JPH10198802A - Image processor, image processing method, and medium recording image processing program - Google Patents

Abstract

PROBLEM TO BE SOLVED: To provide an image processor, in which contrast is automatically adjustable, by finding luminance distribution in image data and converting the image data so as to expand the distribution corresponding to a reproducible range. SOLUTION: In the image processing system, an image input device 10 outputs image data to an image processor 20 by picking up images or the like, this image processor 20 performs prescribed image processing such as contrast emphasis and outputs images to an image output device 30 and this image output device 30 displays the contrast emphasizes images. The image processor 20 is composed of a luminance distribution detecting means and the image data conversion means, at least. In order to find the contrast distribution for handling prescribed image data, the luminance distribution detecting means detects the luminance distribution of these image data for the unit of a pixel. While using this detected luminance distribution, the image data conversion means converts the image data by distributing the quantity of this luminance distribution to be expanded within the reproducible range.

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 on which an image processing program is recorded, and more particularly, to an image processing apparatus for processing contrast of image data, an image processing method, and an image processing program. Related media.

[0002]

2. Description of the Related Art Conventionally, an image processing apparatus of this type is disclosed in Japanese Patent Publication No. 7-66318.

In the image processing apparatus disclosed in the publication, the relationship between the converted luminance y 'and the converted luminance y' is associated as shown in equation (1), and the relation is set to the parameter a or parameter b selected by the operator. The luminance of the image data is converted based on this. As a result, an image in which contrast is enhanced for image data having low contrast is obtained.

Y ′ = ay + b (1)

[0005]

In the above-described conventional image processing apparatus, a plurality of settings in which the degree of contrast is changed are prepared in advance and are switched. Therefore, it has not been possible to automatically apply the most suitable data corresponding to actual image data. In particular, when changing based on the expression (1), if the whole image is a bright image, only the brightness is enhanced, or if the whole image is dark, the darkness is only enhanced. Sometimes it was.

[0006] Further, while the contrast level can be operated even on a television or the like, it has not been possible to perform automation such as executing optimum emphasis on each image.

SUMMARY OF THE INVENTION The present invention has been made in view of the above problems, and has as its object to provide an image processing apparatus, an image processing method, and a medium on which an image processing program is recorded, which can automatically adjust contrast.

[0008]

According to a first aspect of the present invention, an image is divided into dot matrix pixels, and a predetermined image processing is performed on image data representing information of each pixel. And converts the image data so as to obtain a luminance distribution in the image data and expand the distribution according to a reproducible range.

According to the first aspect of the present invention, when the image data represents information of each pixel by decomposing the image into pixels of a dot matrix, the luminance distribution in the image data is determined. By obtaining the value, the width of the contrast in the image data can be quantified to some extent, and the distribution can be enlarged corresponding to the reproducible range after being quantified.

[0010] Of course, the digitization does not necessarily require a specific numerical value, and may be treated as a numerical value in the processing process, or may be treated as a signal magnitude. As a more specific example, the invention according to claim 2 is the image processing apparatus according to claim 1, wherein the image processing apparatus includes a luminance distribution detecting unit that detects a luminance distribution of image data in a pixel unit in a predetermined image. The image processing apparatus further includes image data conversion means for determining the extent to which the luminance distribution can be expanded within a reproducible range based on the detected luminance distribution and converting the image data.

In order to determine the luminance distribution when handling image data of a predetermined image, the luminance distribution detecting means detects the luminance distribution of the same image data in pixel units. Then, using the detected luminance distribution, the image data conversion means determines the amount of enlargement of the same luminance distribution within a reproducible range, and converts the image data.

