JP3698205B2 - Image processing apparatus, image processing method, and medium on which image processing program is recorded - Google Patents

Description

[0001]
BACKGROUND OF THE INVENTION
The present invention relates to an image processing apparatus, an image processing method, and a medium on which an image processing program is recorded, and more particularly to an image processing apparatus, an image processing method, and a medium on which an image processing program is recorded.
[0002]
[Prior art]
Conventionally, as this type of image processing apparatus, an apparatus disclosed in Japanese Patent Publication No. 7-66318 is known.
[0003]
In the image processing apparatus shown in the same publication, the relationship between the luminance y ′ after conversion and the luminance y ′ after conversion as shown in equation (1), and the image is based on the parameter a or parameter b selected by the operator. The brightness of the data is converted. As a result, an image in which the contrast is enhanced for image data having a low contrast is obtained.
[0004]
y ′ = ay + b (1)
[0005]
[Problems to be solved by the invention]
In the above-described conventional image processing apparatus, a plurality of settings with different levels of contrast strength are prepared in advance and switched. Therefore, the most suitable one corresponding to actual image data cannot be automatically applied.
In particular, in the case of changing based on the expression (1), if the whole image is bright, only the brightness is enhanced. If the entire image is dark, only the darkness is enhanced. Sometimes.
[0006]
Further, although the contrast strength can be operated on a television or the like, it has not been possible to automate the execution of optimum enhancement on each image.
[0007]
SUMMARY An advantage of some aspects of the invention is that it provides an image processing apparatus capable of automatically adjusting contrast, an image processing method, and a medium on which an image processing program is recorded.
[0008]
[Means for Solving the Problems]
In order to achieve the above object, an image processing apparatus that performs predetermined image processing on image data of an image composed of a plurality of pixels, and obtains a luminance distribution for each pixel in the image data, and the luminance distribution When converting the image data so that the image data is widely dispersed in the effective luminance range that can be taken by the image data, the maximum distribution luminance is obtained from the luminance distribution, and the maximum distribution luminance within the effective luminance range belongs. It is good also as a structure which correct | amends the brightness per pixel in the said image data according to the range .
[0009]
In the above configuration , when the image data represents the information of each pixel by decomposing the image into dot matrix pixels, the brightness distribution in the image data is obtained to obtain the contrast width in the image data. Things can be quantified to some extent, and the distribution can be expanded corresponding to the reproducible range after quantification. Further, since it may not be possible to deal with cases where the entire image is bright or dark just by increasing the contrast, it is determined whether or not the image data is generally bright. As the method, the maximum distribution luminance of the conversion source luminance y is used, and if this maximum distribution luminance is on the bright side, the entire image is darkened by γ correction, and if the maximum distribution luminance is on the dark side, the whole is obtained by γ correction. By making it brighter, the entire brightness is automatically corrected, which cannot be obtained only by contrast enhancement. Here, the maximum distribution luminance of the luminance y of the conversion source may be obtained by median or may be obtained by an average value.
[0010]
Needless to say, the numerical value is not necessarily a specific numerical value, and may be handled as a numerical value in the process, or may be handled as a signal magnitude. As an example that more specifically represents this, the invention according to claim 1 is an image processing apparatus that performs predetermined image processing on image data of an image composed of a plurality of pixels, and is a pixel unit in the image data. A luminance distribution detecting means for detecting the luminance distribution in the image, and converting the image data so that the detected luminance distribution is widely dispersed in an effective luminance range that can be taken by the image data. In addition to obtaining the luminance, a γ value is obtained in accordance with the belonging range of the maximum distribution luminance within the effective luminance range, and the luminance information for each pixel in the image data is γ-corrected based on the γ value. The image data conversion means is provided.
[0011]
In order to obtain the luminance distribution in handling the image data of the predetermined image, the luminance distribution detecting means detects the luminance distribution in pixel units in the image data. Then, the image data conversion means obtains the maximum distribution luminance from the luminance distribution when converting the image data so that the detected luminance distribution is widely dispersed in an effective luminance range that the image data can take, A γ value is obtained in accordance with the belonging range of the maximum distribution luminance within the effective luminance range, and luminance information for each pixel in the image data is γ-corrected based on the γ value.
[0012]
That is, the so-called contrast width from the brightest brightness to the darkest brightness can be determined by obtaining the brightness distribution of the image data in pixel units, and the contrast magnification can be determined by comparing with the reproducible brightness width range. Therefore, after that, it is sufficient to enlarge the luminance distribution so that the enlargement ratio is obtained. For example, after the luminance distribution as a whole is aggregated based on the luminance at each pixel, the luminance distribution is the same when the aggregated luminance distribution is not widely dispersed in the effective luminance range that the image data can take. The luminance information of each pixel in the image data is converted so as to be widely distributed in the luminance range.
[0013]
There are several methods for enlarging the luminance distribution, and the luminance distribution of the image data may be enlarged within a possible range. To explain the main point, the statistical width of the detected luminance distribution is compared with the width of the reproducible luminance range to obtain the degree of enlargement as the enlargement ratio, and the upper and lower ends of the enlarged luminance distribution. Is to obtain an adjustment value so as to be within the luminance range, and to individually correct the luminance of each pixel. As a specific example , the invention according to claim 2 is the image processing apparatus according to claim 1 , wherein the image data converting means has a luminance before conversion when the reproducible luminance range is yrange. The luminance Y of the conversion destination is obtained from the maximum value ymax and the minimum value ymin of the distribution range of y and the luminance based on the following equation.
[0014]
Y = ay + b (2)
However, a = yrange / (ymax−ymin) (3)
b = −a · ymin or yrange−a · ymax (4)
In the above conversion equation, if Y <0, Y = 0, and if Y> yrange, Y = yrange.
[0015]
This conversion is so-called linear expansion, and although the conversion formula itself is the same as the conventional one, it is significant that the parameters are selected by the image data conversion means. Regardless of the selection of b, Y = 0 when y = ymin, and Y = yrange when y = ymax. Then, the luminance distribution is uniformly spread within a range of yrange which is a reproducible luminance range. In this example, it is a so-called narrow-sense linear transformation, and of course, it is not necessary to be limited to this, and a broad-sense nonlinear transformation can also be implemented. Further, it goes without saying that the conversion formula is merely an example and can be applied even if the conversion formula has the same meaning.
