and sampled pixels.

FIG. 11 is a diagram showing array variables for detecting a frequency distribution.

FIG. 12 is a flowchart of a gray image extraction thinning process.

FIG. 13 is a diagram showing a monochrome image.

FIG. 14 is a diagram showing a luminance distribution of a monochrome image.

FIG. 15 is a diagram showing a state of frequency distribution for each color component in the case of a non-natural image and a natural image.

FIG. 16 is a diagram showing an image with a frame portion.

FIG. 17 is a diagram showing a frequency distribution of an image having a frame portion.

FIG. 18 is a diagram showing edge processing of frequency distribution and edges obtained by the edge processing.

FIG. 19 is a diagram showing a method of extracting elements for forming a feature vector for each color component.

FIG. 20 is a diagram showing a linear relationship that does not require correction of color reproducibility.

FIG. 21 is a diagram showing a conversion table when converting image data based on a frequency distribution.

FIG. 22 is a diagram showing the expansion of the frequency distribution and the range of all gradations.

[Fig. 23] Fig. 23 is a diagram illustrating a case where a contrast enlargement ratio is limited.

FIG. 24 is a diagram showing a frequency distribution that requires uniform brightness.

FIG. 25 is a diagram showing a conversion relationship changed by γ correction.

FIG. 26 is a diagram showing a conversion relationship for emphasizing contrast by the correspondence relationship of S-shaped curves.

FIG. 27 is a diagram showing a conversion relationship when the specified conversion points are connected by an interpolation method.

FIG. 28 is a partial flowchart in the case of correcting contrast and brightness with an S-shaped curve.

FIG. 29 is a diagram showing a state in which the program is transferred from the medium recording the image processing program to the hard disk.

FIG. 30 is a graph showing a change situation of a window function to be used.

FIG. 31 is a flowchart of an image processing program in the case of adjusting an offset amount using a window function.

FIG. 32 is a graph showing a change situation of another window function to be used.

[Explanation of symbols]

10 ... Image input device 11 ... Scanner 11b ... Scanner 12 ... Digital still camera 12a ... Digital still camera 12b ... Digital still camera 13b ... Modem 20 ... Image processing device 21 ... Computer 22 ... Hard disk 30 ... Image output device 31 ... Printer 31a ... Printer 31b ... printer 32 ... Display 32a ... display 41 ... Flexible disk 42 ... CD-ROM 43 ... Hard disk 44 ... ROM 45 ... RAM 46a ... communication line 46b ... Modem 46c ... File server

   ─────────────────────────────────────────────────── ─── Continued front page    F term (reference) 5B057 CA01 CA08 CB01 CB08 CE17                       CH01 CH07 CH11 DB06 DB09                       DC23 DC25                 5C055 AA05 AA07 AA08 AA14 BA06                       BA07 BA08 EA02 EA03 HA37                 5C066 AA01 AA03 AA13 BA13 CA08                       EA13 EC05 HA03                 5C077 MP08 PP12 PP15 PP32 PP37                       PP39 PQ12 PQ19 PQ22 PQ23                       RR06 TT06 TT09                 5C079 HB01 LA02 LA12 LA23 LA24                       LB01 MA01 MA04 MA11 NA18                       NA29 PA01 PA02

Claims (20)