That is, if the luminance distribution of image data in pixel units is obtained, the width of the so-called contrast from the brightest luminance to the darkest luminance can be determined, and the contrast expansion ratio can be determined by comparing the range of the reproducible luminance width. Can be determined, and thereafter, it is sufficient to expand the luminance distribution so as to have the enlargement ratio. For example, after summing the luminance distribution as a whole based on the luminance at each pixel, if the totaled luminance distribution is not widely distributed over the valid luminance range that the image data can take, the luminance distribution is the same. The luminance information of each pixel in the image data is converted so as to be widely distributed in the luminance range.

Any number of methods are available for expanding the luminance distribution, and the luminance distribution of the image data may be expanded within a possible range. The point is that the statistical width of the detected luminance distribution and the width of the reproducible luminance range are compared to determine the degree of enlargement as an enlargement ratio, and the upper and lower ends of the enlarged luminance distribution are calculated. Is to determine an adjustment value to fall within this luminance range, and individually correct the luminance of each pixel. As a specific example, the invention according to claim 3 is the image processing apparatus according to claim 2, wherein the image data conversion means sets the luminance before conversion when the range of reproducible luminance is yrange. The conversion destination luminance Y is obtained from the maximum value ymax and the minimum value ymin of the distribution range of y and luminance based on the following equation.

Y = ay + b (2) where a = yrange / (ymax−ymin) (3) b = −a · ymin or yrange−a · ymax (4) In the above conversion formula, Y <0 Then, Y = 0 and Y> y
If it is range, Y = yrange.

This conversion is a so-called linear expansion, and although the conversion formula itself is the same as the conventional one, it is significant that its parameters are selected by the image data conversion means. Regardless of the selection of b, if y = ymin, Y
= 0, and if y = ymax, then Y = yrange. Then, the luminance distribution spreads uniformly within the range of yrange, which is the range of reproducible luminance. In this example, it is a so-called linear conversion in a narrow sense, and needless to say, it is not limited to this, and a non-linear conversion in a broad sense can be performed. Further, it is needless to say that the conversion formula is merely an example, and that a conversion formula having the same meaning can be applied.

Further, it may not be possible to cope with the case where the entire image becomes bright or dark simply by increasing the contrast, and the invention according to claim 4 is the invention according to claim 2 or 3. In the image processing apparatus, the luminance distribution enlarging means obtains the maximum distribution luminance of the conversion source luminance y and obtains the conversion destination luminance Y by γ correction based on the belonging range of the maximum distribution luminance.

As a method for judging whether or not the image data is bright as a whole, the maximum distribution luminance of the conversion source luminance y is used, and if the maximum distribution luminance is on the bright side, it is entirely darkened by γ correction. If the maximum distribution luminance is on the dark side, the overall brightness is automatically corrected which cannot be obtained only by enhancing the contrast, for example, by making the whole image brighter by γ correction. Here, the maximum distribution luminance of the conversion source luminance y may be obtained by a median or an average value.

In converting the luminance by various methods, the invention according to claim 5 is an image processing apparatus according to any one of claims 2 to 4, wherein the conversion is performed within a range where the luminance y of the conversion source can take. Calculate and store the luminance Y of the conversion destination,
At the time of conversion, the correspondence is revoked and converted.

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 luminance Y of the conversion destination is calculated and stored in advance based on the luminance y of the conversion source, the conversion can be performed only by invoking the correspondence at the time of conversion.

In converting the luminance, the image data may include the luminance data in some cases, or may include the luminance data only indirectly. Of course,
If it includes direct luminance data, it may be converted, or even if it is indirect luminance data, it may be converted into luminance data and then subjected to predetermined luminance conversion. However, the conversion of the luminance does not have to be extremely accurate, and it can be said that it is only necessary to roughly know.

Since strict accuracy is not required in that sense, the invention according to claim 6 is based on claims 1 to 5.
The image processing apparatus according to claim 5, wherein when the image data is represented by a plurality of component values corresponding to the luminance, the calculation of the luminance is obtained by linear addition of the same component value.

When image data is represented by so-called RGB (red, green, blue) gradation data, it can be said that each component value for red, green, and blue corresponds to luminance. For this reason, it can be said that linear addition of the same component value sufficiently represents luminance, and can be a very easy conversion method.