[0016]
[0017]
[0018]
In converting the luminance by various methods, the invention according to claim 3 is the image processing apparatus according to claim 2 , wherein the conversion destination luminance Y is calculated and stored within the range of the conversion source luminance y. In addition, at the time of conversion, the correspondence is called up and converted.
[0019]
Although it is not impossible to calculate the luminance every time based on the conversion formula, the range of values that the luminance distribution can take is determined. For this reason, if the conversion destination luminance Y is calculated and stored in advance based on the conversion source luminance y, it is possible to perform conversion only by invoking the correspondence at the time of conversion.
[0020]
In converting the luminance, the image data may be included as luminance data, or may be indirect only including luminance data. Of course, if direct luminance data is included, it may be converted, and even if it is indirect luminance data, it may be converted into luminance data before performing predetermined luminance conversion. However, the luminance conversion does not have to be very accurate, and it can be said that it is sufficient to know roughly.
[0021]
In that sense, since strict accuracy is not required, the invention according to claim 4 is the image processing apparatus according to any one of claims 1 to 3 , wherein the image data corresponds to a plurality of brightness levels. In this case, the luminance is calculated by linear addition of the same component values.
[0022]
When the image data is represented by so-called RGB (red, green, and blue) gradation data, it can be said that each component value for red, green, and blue corresponds to the luminance. For this reason, it can be said that linear addition of the same component values sufficiently represents luminance, and can be an extremely easy conversion method.
[0023]
The luminance distribution as an image does not necessarily have to be obtained for all the pixels of the image data as the luminance for each pixel is obtained. For example, the luminance distribution is obtained by thinning out the image data corresponding to a predetermined extraction rate. It is good.
[0024]
For the purpose of obtaining the distribution, it is possible to obtain a luminance distribution with a certain degree of certainty according to the extraction rate even if thinning is performed at a predetermined extraction rate without obtaining luminance for all pixels.
[0025]
Here, although there are various thinning methods, a predetermined number of extractions may be secured on the short side in the vertical and horizontal ranges.
[0026]
Since the image is flat, the image data is also naturally distributed in the vertical and horizontal directions, but in determining a certain extraction rate, by ensuring a certain number of extractions at least on the short side, the extraction rate is reduced. The certainty corresponding to it is held.
[0027]
Furthermore, when obtaining the luminance distribution, a configuration may be adopted in which the inner portion is regarded as the end portion by a predetermined distribution ratio from the actual end portion.
[0028]
Considering the luminance distribution of the image data statistically, it is reasonable to consider that the distribution is up to both end portions within a reproducible luminance range although it is extremely small. Therefore, both ends of the actual luminance distribution are always at both ends in a reproducible luminance range. If these both ends are employed, the enlargement ratio is substantially “1”, and thus the original effect cannot be achieved.
[0029]
However, if the predetermined distribution ratios at both ends are excluded, the bottom portion having a very small distribution is statistically ignored and appropriately ignored. For this reason, this range is used as a reference for determining the degree of enlargement.
[0030]
The predetermined distribution ratio only needs to be capable of ignoring the bottom part with extremely small distribution, and may be a certain number of pixels of the total number of pixels, or the distribution is less than a certain number. Sometimes it can be considered as an end.
[0031]
Furthermore, the invention according to claim 5 is the image processing apparatus according to any one of claims 1 to 4 , wherein the range of the brightness distribution to be enlarged is a predetermined amount from the end of the actual reproducible range. The configuration is set inside.
[0032]
In an actual image, there are a highlight portion and a high shadow portion, and the human eye is likely to realize a subtle difference in these portions. Therefore, if the enlargement is artificially applied to the end of the so-called reproducible luminance range, the highlight portion feels white and the high shadow portion appears black.
[0033]
However, by setting the range of the luminance distribution to be enlarged inward by a predetermined amount from the end part of the actual reproducible range, no intentional enlargement is performed at both end parts.
[0034]
Furthermore, the invention according to claim 6 is the image processing apparatus according to any one of claims 1 to 5 , wherein a restriction is set on an expansion range of the luminance distribution.
[0035]
It may be natural that the contrast is narrow. For example, in the evening scenery, it is natural that the width of the luminance distribution is narrow, and if it is enlarged more than necessary, it becomes a daytime scenery. A similar example is possible in other cases, and this phenomenon is avoided by setting a limit on the expansion range of the luminance distribution.
[0036]
Furthermore, the invention according to claim 7 is the image processing apparatus according to claim 6 , wherein the corresponding position of the luminance distribution range before conversion and the corresponding position of the luminance distribution range after conversion within the reproducible range are held. The configuration is as follows.
[0037]
If the luminance distribution is used as much as possible within a reproducible range, there is no remaining range in which the luminance distribution can be expanded. However, if the expansion range is limited, there remains a remaining range in which the luminance distribution can be expanded. In other words, there remains a degree of freedom in which range is enlarged around, and the atmosphere of the image may change depending on the center. Therefore, the corresponding position of the luminance distribution range before conversion within the reproducible range and the corresponding position of the luminance distribution range after conversion are held so that the center does not change.
[0038]
Various ways of capturing the center of the luminance distribution of the image in this sense are possible. As an example , the invention according to claim 8 is the luminance distribution range before conversion in the image processing apparatus according to claim 7. The luminance distribution is expanded so that the ratio of the expandable range remaining at the upper end and the lower end of the image is maintained even after conversion.
[0039]
In other words, since it is only necessary to hold the center substantially, it is not always necessary to directly hold the center, but conversely, the enlargement remaining at the upper and lower ends of the luminance distribution range before conversion A possible range is captured, and the luminance distribution is expanded so that the ratio of this range is maintained even after conversion, so that the center is substantially maintained.
[0040]
Furthermore, the invention according to claim 9 is the image processing apparatus according to any one of claims 1 to 8 , wherein the binary image data is determined based on the luminance distribution, and if the binary image data, the luminance is determined. The distribution is not expanded.
[0041]
Since it can be said that there is no substantial luminance distribution for the binary image, if the binary image data is determined from the luminance distribution, the luminance distribution is not expanded.
[0042]
Since the binary image data can also have a certain color, it can have two luminance levels corresponding to the presence or absence of the color. Although it is possible to determine whether or not the brightness of the color, if there is no information suggesting it , the invention according to claim 10 can be reproduced in the image processing apparatus according to claim 9. It is configured to determine that the binary image data is black and white when the luminance distribution is concentrated at both ends in the range.