[Claims] 1. For image data in which an image is represented as a set of pixels in a dot matrix by gradation color data consisting of substantially equal color components, each component value of the image data is input to perform a predetermined conversion process. An image processing device that performs conversion to correct the color balance according to the relationship between the input and the output by applying and outputting, and calculates the distribution of gradation color data for each color component and An image processing apparatus comprising: a characteristic equalizing unit that obtains a shift between the color components and that makes the characteristics of the respective color components uniform based on the obtained shift. 2. The image processing apparatus according to claim 1, wherein the characteristic equalizing means determines a degree of similarity between the color components based on the distribution, and if the degree of similarity is small, the correction is performed. An image processing apparatus, comprising: a correction control unit for eliminating the problem. 3. The image processing apparatus according to claim 2, wherein the correction control unit divides a gradation range that the gradation color data can take into a plurality of areas and compares distributions of the respective areas. An image processing apparatus characterized by determining a degree of similarity between respective color components. 4. The image processing apparatus according to claim 2 or 3, wherein the correction control means comprises:
An image processing apparatus, characterized in that a degree of similarity is judged based on an inner product of vectors having distributions of respective color components as elements. 5. The image processing apparatus according to any one of claims 2 to 4, wherein the characteristic equalizing means uses an effective value for equalizing the characteristics, and the correction control means. The image processing apparatus is characterized in that the effective value is effectively changed or invalidated by changing the effective value, and the effective value is continuously changed. 6. The image processing apparatus according to any one of claims 1 to 5, wherein the characteristic equalizing means targets the pixels whose gradation color data of each color component are similar to each other in the distribution target. An image processing device characterized by the following. 7. The image processing apparatus according to claim 1, wherein the characteristic equalizing means determines the characteristic from a predetermined position in the distribution and makes the characteristic uniform. An image processing apparatus, characterized in that an offset amount corresponding to a shift between respective components is obtained and each component value is corrected. 8. The image processing apparatus according to claim 7, wherein the characteristic equalizing means determines an end position of the range of the distribution as a characteristic of the distribution. 9. The image processing apparatus according to claim 7 or 8, wherein the characteristic equalizing means determines that a substantially central position of the distribution is a characteristic of the distribution. Image processing device. 10. The image processing apparatus according to claim 1, wherein the characteristic equalizing means is
An image processing apparatus, characterized in that the spread of the distribution for each color component is made substantially uniform. 11. The image processing apparatus according to claim 10, wherein the characteristic equalizing means expands both ends of the spread of the distribution within an effective gradation range. 12. The image processing apparatus according to claim 10, wherein the characteristic equalizing means gives a large number of gradations to a range having a large distribution density and a small distribution density based on the spread amount of the same distribution. An image processing apparatus characterized by assigning a small number of gradations to a range. 13. The image processing apparatus according to claim 1, wherein the characteristic equalizing means uniformizes the brightness based on the distribution of each component. Processing equipment. 14. The image processing apparatus according to claim 13, wherein the characteristic equalizing means determines whether the image is bright or dark by comparing a substantially central position of the distribution with a predetermined gradation. Processing equipment. 15. The image processing apparatus according to claim 13 or 14, wherein the characteristic equalizing means sets γ for each component based on a determination result of brightness of an image.
An image processing apparatus characterized in that the lightness and darkness of an image are made uniform by correction. 16. For image data in which an image is represented as a set of pixels in a dot matrix by gradation color data consisting of substantially equal color components, each component value of the image data is input to perform a predetermined conversion process. When performing conversion to correct the color balance based on the relationship between input and output by applying and outputting, the distribution of gradation color data is calculated for each color component, and the shift between each color component is also calculated. An image processing method characterized in that characteristics of respective color components are made uniform based on deviation. 17. A medium in which a program for image processing by a computer is recorded, wherein the image data is represented as a set of pixels in a dot matrix form by gradation color data consisting of substantially equal color components. By inputting each component value of the data, performing a predetermined conversion process and outputting it, when performing conversion to correct the color balance in the relationship between input and output, the distribution of gradation color data for each color component is calculated. A medium on which an image processing program is recorded, which is characterized in that a shift between each color component is obtained and a characteristic between the color components is made uniform based on the obtained shift. 18. For image data in which an image is represented as a set of pixels in a dot matrix by gradation color data consisting of substantially equal color components, each component value of the image data is input to perform a predetermined conversion process. An image processing apparatus that performs conversion to correct the color balance depending on the relationship between input and output by applying and outputting, and obtains the degree of similarity of the distribution of gradation color data for each color component, An image processing apparatus characterized in that the color balance is not corrected when the degree of similarity is small. 19. For image data in which an image is represented as a set of pixels in a dot matrix by gradation color data consisting of substantially equal color components, each component value of the image data is input to perform a predetermined conversion process. When performing conversion to correct the color balance according to the relationship between input and output by applying and outputting, calculate the degree of similarity of gradation color data distribution for each color component, and if this degree of similarity is small, color balance is calculated. An image processing method characterized by not modifying. 20. A medium on which a program for image processing by a computer is recorded, wherein the image data is represented by a set of pixels in a dot matrix with gradation color data consisting of substantially equal color components. By inputting each component value of the data, performing a predetermined conversion process and outputting it, when performing the conversion that corrects the color balance in the relationship between the input and the output, the distribution of the gradation color data for each color component A medium on which an image processing program is recorded, wherein a degree of similarity is obtained, and when the degree of similarity is small, the color balance is not corrected.