Assuming that the luminance for each pixel is determined, the luminance distribution as an image does not necessarily need to be determined for all the pixels of the image data. For example, the invention according to claim 7 is based on claims 1 to 6 In any one of the image processing apparatuses described above, the image data is thinned in accordance with a predetermined extraction rate to obtain a luminance distribution.

In order to obtain the distribution, it is possible to obtain a luminance distribution having a certain degree of accuracy according to the extraction rate even if thinning is performed at a predetermined extraction rate without obtaining the luminance for all the pixels. Can be.

Here, although there are various methods of thinning out, the invention according to claim 8 is the image processing apparatus according to claim 7, wherein the predetermined number of extractions is shorter on the shorter side in the vertical and horizontal directions. It is configured to be secured.

Since an image is planar, its image data is naturally distributed in the vertical and horizontal directions. To determine a certain extraction rate, at least a certain number of extractions must be secured on the short side. The certainty corresponding to the extraction rate is maintained.

According to a ninth aspect of the present invention, in the image processing apparatus according to any one of the first to eighth aspects, when a luminance distribution is obtained, an inner portion is separated from an actual end by a predetermined distribution ratio. It is configured to be regarded as a unit.

If the luminance distribution of the image data is considered statistically, it is reasonable to consider that the distribution is to the both end portions in 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 two ends are adopted, the enlargement ratio becomes substantially "1", and the original effect cannot be achieved.

However, considering the distribution ratio except for the predetermined distribution ratio at both ends, the tail portion with extremely small distribution is appropriately ignored statistically. Therefore, this range is used as a criterion for judging the degree of enlargement.

The predetermined distribution ratio may be any value as long as the tail portion with extremely small distribution can be neglected, and may be a fixed number of pixels of the total number of pixels.
When the distribution becomes smaller than a certain number, the distribution may be regarded as an end.

Further, according to a tenth aspect of the present invention, in the image processing apparatus according to any one of the first to ninth aspects, the range of the luminance distribution to be enlarged is larger than the end of the actual reproducible range. It is configured to be set inside by a predetermined amount.

In an actual image, there are a highlight portion and a high shadow portion, and it is easy for human eyes to realize a subtle difference between these portions. Therefore, if an artificial enlargement is applied to the end of the so-called reproducible luminance range, the highlight portion will appear white and the high shadow portion will appear black.

However, by setting the range of the luminance distribution to be enlarged inside by a predetermined amount from the end of the actual reproducible range, artificial enlargement is not performed at both ends.

Further, the invention according to claim 11 is the image processing apparatus according to any one of claims 1 to 10, wherein a limit is set to an expansion range of the luminance distribution.

In some cases, the contrast is narrow. For example, it is natural that the width of the luminance distribution is narrow in the evening scene, and if it is expanded beyond necessity, it becomes a day scene. A similar example is possible in other cases, and such a phenomenon is avoided by setting a limit on the enlarged range of the luminance distribution.

According to a twelfth aspect of the present invention, in the image processing apparatus according to the eleventh aspect, a corresponding position of a luminance distribution range before conversion and a corresponding position of a luminance distribution range after conversion within a reproducible range. Are held.

If the maximum luminance distribution is used within a reproducible range, there is no remaining range in which the luminance distribution can be expanded. However, if the enlargement range is limited, a remaining range where the luminance distribution can be enlarged remains. In other words, there remains a degree of freedom as to which area is to be enlarged at the center, and the atmosphere of the image may change depending on the center. Therefore, the corresponding position of the luminance distribution range before the conversion and the corresponding position of the luminance distribution range after the conversion within the reproducible range are maintained so that their centers do not change.

The center of the luminance distribution of an image in this sense can be interpreted in various ways. As an example, the invention according to claim 13 is based on the image processing apparatus according to claim The configuration is such that 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 is maintained after the conversion.