[0043]
That is, for a black and white image, it can be determined that the luminance distribution is concentrated at both ends within the reproducible range.
[0044]
Further, the frame portion of the image data may be determined based on the protruding luminance distribution, and if there is a frame portion, the frame data may not be used for expanding the luminance distribution.
[0045]
When an image is processed, it can frequently occur that it has a frame. If it exists as a single-color frame, only the luminance distribution corresponding to that color naturally protrudes. Accordingly, if such a prominent luminance distribution is used as a criterion for the enlargement determination, an effective determination cannot be made. Therefore, it is determined that the frame portion is not used for expanding the luminance distribution.
[0046]
Furthermore, as an example, a configuration may be adopted in which the luminance distribution concentrated at the end within the reproducible range is determined to be the frame portion.
[0047]
A white frame or a black frame is frequently used and may be generated depending on a trimming result, and corresponds to an end portion within a reproducible range. Therefore, the luminance distribution concentrated on the end portion is determined as the frame portion.
[0048]
By the way, the invention according to claim 11 is configured such that in the image processing apparatus according to any one of claims 1 to 10 , the luminance distribution is not enlarged when the image data is not a natural image.
[0049]
The narrowness of contrast tends to be a problem for natural images such as photographs, and it can be said that it is almost unnecessary for things like business graphs. Conversely, modifying something like a business graph can lead to a difference from the creator's image. Therefore, the luminance distribution is expanded only in the case of such a natural image.
[0050]
As an example of determining whether or not the image is a natural image , the invention according to claim 12 is the image processing device according to claim 11 , wherein the image data is determined not to be a natural image when the luminance distribution exists in a line spectrum shape. The natural image determining means is provided.
[0051]
It can be said that the luminance distribution has a smooth width as a feature of the natural image. Therefore, if the luminance distribution appears in a line spectrum, it can be determined that the image is not a natural image. In the invention according to claim 12 configured as described above, the natural image determination means determines the state of the luminance distribution, and when it exists in the shape of a line spectrum, determines that the image data is not a natural image, and thereby the luminance distribution Will not be expanded.
[0052]
As described above, the method for converting the image data so as to expand the distribution corresponding to the reproducible range by obtaining the luminance distribution in the image data is not necessarily limited to a substantial device, It can be easily understood that it also functions as a method. Therefore, the invention according to claim 13 is an image processing method for performing predetermined image processing on image data of an image composed of a plurality of pixels, and obtains a luminance distribution in units of pixels in the image data. In converting the image data so that the luminance distribution is widely dispersed in the effective luminance range that can be taken by the image data, the maximum distribution luminance is obtained from the luminance distribution, and the maximum distribution luminance within the effective luminance range is obtained. The γ value is obtained in accordance with the affiliation range, and the luminance information for each pixel in the image data is γ-corrected based on the γ value .
[0053]
That is, it is not necessarily limited to a substantial apparatus, and there is no difference that the method is also effective.
[0054]
By the way, such an image processing apparatus may exist alone, or may be used in a state of being incorporated in a certain device. The idea of the invention is not limited to this, and includes various aspects. . Therefore, it can be changed as appropriate, such as software or hardware.
[0055]
As an example, a printer driver that converts image data corresponding to printing ink based on input image data and prints it on a predetermined color printer supports a range that can be reproduced by obtaining a luminance distribution in the image data. Thus, the image data can be converted to expand the distribution.
[0056]
In other words, the printer driver converts the image data input corresponding to the printing ink. At this time, the luminance distribution of the input image data is obtained, and the distribution is expanded corresponding to the reproducible range. The input image is converted so as to obtain a distribution and printed.
[0057]
In the case of software for an image processing apparatus as an embodiment of the idea of the invention, it naturally exists on a recording medium on which such software is recorded and must be used. As an example, the invention according to claim 14 is a medium in which image data of an image composed of a plurality of pixels is input by a computer and an image processing program for performing predetermined image processing is recorded, wherein the pixels in the image data are recorded. In the step of detecting the luminance distribution in units, and converting the image data so that the detected luminance distribution is widely dispersed in the effective luminance range that the image data can take, the maximum distribution luminance is calculated from the luminance distribution. Obtaining a γ value according to the belonging range of the same maximum distribution luminance within the effective luminance range, and γ-correcting luminance information for each pixel in the image data based on the γ value ; It is set as the structure which comprises.
[0058]
Of course, the recording medium may be a magnetic recording medium, a magneto-optical recording medium, or any software recording medium to be developed in the future. In addition, the duplication stages such as the primary duplication product and the secondary duplication product are equivalent without any question. In addition, even when the communication method is used as a supply method, the present invention is not changed, and the same applies to the case where data is written on a semiconductor chip.
[0059]
Further, even when a part is software and a part is realized by hardware, there is nothing completely different in the idea of the invention, and a part is stored on a recording medium, and it is appropriately changed as necessary. It may be in the form of being read. Furthermore, it goes without saying that the present invention can also be applied to an image processing apparatus built in a color facsimile machine, color copier, color scanner, digital camera, digital video, or the like.
[0060]
【The invention's effect】
As described above, according to the present invention, by obtaining the luminance distribution, it becomes possible to quantitatively handle the contrast width of the image, and the degree of enlargement within a reproducible range can be obtained. Therefore, an image processing apparatus capable of automating contrast enhancement can be provided. It is also possible to adjust the degree of brightness that cannot be corrected only by contrast enhancement.
[0061]
According to the invention of claim 2 , the luminance distribution can be effectively expanded within a predetermined range of gradations.
[0062]
[0063]
Further, according to the invention of claim 3 , conversion can be facilitated.
[0064]
Furthermore, according to the fourth aspect of the present invention, the luminance can be easily obtained with a necessary and sufficient accuracy.
[0065]
Furthermore, the amount of processing can be reduced.
[0066]
Furthermore, the bias of the image extraction point is eliminated, and the luminance distribution is likely to be accurate.
[0067]
Furthermore, it is possible to obtain a luminance distribution that is more effective for judgment.
[0068]
Furthermore, according to the fifth aspect of the present invention, the highlight portion and the high shadow portion can be prevented from being crushed.
[0069]
Further, according to the sixth aspect of the present invention, it is possible to prevent the atmosphere of the image from being changed by enhancing the contrast too much.