That is, since the center only needs to be held substantially, it is not always necessary to directly capture and hold the same center. Conversely, the center remains at the upper and lower ends of the luminance distribution range before conversion. The range which can be expanded is captured, and the luminance distribution is expanded so that the ratio of this range is maintained even after the conversion, and the center is substantially held.

According to a fourteenth aspect of the present invention, in the image processing apparatus according to any one of the first to thirteenth aspects, the binary image data is determined based on the luminance distribution and the binary image data is determined. If so, the brightness distribution is not expanded.

Since it can be said that there is substantially no luminance distribution in a binary image, when the binary image data is determined from the luminance distribution, the luminance distribution is not expanded.

Since the binary image data can have a certain color, it can have two luminances corresponding to the presence or absence of the color. Although it is possible to determine whether or not the color has the luminance, if there is no information indicating the same, the invention according to claim 15 can be reproduced by the image processing apparatus according to claim 14. When the luminance distribution is concentrated at both ends of the range, the image data is determined to be monochrome binary image data.

That is, for a monochrome image, it can be said that the luminance distribution is concentrated at both ends within the reproducible range, and the determination can be made.

According to a sixteenth aspect of the present invention, in the image processing apparatus according to any one of the first to thirteenth aspects, the frame part of the image data is determined based on the prominent luminance distribution. If there is, the configuration is such that the data in the frame portion is not used for expanding the luminance distribution.

When an image is processed, what often happens is that the image has a frame. If the image exists as a single color frame, only the luminance distribution corresponding to the color naturally protrudes. Therefore, if such a prominent luminance distribution is used as a reference for judging the enlargement, it may not be possible to make an effective judgment.

Further, as an example, the invention according to claim 17 is the image processing apparatus according to claim 16, wherein the luminance distribution concentrated at the end within the reproducible range is the frame portion. There is a configuration for determination.

A white frame or a black frame is frequently used and employed, and may also be generated as a result of trimming.
It corresponds to the end within the reproducible range. Therefore, the luminance distribution concentrated at this end is determined as the frame.

The invention according to claim 18 is the image processing apparatus according to any one of claims 1 to 17, wherein the luminance distribution is not expanded when the image data is not a natural image. It is.

It is natural images such as photographs that narrow contrast is likely to cause a problem, and it can be said that such images are hardly necessary in business graphs. Conversely, tweaking things like business graphs can result in a different image from the creator. Therefore,
The luminance distribution is expanded only in the case of such a natural image.

As an example of the determination as to whether or not the image data is a natural image, in the image processing apparatus according to the present invention, when the luminance distribution exists in a linear spectrum, the image data It is configured to include a natural image determination unit that determines that the image is not the same.

As a feature of the natural image, it can be said that the luminance distribution has a smooth width. Therefore, if the luminance distribution appears in the form of a line spectrum, it is generally safe to judge that the image is not a natural image. In the invention according to claim 19 configured as described above, the natural image determining unit determines the state of the luminance distribution,
If the image data exists in the form of a line spectrum, it is determined that the image data is not a natural image, so that the luminance distribution is not enlarged.

As described above, the method of obtaining the luminance distribution in the image data and converting the image data so as to expand the distribution in accordance with the reproducible range need not be limited to a substantial device. However, it can be easily understood that it also functions as the method. Therefore, claim 20
The present invention relates to an image processing method for performing predetermined image processing on image data representing information of each pixel by decomposing an image into dot matrix pixels, and as a whole based on the luminance at each pixel. Tabulated luminance distribution, and if the tabulated luminance distribution is not widely distributed over the effective luminance range that the image data can take, each of the image data is distributed so that the luminance distribution is widely distributed over the same luminance range. The configuration is such that the information on the luminance of the pixel is converted.

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

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. Is included. Therefore, it may be software or hardware,
It can be changed as appropriate.