[0070]
Furthermore, according to the invention concerning Claim 7 , the atmosphere represented by the brightness of an image can be hold | maintained, Furthermore, according to the invention concerning Claim 8 , it can be performed easily.
[0071]
Further, according to the invention according to claim 9 , it is possible to easily determine an unnecessary condition for expansion of the luminance distribution so as not to perform the expansion, and further, according to the invention according to claim 10 , It is possible to efficiently determine a monochrome image having a high frequency.
[0072]
Furthermore, it is possible to prevent the processing from being inaccurate due to the brightness of the frame portion that tends to appear in the image, and it is possible to easily determine a frequently-used black and white frame portion.
[0073]
Furthermore, according to the invention according to claim 11 , it can be performed only in the case of a natural image that needs to be expanded in luminance distribution. Further, according to the invention according to claim 12 , whether or not the image is a natural image is determined. Can be easily determined.
[0074]
According to the thirteenth aspect of the present invention, it is possible to provide an image processing method that can quantitatively handle an image such as the contrast width of an image by obtaining a luminance distribution and automate contrast enhancement. According to the invention of claim 14 , it is possible to provide a medium on which an image processing program for automating contrast enhancement is recorded in the same manner.
[0075]
DETAILED DESCRIPTION OF THE INVENTION
Hereinafter, embodiments of the present invention will be described with reference to the drawings.
[0076]
FIG. 1 is a block diagram illustrating an image processing system according to an embodiment of the present invention, and FIG. 2 is a block diagram illustrating a specific hardware configuration example.
[0077]
In the figure, an image input device 10 captures an image and outputs image data to the image processing device 20. The image processing device 20 performs image processing such as predetermined contrast enhancement and outputs the image data to the image output device 30. Then, the image output apparatus 30 displays an image with enhanced contrast.
[0078]
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 the computer 21 and the hard disk 22. Specific examples of the output device 30 include a printer 31, a display 32, and the like.
[0079]
In the present image processing system, since an optimum contrast is to be given to an image having a low contrast, it is image data obtained by taking a photograph with a scanner 11 as the image input device 10 or a digital still camera 12. The image data having a low contrast taken in step 1 is processed and input to the computer system as the image processing apparatus 20.
[0080]
The image processing apparatus 20 determines at least a luminance distribution detecting unit that extracts a luminance distribution, and an extent that the luminance distribution can be expanded within a reproducible range based on the detected luminance distribution. Image data conversion means for converting data is configured. Of course, the image processing apparatus 20 may also be a color conversion unit that corrects a color difference for each model, or a resolution conversion unit that converts a resolution corresponding to each model. . In this example, the computer 21 executes each image processing program stored in the internal ROM or the hard disk 22 while using a RAM or the like. Such an image processing program is supplied via various recording media such as a CD-ROM, floppy disk, and MO, and connected to an external network via a public communication line by a modem or the like. And downloading and installing data.
[0081]
The execution result of this image processing program is obtained as image data with enhanced contrast as will be described later, and is printed by the printer 31 that is the image output device 30 based on the obtained image data, or by the same image output device 30. It is displayed on a certain display 32. More specifically, this image data is RGB (green, blue, red) gradation data, and the image is dots arranged in a grid pattern in the vertical direction (height) and the horizontal direction (width). It is configured as matrix data. That is, the image data represents information of each pixel by decomposing the image into pixels in a dot matrix.
[0082]
In the present 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 a digital still camera as shown in FIG. A system in which an image processing device for emphasizing contrast is incorporated in 12a and the converted image data is displayed on the display 32a or printed on the printer 31a may be used. As shown in FIG. 4, in the printer 31b that inputs and prints image data without using a computer system, the image data input through the scanner 11b, the digital still camera 12b, the modem 13b, or the like is automatically received. It is also possible to configure so as to enhance contrast.
[0083]
Of the image processing executed by the computer 21, luminance distribution detection processing corresponding to luminance distribution detection means and luminance conversion processing corresponding to image data conversion means are shown in FIGS. 5 and 6, respectively.
[0084]
FIG. 5 mainly corresponds to luminance distribution detection processing. First, the luminance distribution detection processing will be described.
[0085]
Before describing how to express luminance, the pixels to be distributed will be described. As shown in step S102 of FIG. 5, a thinning process for thinning out the target pixels is executed. As shown in FIG. 7, in the case of a bitmap image, it is formed as a two-dimensional dot matrix consisting of predetermined dots in the vertical direction and predetermined dots in the horizontal direction, and all pixels can be obtained if an accurate luminance distribution is obtained. It is necessary to examine the brightness. However, this distribution extraction process is intended to determine the width of the distribution and need not be accurate. Therefore, it is possible to perform thinning to such an extent that it is within a certain error range. According to the statistical error, the error with respect to the number of samples N can be expressed as 1 / (N ** (1/2)). However, ** represents a power.
Therefore, in order to perform processing with an error of about 1%, N = 10000.
[0086]
Here, the bitmap screen shown in FIG. 7 has the number of pixels of (width) × (height), and the sampling period ratio is
ratio = min (width, height) / A + 1 (5)
And Here, min (width, height) is the smaller of width and height, and A is a constant. Further, the sampling period ratio here indicates how many pixels are sampled, and the pixels marked with ◯ in FIG. 8 indicate the case where the sampling period ratio = 2. That is, one pixel is sampled every two pixels in the vertical and horizontal directions, and every other pixel is sampled. The number of sampling pixels in one line when A = 200 is as shown in FIG.
[0087]
As can be seen from the figure, except for the case where the sampling period ratio = 1 in which sampling is not performed, when there is a width of 200 pixels or more, the number of samples is at least 100 pixels. Therefore, in the case of 200 pixels or more in the vertical direction and the horizontal direction, (100 pixels) × (100 pixels) = (10000 pixels) is secured, and the error can be reduced to 1% or less.
[0088]
Here, the reason for using min (width, height) as a reference is as follows. For example, as shown in FIG. 10 (a), when width >> height is satisfied and the sampling period ratio is determined by the longer width, as shown in FIG. 10 (b). In addition, in the vertical direction, only two lines of the upper end and the lower end may be extracted. However, if the sampling period ratio is determined based on the smaller one as min (width, height), thinning is performed so as to include the intermediate portion in the smaller vertical direction as shown in FIG. Will be able to.