As one example, a printer driver that converts input image data into image data corresponding to printing ink and prints the image data on a predetermined color printer also obtains a luminance distribution in the image data and reproduces the image data. , The image data can be converted so as to expand the distribution.

That is, the printer driver converts the input image data corresponding to the printing ink. At this time, the printer driver obtains the luminance distribution of the input image data and expands the distribution according to the reproducible range. The input image is converted so as to have an enlarged distribution and printed.

When the software of the image processing apparatus is realized as an example of realizing the idea of the present invention, the software naturally exists on a recording medium on which such software is recorded, and it must be said that the software is used. As one example, the invention according to claim 21 records an image processing program for decomposing an image into dot matrix pixels, inputting image data representing information of each pixel by a computer, and performing predetermined image processing. A medium, inputting the image data, and totalizing the luminance distribution as a whole based on the luminance at each pixel; and distributing the totaled luminance distribution widely over an effective luminance range that the image data can take. If not, converting the luminance information of each pixel in the image data so that the luminance distribution is widely dispersed in the same luminance range.

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. Also, the duplication stages of the primary duplicated product, the secondary duplicated product, and the like are equivalent without any question. In addition, the present invention is still used even when the supply method is performed using a communication line, and the same applies to a case where the information is written on a semiconductor chip.

Further, even when a part is realized by software and a part is realized by hardware, there is no difference in the concept of the present invention. It may be in a form that is appropriately read in accordance with it. Further, it is needless to say that the present invention can be applied to an image processing apparatus built in a color facsimile machine, a color copier, a color scanner, a digital camera, a digital video, or the like.

[0060]

As described above, according to the present invention, by obtaining the luminance distribution, it is possible to quantitatively handle the image such as the width of the contrast, and the degree of enlargement within the reproducible range is obtained. Therefore, it is possible to provide an image processing apparatus capable of automating contrast enhancement.

According to the third aspect of the present invention, the luminance distribution can be effectively expanded within a predetermined range of gradations.

Further, according to the invention of claim 4,
It is possible to adjust the degree of brightness that cannot be corrected only by enhancing the contrast.

Further, according to the invention of claim 5,
Conversion can be facilitated.

Further, according to the invention of claim 6,
The luminance can be easily obtained with a necessary and sufficient degree of accuracy.

Further, according to the seventh aspect of the present invention,
The processing amount can be reduced.

Further, according to the invention of claim 8,
The bias of the extraction points of the image is eliminated, and the luminance distribution is likely to be accurate.

Further, according to the ninth aspect of the present invention,
A more effective luminance distribution can be obtained.

Further, according to the tenth aspect of the present invention, it is possible to prevent a highlight portion and a high shadow portion from being crushed.

Further, according to the eleventh aspect, it is possible to prevent the contrast of the image from being excessively emphasized and changing the atmosphere of the image.

Further, according to the twelfth aspect of the present invention, it is possible to maintain the atmosphere represented by the brightness of the image, and according to the thirteenth aspect of the present invention, it is possible to easily execute it. Can be.

Further, according to the fourteenth aspect of the present invention, it is possible to easily determine an unnecessary condition for expanding the luminance distribution so as not to perform the expansion. According to this, it is possible to efficiently determine a frequent black and white image.

Further, according to the sixteenth aspect of the present invention, it is possible to prevent the processing from becoming inaccurate due to the brightness of the frame portion that tends to appear in the image. The frequently used black and white frame portion can be easily determined.

Further, according to the eighteenth aspect of the present invention, it is possible to perform the processing only in the case of a natural image that requires an enlargement of the luminance distribution. Can be easily determined.

According to the twentieth aspect of the present invention, there is provided an image processing method capable of calculating a luminance distribution and quantitatively treating an image such as the width of a contrast, and automating the enhancement of the contrast. According to the invention according to claim 21, it is possible to provide a medium in which an image processing program for automatically enhancing contrast is recorded.

[0075]

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 an example of a specific hardware configuration.

In the figure, an image input device 10 outputs image data to an image processing device 20 by taking an image or the like, and the image processing device 20 performs image processing such as predetermined contrast enhancement and performs image processing. 30 and the image output device 30 displays an image with enhanced contrast.

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. A specific example of the image output device 30 corresponds to the printer 31, the display 32, or the like.

In the present image processing system, an image having low contrast is provided with an optimum contrast. Therefore, image data obtained by taking a photograph with the scanner 11 as the image input device 10 may be used.
Image data with a low contrast captured by the digital still camera 12 is processed, and the image processing device 2
0 is input to the computer system.

The image processing apparatus 20 includes at least a luminance distribution detecting means for extracting a luminance distribution and a degree of expansion of the luminance distribution within a reproducible range based on the detected luminance distribution. To convert the image data. Of course, the image processing apparatus 20
May be a color conversion means for correcting a color difference depending on each model, or a resolution conversion means for converting 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. Note that such an image processing program is supplied via various recording media such as a CD-ROM, a floppy disk, and an MO, and is connected to an external network via a public communication line using a modem or the like, and is connected to a software. Downloading and introducing data is also being carried out.

The execution result of this image processing program is obtained as image data with enhanced contrast as described later, and the image output device 3 is executed based on the obtained image data.
The image is printed by the printer 31 which is 0 or displayed on the display 32 which is the same image output device 30. This image data is more specifically RGB (green, blue, red) gradation data, and the image is displayed in the vertical direction (heig).
ht) and dot matrix data arranged in a grid in the horizontal direction (width). That is, the image data represents the information of each pixel by decomposing the image into dot matrix pixels.

In this embodiment, a computer system is incorporated between image input / output devices to perform image processing. However, such a computer system is not always required, and as shown in FIG. An image processing device for enhancing contrast is incorporated in the digital still camera 12a, and the digital still camera 12a displays the converted image data on the display 32a or the printer 31a.
May be a system that prints the data. As shown in FIG. 4, in a printer 31b that inputs and prints image data without using a computer system, image data input via a scanner 11b, a digital still camera 12b, a modem 13b, or the like is automatically converted. It is also possible to configure so that contrast is enhanced.

Among the image processing executed by the computer 21, a luminance distribution detecting process corresponding to the luminance distribution detecting means and a luminance converting process corresponding to the image data converting means are shown in FIGS. 5 and 6, respectively.

FIG. 5 mainly corresponds to the luminance distribution detection processing. First, the luminance distribution detection processing will be described.

Before explaining how to represent luminance, pixels to be distributed will be described. As shown in step S102 of FIG. 5, a thinning process for thinning out target pixels is executed. As shown in FIG. 7, in the case of a bitmap image, a two-dimensional dot matrix including predetermined dots in the vertical direction and predetermined dots in the horizontal direction is established. It is necessary to check the brightness for. However, this distribution extraction process is aimed at finding the width of the distribution,
It does not have to be accurate. Therefore, it is possible to perform the thinning to the extent that it falls within a certain error range. According to the statistical error, the error with respect to the number of samples N is approximately 1 /
(N ** (1/2)). Here, ** indicates a power. Therefore, in order to perform processing with an error of about 1%, N = 10000.

Here, the number of pixels of the bitmap screen shown in FIG. 7 is (width) × (height), and the sampling period ratio is ratio = min (width, height) / A + 1 (5). Here, min (width, height)
ht) is the smaller of width and height, and A is a constant. The sampling period ratio here indicates how many pixels are sampled. Pixels marked with a circle in FIG.
The case where tio = 2 is shown. In other words, one pixel is sampled every two pixels in the vertical direction and the horizontal direction, and sampling is performed every other pixel. When A = 200, the number of sampling pixels in one line is as shown in FIG.