[0089]
In this example, thinning is performed with an accurate sampling period for pixels in the vertical and horizontal directions. This is suitable for processing while thinning out pixels that are sequentially input. However, when all the pixels have been input, the pixels may be selected by randomly specifying coordinates in the vertical direction or the horizontal direction. In this way, when the necessary minimum number of pixels, such as 10,000 pixels, is determined, the extraction process is repeated at random until the number of pixels reaches 10,000, and the extraction is stopped when the number of pixels reaches 10,000.
[0090]
If the pixel data of the pixel thus selected has luminance as its component element, the distribution can be obtained using the luminance value. However, even in the case of image data in which the luminance value is not a direct component value, the component value that indirectly represents the luminance is provided. Accordingly, the luminance value can be obtained by performing conversion from the color space where the luminance value is not a direct component value to the color space where the luminance value is a direct component value.
[0091]
The color conversion between the different color spaces is not uniquely determined by the conversion formula, but the correspondence between the color spaces having the respective component values as coordinates is obtained and stored. It is necessary to perform sequential conversion with reference to the color conversion table. In relation to the table, the component values are expressed as gradation values. In the case of 256 gradations having a three-dimensional coordinate axis, a color conversion table of about 16.7 million (256 × 256 × 256) elements is obtained. Must have. As a result of considering the efficient use of storage resources, instead of preparing correspondences for all coordinate values, we usually prepare correspondences for appropriate discrete grid points and use interpolation operations together. Like to do. Since this interpolation calculation is possible through several multiplications and additions, the amount of calculation processing becomes enormous.
[0092]
In other words, if a full-size color conversion table is used, the processing amount is reduced, but the table size becomes unrealistic. If the table size is made realistic, the calculation processing amount becomes unrealistic. Many.
[0093]
In view of such a situation, in the present embodiment, as used in the case of a television or the like, the following conversion formula for obtaining luminance from the three primary colors of RGB is adopted. That is, for the luminance yp at the point P, from the RGB component values (Rp, Gp, Bp),
yp = 0.30Rp + 0.59Gp + 0.11Bp (6)
And In this way, the luminance value can be obtained by only three multiplications and two additions.
[0094]
In this embodiment, as a result of targeting the RGB color space, such a conversion formula is adopted, but since each component value indicates the brightness of the color in the background, each component is When the value is viewed alone, it has a property of linearly corresponding to the luminance. Therefore, more roughly, without considering each addition ratio, simply yp = (Rp + Gp + Bp) / 3 (7)
It is not impossible to simplify it, and
yp = Gp (7) '
In this way, it is also possible to use the green component value having the largest proportion in the formula (6) as the luminance value.
[0095]
In the thinning-out process, the luminance is obtained from the RGB image data at the same time for the selected pixel, and the distribution is obtained. Eventually, the width of this distribution is obtained in step S116, but there are matters to consider before that.
[0096]
The first is a case where the image is a binary image such as a black and white image. The concept of contrast enhancement is inappropriate for binary images including black and white images. If there is a black and white image as shown in FIG. 11, the luminance distribution for this image is concentrated at both ends in the reproducible range as shown in FIG. Basically, it concentrates on the gradation “0” and the gradation “255”.
[0097]
Therefore, when performing the black and white check in step S104, it can be determined whether or not the sum of the number of pixels of gradation “0” and gradation “255” matches the number of pixels selected by thinning. In the case of a monochrome image, the non-enlargement process is executed in step S106 in order to interrupt the process without executing the following process. In the present embodiment, the distribution extraction process and the luminance conversion process are roughly divided. Therefore, in this non-enlarging process, a flag is set so that the subsequent luminance conversion process is not executed, and the distribution extraction process is terminated.
[0098]
The binary data is not limited to black and white, and there may be binary data with color. In such a case as well, there is no need to perform the process of enhancing the contrast, and if the distribution state is examined and the distribution is concentrated only on two values (one is approximately “0”), the process is interrupted as binary data. Should be achieved.
[0099]
Second, consider whether the image is like a business graph or a natural picture like a photograph. Although a process of enhancing contrast is sometimes required for natural images, it is often preferred that contrast is not enhanced for business graphs or paintings. Accordingly, in step S108, it is checked whether or not the image is a natural image.
[0100]
Natural images have a very large number of colors including shadows, but business graphs and certain types of paintings 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. In order to accurately determine the number of colors, it is necessary to determine how many of 16.7 million colors are used as described above, but this is not realistic. On the other hand, when the number of colors is extremely small as in the business graph, the probability that the colors are different and have the same luminance is low. That is, the approximate number of colors can be determined based on the luminance. If the number of colors is small, the luminance distribution is sparse, and in a business graph, it appears as a line spectrum. For this reason, in step S108, the number of luminance values whose distribution number is not “0” among the luminances of 256 gradations is counted. If it is less than “25”, which is approximately 10%, it is determined that the image is not a natural image, and the non-enlargement process is executed in step S106 as in the case of binary data. Of course, it is possible to appropriately change whether or not the color is “25” or less as the threshold value.
[0101]
It is also possible to determine whether or not the distribution is a line spectrum by the adjacent ratio of luminance values whose distribution number is not “0”. That is, it is determined whether or not there is a distribution number in the luminance values adjacent to the luminance value that is not “0”. If at least one of the two adjacent luminance values is adjacent, nothing is performed, and if both are not adjacent, the count is performed. As a result, the ratio between the number of luminance values other than “0” and the count value is used. Just judge. For example, if the number of luminance values other than “0” is “20” and the number of non-adjacent ones is “20”, it can be seen that the distribution is a line spectrum.
[0102]
Further, when the image processing program is executed via the operating system, it is possible to make a determination based on the extension of the image file. Of bitmap files, especially photographic images are compressed, and an implicit extension is often used to indicate the compression method. For example, an extension “JPG” indicates 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, the extension of the file will be answered. What is necessary is just to judge that there exists and to perform contrast emphasis. In addition, if the extension is unique to a business graph such as “XLS”, it can be determined that contrast enhancement is not performed.
[0103]
A third consideration is whether or not there is a frame around the image as shown in FIG. If such a frame is white or black, as shown in FIG. 14, the luminance distribution appears as a line spectrum at both ends in a range where the influence can be reproduced, and both ends corresponding to the internal natural image. It also appears as a smooth luminance distribution inside
[0104]
Of course, since it is more appropriate not to take the frame portion into consideration of the luminance distribution, the sum of the number of pixels of the gradation “0” and the gradation “255” is sufficiently large in the frame check in step S108, and It is determined whether the number of pixels selected by thinning does not match. If the result is affirmative, it is determined that there is a frame portion, and frame processing is performed in step S112. In this frame processing, the number of pixels of gradation “0” and gradation “255” in the luminance distribution is set to “0” in order to ignore the frame portion. Thereby, in the following process, it can handle like the thing without a frame part.
[0105]
In this example, a white or black frame portion is targeted, but there may be a case where there is a specific color frame. In such a case, a protruding line spectrum appears in the original smooth curve drawn by the luminance distribution. Accordingly, a line spectrum having a large difference between adjacent luminance values may be considered as a frame portion and not subjected to luminance distribution. In this case, since the color may be used other than the frame portion, an average of luminance values on both sides may be assigned.
[0106]
In the case where the luminance distribution is expanded after taking the above-described considerations, both ends of the luminance distribution are obtained in step S116. As shown in FIG. 15, the luminance distribution in the natural image appears approximately in a mountain shape. Of course, there are various positions and shapes. The width of the luminance distribution is determined by where the both ends are determined. However, the point where the base is simply extended and the number of distributions becomes “0” cannot be the both ends. This is because the number of distributions may change around “0” in the base portion, and if it is statistically viewed, it changes while approaching “0” as much as possible.
[0107]
For this reason, in the distribution range, portions that have passed a certain distribution ratio from the side with the highest luminance and the side with the lowest luminance are defined as both ends of the distribution. In the present embodiment, as shown in FIG. 15, this distribution ratio is set to 0.5%. Of course, this ratio can be appropriately changed. In this way, by cutting the upper end and the lower end by a certain distribution ratio, it is possible to ignore white spots and black spots caused by noise or the like. That is, if such a process is not performed, if there is even a white point or a black point, it will be at both ends of the luminance distribution. In many cases, the bottom end is gradation “0”, and the top end is Although the gradation becomes “255”, such a problem can be eliminated by setting the portion that is inward by 0.5% of the number of pixels from the upper end portion as the end portion.
[0108]
In the actual processing, 0.5% of the number of pixels to be processed (the total number of pixels selected in the thinning process or the total number of pixels corresponding to the frame portion is deleted) is calculated, and the upper end of the reproducible luminance distribution is calculated. The number of distributions is accumulated in the order from the luminance value and the luminance value at the lower end in order to obtain a luminance value that is 0.5%. Hereinafter, this upper end side is called ymax, and the lower end side is called ymin.
[0109]
In the present embodiment, the upper end and the lower end are obtained through such processing on the luminance distribution, but it is also possible to obtain both ends under statistical processing. For example, it is also possible to adopt a method in which the portion where the percentage of the average value of the luminance values is less than or equal to the edge is used.
[0110]
The above processing corresponds to the distribution detection processing. Next, luminance conversion processing for converting image data based on the luminance values ymax and ymin thus determined will be described. As described above, when the non-enlargement process is executed in step S106, the predetermined process is detected with reference to a predetermined flag in step S202, and the image process is terminated without performing the following process.
[0111]
In the basic luminance conversion, when the reproducible luminance range is “0” to “255”, the luminance y of the conversion destination is calculated from the luminance y before conversion and the maximum value ymax and minimum value ymin of the luminance distribution range. Y is obtained based on the following equation.
[0112]
Y = ay + b (2)
However, a = 255 / (ymax−ymin) (3) ′
b = −a · ymin or 255−a · ymax (4) ′
In the above conversion equation, if Y <0, Y = 0, and if Y> 255, Y = 255. Here, a is an inclination and b can be said to be an offset. According to this conversion equation, as shown in FIG. 16, the luminance distribution having a certain narrow width can be expanded to a reproducible range. Basically, in expanding the luminance distribution range, since the number of pixels does not change, the areas of the histograms match.
[0113]
By the way, when the luminance distribution is expanded by making full use of the reproducible range in this way, the highlight part may be lost in white or the high shadow part may be lost in black. In order to prevent this, the reproducible range is limited in this embodiment. That is, the luminance value “5” is left as a range that does not expand to the upper end and the lower end of the reproducible range. As a result, the parameters of the conversion equation are as follows:
[0114]
a = 245 / (ymax−ymin) (8)
b = 5-a · ymin or 250-a · ymax (9)
In this case, conversion is not performed in the range of y <ymin and y> ymax.
[0115]
In this embodiment, in order to retain the highlight portion and the high shadow portion, the luminance value is uniformly set from the end portion and the range of “5” is set as the non-enlarged region. If the image output apparatus is relatively easy to reproduce, the range may be narrowed. If the reproducibility is even weaker, the range may be made larger. Further, the enlargement rate may be limited gradually in the border area instead of being uniformly enlarged.
[0116]
Further, FIG. 17A shows a case where the luminance distribution of the image is narrow, but if the enlargement ratio of the luminance distribution (corresponding to a) is applied as described above, the reproducible range is shown. In some cases, a very large enlargement ratio can be obtained. Then, in the dusk state like the evening, the range of contrast from the brightest part to the dark part is naturally narrow, but as a result of trying to enlarge the contrast greatly for this image, it is converted like a daytime image. It can be. Since such conversion is not desired, the enlargement ratio is limited so that a is not 1.5 (˜2) or more. As a result, twilight is expressed as twilight.
[0117]
A case where no limitation is imposed on the enlargement ratio is shown by a one-dot chain line in FIG. 17A, and no extra portion remains in a reproducible range after conversion. However, when the expansion range is limited, as shown by the two-dot chain line in FIG. 5B, there is a degree of freedom where the distribution after the conversion is brought. It can be too dark or too dark.
[0118]
For this reason, in this embodiment, the ratio (m1: m2) of the remaining areas remaining on the upper end side and the lower end side within the range in which the luminance distribution before conversion can be reproduced is the upper end side and lower end side after conversion. Are converted so as to coincide with the ratio of the remaining area (n1: n2). Hereinafter, how to obtain the parameter b in this case will be described.
[0119]
In the luminance distribution of the image before conversion,
m1 = ymin
m2 = 255−ymax
here,
m1 + m2 = 255− (ymax−ymin)
Therefore, if ydif = ymax−ymin,
m1 + m2 = 255−ydif
In the luminance distribution of the converted image,
n1 = Ymin
n2 = 255-Ymax
Similarly,
n1 + n2 = 255- (Ymax-Ymin)
= 255-a (ymax-ymin)
= 255−a ・ ydif
It becomes.