As can be seen from the figure, except for the case where the sampling period ratio = 1 in which sampling is not performed, when the width is 200 pixels or more, the number of samples is at least 100 pixels or more. 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 based on the following reason.
For example, assuming width >> height as in the bitmap image shown in FIG. 10A, if the sampling cycle ratio is determined by the longer width, as shown in FIG. In addition, it may happen that only two lines, the upper and lower lines, are extracted in the vertical direction. However, min (wi
(dth, height), if the sampling period ratio is determined based on the smaller one, it is possible to perform the thinning including the intermediate part even in the smaller vertical direction as shown in FIG. Become.

In this example, thinning is performed at an accurate sampling cycle for pixels in the vertical and horizontal directions. This is suitable when processing is performed while thinning out sequentially input pixels. However, when all pixels have been input, pixels may be selected by randomly designating coordinates in the vertical direction and the horizontal direction.
In this way, when the minimum required number of pixels, such as 10,000 pixels, is determined, the process of randomly extracting until 10,000 pixels is repeated is repeated.
It suffices to stop the extraction at the time of the pixel.

If the pixel data of the selected pixel has luminance as its component element, the distribution can be obtained using the luminance value. However, even in the case of image data whose luminance value is not a direct component value, the image data has a component value representing luminance indirectly.
Therefore, a luminance value can be obtained by converting from a color space in which the luminance value is not a direct component value to a color space in which the luminance value is a direct component value.

The color conversion between these different color spaces is not uniquely determined by the conversion formula, but a mutual correspondence is obtained for the color spaces having the respective component values as coordinates. It is necessary to sequentially perform conversion by referring to a color conversion table that stores the relationship. In relation to a table, the component values are represented as gradation values. In the case of 256 gradations having three-dimensional coordinate axes, about 16.7 million (2
It must have a color conversion table of 56 × 256 × 256) elements. As a result of considering efficient use of storage resources, instead of preparing correspondences for all coordinate values, usually prepare correspondences for appropriate discrete grid points and use interpolation together I am trying to do it. Since this interpolation calculation can be performed through several multiplications and additions, the amount of calculation processing becomes enormous.

That is, if a full-size color conversion table is used, the processing amount is reduced, but the table size becomes impractical. If the table size is set to a realistic size, the calculation processing amount is impractical. Often become.

In view of such a situation, the present embodiment employs the following conversion formula for obtaining luminance from the three primary colors of RGB as used in a television or the like. That is, the luminance yp at the point P is R
From the GB component values (Rp, Gp, Bp), yp = 0.30Rp + 0.59Gp + 0.11Bp (6) In this way, a luminance value can be obtained only by three multiplications and two additions.

In the present embodiment, such a conversion formula is adopted as a result of targeting the RGB color space. However, since each component value indicates the color brightness on the background, There is a property that when each component value is viewed independently, it linearly corresponds to luminance. Therefore, more simply, it is not impossible to simply simplify to yp = (Rp + Gp + Bp) / 3 (7) without considering each addition ratio, and furthermore, yp = Gp (7) ) ', It is also possible to use the green component value having the largest ratio in the equation (6) as the luminance value.

In the thinning-out process, R
The luminance is simultaneously obtained from the GB image data to obtain a distribution.
Eventually, the width of this distribution will be determined in step S116, but there are matters to be considered before that.

The first case is when the image is a binary image such as a black and white image. For a binary image including a black-and-white image, the concept of enhancing contrast is inappropriate. FIG.
As shown in FIG. 12, the luminance distribution for this image is concentrated at both ends in the reproducible range as shown in FIG. It also basically concentrates on the gradation “0” and the gradation “255”.

Therefore, when performing a black-and-white check in step S104, it can be determined whether or not the sum of the number of pixels of the gradation "0" and the gradation "255" matches the number of pixels selected by thinning. Then, in the case of a monochrome image, step S1 is executed to interrupt the processing without executing the following processing.
At 06, non-enlargement processing is executed. In the present embodiment, since the distribution extraction processing and the luminance conversion processing are largely divided, in the non-enlargement processing, a flag is set so that the subsequent luminance conversion processing is not executed, and the distribution extraction processing ends.