[0120]
Since m1: m2 = n1: n2,
n1 = m1 (n1 + n2) / (m1 + m2)
= Ymin (255−a · ydif) / (255−ydif)
On the other hand, since Y = ay + b, b = Y−ay.
b = Ymin−a · ymin
= Ymin {(255−a · ydif) / (255−ydif) −a} (10)
It becomes. Moreover, when it calculates | requires using Ymax,
b = Ymax−a · ymax
= 255− (255−ymax) (255−a · ydif) / (255−ydif) −a · ymax (11)
The parameter b can be obtained as described above, and step S204 is ended.
[0121]
By the way, it is unreasonable to execute the conversion equation (Y = ay + b) every time the luminance is converted. This is because the range that the luminance y can take is only “0” to “255”, and it is possible to obtain the luminance Y after conversion corresponding to all the values that the luminance y can take in advance. It is. Therefore, this correspondence is obtained in step S206 and stored as a table as shown in FIG.
[0122]
Image data can be changed when such a conversion table is formed. However, it is extremely effective to not only enhance the contrast by expanding the luminance range but also adjust the brightness. For example, when the peak of the luminance distribution is on the whole dark side as shown by the solid line in FIG. 19, it is better to move the mountain to the bright side as shown by the chain line. In the case where the peaks of the luminance distribution are close to the bright side as shown by the solid line in FIG. 20, the peaks may be moved to the dark side as shown by the chain line.
[0123]
As a result of various experiments, in the present embodiment, the median ymed in the luminance distribution is obtained, and when the median ymed is less than “85”, the image is determined to be a dark image and γ correction corresponding to the following γ values is performed. Brighten.
[0124]
γ = ymed / 85 (12)
Or
γ = (ymed / 85) ** (1/2) (13)
And
[0125]
In this case, even if γ <0.7, γ = 0.7. This is because if such a limit is not set, the night image becomes like the daytime. Note that if the image is too bright, the overall image becomes whitish and the contrast tends to be low, so that processing such as enhancement of saturation is suitable.
[0126]
On the other hand, when the median ymed is larger than “128”, the image is judged to be a bright image and darkened by γ correction corresponding to the following γ values.
[0127]
γ = ymed / 128 (14)
Or
γ = (ymed / 128) ** (1/2) (15)
And In this case, even if γ> 1.3, a limit is set so that γ = 1.3 so as not to be too dark. Note that if the image is too dark, the color will be excessive and the image will become darker, so processing such as reducing the saturation enhancement is suitable. However, this darkening process may adversely affect a subject in a bright background. This is because, for example, a landscape image in which the sky is half of the image or a commemorative photo on a sunny day, the face is often darkened by backlighting. In the case of these images, a dark part and a bright part are mixed, and thus the standard deviation ystd of the luminance is often a relatively high value. Therefore, when the standard deviation of brightness ystd> 70, the γ correction for darkening is not performed.
[0128]
This γ correction may be performed on the luminance distribution before conversion or on the luminance distribution after conversion. FIG. 21 shows the correspondence when γ correction is performed. When γ <1, the curve swells upward, and when γ> 1, the curve swells downward. Of course, the result of such γ correction may be reflected in the table shown in FIG. 18, and the same correction is performed on the table data in step S208.
[0129]
Finally, image data is converted in step S210. Up to this point, a correspondence relationship for luminance conversion has been obtained, and for example, it was not a conversion relationship for component values (Rp, Gp, Bp) on the RGB coordinate axes. However, the conversion formula (2) can also be applied in correspondence with the RGB component values (Rp, Gp, Bp). That is, the component values (R, G, B) after conversion with respect to the component values (r, g, b) before conversion are
R = a · r + b (16)
G = a · g + b (17)
B = a · b + b (18)
Can also be sought. This is clear from the fact that both the equations (2) and (4) show a linear correspondence. In addition, the RGB component values (r, g, b), (R, G, B) are also in the same range corresponding to the luminance y, Y ranging from “0” to “255”. Therefore, it can be said that the above-described conversion table of luminance y and Y may be used as it is.
[0130]
Therefore, in step S210, the image data (R, G, B) after conversion is obtained by referring to the conversion table corresponding to the equations (16) to (18) for the image data (r, g, b) of all pixels. The process will be repeated.
[0131]
Next, the operation of the present embodiment having the above configuration will be described step by step.
[0132]
If a photograph is taken with the scanner 11 or the like, image data representing the photograph as RGB gradation data is taken into the computer 21, and the CPU executes the image processing program shown in FIGS. 5 and 6 to execute the image data. The process of enhancing the contrast is executed.
[0133]
First, in step S102, the image data is thinned out within a predetermined error, and the luminance y for the selected pixel is obtained to obtain a distribution. The 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 the image is a binary image or not a natural image, in step S110, it is determined whether the image data has a frame portion. If there is a frame portion, 0.5% of the upper and lower ends of the luminance distribution obtained by removing the frame portion is determined. To obtain the two ends ymax and ymin of the distribution.
[0134]
If both ends ymax and ymin of the luminance distribution are obtained,
Y = ay + b (2)
a = 245 / (ymax−ymin) (8)
For b, either: b = 5-a · ymin or 250-a · ymax (9)
b = ymin {(255−a · ydif) / (255−ydif) −a} (10)
b = 255− (255−ymax) (255−a · ydif) / (255−ydif) −a · ymax (11)
In step S204, parameters a and b are obtained from the following relational expression. In step S206, the conversion relationship from luminance y to luminance Y is stored in a table. In step S208, γ correction is executed as necessary, and in step S210, image data for all pixels is converted with reference to the completed conversion table.
[0135]
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, the contrast is very weak in the state of the photograph. Regardless, by correcting so as to widen the luminance range, a clear image with clear contrast can be obtained.
[0136]
In the above-described embodiment, the restriction on the enlargement ratio is constant, but the user may be able to select on the computer 21 via a predetermined GUI. It is also possible for the user to designate a part of the image data and execute such contrast enhancement processing only within the range.
[0137]
In this way, after obtaining the luminance y for the pixels of the image data while thinning out in step S102, the edge that is inside by a predetermined distribution ratio at the upper end and the lower end is regarded as the end of the luminance distribution. (Step S116) Since the parameter a corresponding to the enlargement ratio within the reproducible range of luminance and the parameter b corresponding to the offset amount can be obtained, conversion is performed on the luminance y of the conversion source. The prior luminance Y can be automatically converted using a relational expression such as Y = ay + b.
[0138]
Note that the calculation speed of the input image of the video camera 14 may not be in time. Therefore, in such a case, the luminance is detected for each shooting scene to detect the luminance distribution, and the luminance enlargement table is created on the assumption that the scene has the same tendency. The luminance distribution may be enlarged corresponding to the enlargement table for each frame. Of course, if there is a sufficient calculation speed, conversion may be performed for each frame. Of course, the same conversion can be performed on the receiver side.
[Brief description of the 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 illustrating a specific hardware configuration example of the image processing apparatus.
FIG. 3 is a schematic block diagram illustrating another application example of the image processing apparatus of the present invention.
FIG. 4 is a schematic block diagram illustrating another application example of the image processing apparatus of the present invention.
FIG. 5 is a flowchart showing a luminance distribution extraction processing portion in the image processing apparatus of the present invention.
FIG. 6 is a flowchart showing a luminance conversion processing portion in the image processing apparatus of the present invention.
FIG. 7 is a diagram illustrating coordinates in a conversion source image.
FIG. 8 is a diagram showing a sampling period.
FIG. 9 is a diagram illustrating the number of sampling pixels.
FIG. 10 is a diagram illustrating a relationship between a conversion source image and pixels to be sampled.
FIG. 11 is a diagram illustrating a black and white image.
FIG. 12 is a diagram illustrating a luminance distribution of a black and white image.
FIG. 13 is a diagram illustrating an image with a frame portion.
FIG. 14 is a diagram illustrating a luminance distribution of an image having a frame portion.
FIG. 15 is a diagram showing edge processing of luminance distribution and edges obtained by the edge processing.
FIG. 16 is a diagram showing a range of luminance with a reproducible range of expansion of luminance distribution;
FIG. 17 is a diagram illustrating a case where a restriction is imposed on the enlargement ratio of the luminance distribution.
FIG. 18 is a diagram illustrating a conversion table when a luminance distribution is enlarged.
FIG. 19 is a diagram illustrating a concept of brightening by γ correction.
FIG. 20 is a diagram illustrating a concept of darkening by γ correction.
FIG. 21 is a diagram illustrating a correspondence relationship of luminance changed by γ correction.
[Explanation of symbols]
DESCRIPTION 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

Claims (14)

An image processing apparatus that performs predetermined image processing on image data of an image composed of a plurality of pixels,
A luminance distribution detecting means for detecting a luminance distribution in pixel units in the image data;
In converting the image data so that the detected luminance distribution is widely dispersed in an effective luminance range that can be taken by the image data, a maximum distribution luminance is obtained from the luminance distribution, and within the effective luminance range. An image comprising image data conversion means for obtaining a γ value in accordance with the belonging range of the maximum distribution luminance and γ-correcting luminance information for each pixel in the image data based on the γ value. Processing equipment. 2. The image processing apparatus according to claim 1 , wherein the image data converting means has a luminance y before conversion, a maximum value ymax and a minimum value ymin of the luminance distribution range when the reproducible luminance range is yrange. An image processing apparatus characterized in that a conversion destination luminance Y is obtained based on the following equation.
Y = ay + b
However, a = yrange / (ymax−ymin)
b = -a.ymin or yrange-a.ymax
In the above conversion equation, if Y <0, Y = 0, and if Y> y range, Y = yrange. The image processing apparatus according to claim 2 , wherein the conversion destination luminance Y is calculated and stored within a possible range of the conversion source luminance y, and the conversion is performed by invoking a correspondence relationship at the time of conversion. An image processing apparatus. In the image processing device according to any one of claims 1 to 3 , when the image data is represented by a plurality of component values corresponding to the luminance, the luminance calculation is obtained by linear addition of the same component values. An image processing apparatus. 5. The image processing apparatus according to claim 1, wherein the range of the luminance distribution to be enlarged is set inward by a predetermined amount from the end of the actual reproducible range. Image processing device. 6. The image processing apparatus according to claim 1 , wherein a restriction is set on an expansion range of the luminance distribution. 7. The image processing apparatus according to claim 6 , wherein the corresponding position of the luminance distribution range before conversion and the corresponding position of the luminance distribution range after conversion are held within a reproducible range. apparatus. 8. The image processing apparatus according to claim 7 , wherein the luminance distribution is expanded so that the ratio of the expandable range remaining at the upper end and the lower end of the luminance distribution range before the conversion is maintained after the conversion. A featured image processing apparatus. 9. The image processing apparatus according to claim 1, wherein the binary image data is determined based on the luminance distribution, and the luminance distribution is not enlarged if the binary image data. An image processing apparatus. 10. The image processing apparatus according to claim 9 , wherein when the luminance distribution is concentrated at both ends within a reproducible range, the image processing apparatus determines that the binary image data is black and white. 11. The image processing apparatus according to claim 1 , wherein the luminance distribution is not enlarged when the image data is not a natural image. 12. The image processing apparatus according to claim 11 , further comprising a natural image determination unit that determines that the image data is not a natural image when the luminance distribution exists in a spectral form. An image processing method for performing predetermined image processing on image data of an image composed of a plurality of pixels, wherein a luminance distribution in pixel units in the image data is obtained and the luminance distribution is an effective value that the image data can take In converting the image data to be widely dispersed in the luminance range, the maximum distribution luminance is obtained from the luminance distribution , and the γ value is obtained according to the belonging range of the maximum distribution luminance within the effective luminance range, An image processing method, wherein luminance information in units of pixels in the image data is γ-corrected based on the γ value . A medium in which image data of an image composed of a plurality of pixels is input by a computer and an image processing program for performing predetermined image processing is recorded,
Detecting a luminance distribution in pixel units in the image data;
In converting the image data so that the detected luminance distribution is widely dispersed in an effective luminance range that can be taken by the image data, a maximum distribution luminance is obtained from the luminance distribution, and within the effective luminance range. An image processing program comprising : obtaining a γ value according to a range to which the maximum distribution luminance belongs, and γ-correcting luminance information for each pixel in the image data based on the γ value The medium that recorded.

Images