The binary data is not limited to black and white, but may be colored binary data. In such a case as well, it is not necessary to perform processing for enhancing the contrast. If the distribution state is examined and the distribution is concentrated only on two values (one is approximately “0”), the processing is interrupted as binary data. What should I do?

The second consideration is whether the image is like a business graph or a natural image such as a photograph. Although a process of enhancing contrast may be required in a natural image, it is often preferred not to enhance contrast in a business graph or a painting. Therefore, in step S108, it is checked whether the image is a natural image.

The number of colors in a natural image is extremely large, including shadows, but the number of colors is often limited in a business graph or a certain kind of painting. Therefore, if the number of colors is small, it can be determined that the image is not a natural image. To accurately determine the number of colors, it is necessary to determine how many of the 16.7 million colors are used as described above, but this is not practical. On the other hand, when the number of colors is extremely small, as in a business graph, the probability of different colors and 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 distribution of luminance is sparse, and in a business graph, it appears in a linear spectrum. For this reason, in step S108, the number of luminance values whose distribution number is not “0” among the 256 gradation luminances is counted. If it is less than or equal to about 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, whether or not the color is equal to or less than the “25” color that is the threshold can be changed as appropriate.

It is also possible to determine whether or not the distribution is in the form of a linear spectrum based on the adjacent ratio of luminance values whose distribution number is not "0". That is, it is determined whether or not the number of distributions is a luminance value other than “0” and the number of distributions is adjacent luminance values.
If at least one of the two adjacent luminance values is adjacent, nothing is performed, and if both are not adjacent, counting is performed, and as a result, the ratio between the number of non-zero luminance values and the count value Judge it. For example, if the number of luminance values other than “0” is “20” and the number of non-adjacent luminance values is “20”, it is understood that the luminance values are distributed in a linear spectrum.

Further, when the image processing program is executed via the operating system, it is possible to determine the extension by the extension of the image file. Of the bitmap files, especially for photographic images and the like, file compression is performed, and an implied 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 the printer driver etc. sends an inquiry to the operating system, the extension of the file will be answered. It may be determined that there is, and the contrast may be enhanced. Also, "X
If the extension is unique to the business graph such as “LS”, it can be determined that contrast enhancement is not performed.

The third consideration is whether there is a frame around the image as shown in FIG. If such a frame portion is white or black, the luminance distribution is as shown in FIG.
As shown in (1), the effect appears in a linear spectrum shape at both ends in a reproducible range, and also as a smooth luminance distribution inside other than both ends corresponding to the internal natural image.

Of course, since it is more appropriate not to take the frame 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 check of the frame in step S108. Then, 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 frame processing is performed in step S112. In this frame processing,
In order to ignore the frame part, the number of pixels of the gradation “0” and the gradation “255” in the luminance distribution is set to “0”. As a result, in the following processing, it can be handled in the same manner as the case without the frame portion.

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 line spectrum shape protruding from the original smooth curve drawn by the luminance distribution appears. Therefore,
A line spectrum having a large difference between adjacent luminance values may be considered as a frame part and not subjected to luminance distribution. In this case, since the color may be used in a portion other than the frame portion, the average of the brightness values on both sides may be assigned.

When the luminance distribution is expanded in consideration of the above considerations, both ends of the luminance distribution are obtained in step S116. As shown in FIG. 15, the luminance distribution in a natural image generally appears in a mountain shape. Of course, the position and shape are various. The width of the luminance distribution is determined depending on where the both ends are determined. However, the point where the number of distributions becomes “0” by extending the base cannot be set as the both ends. This is because the number of distributions may change around “0” in the tail part, and the number of distributions changes while approaching “0” without limit statistically.

For this reason, in the distribution range, the portions extending from the side with the highest luminance to the side with the lowest luminance by a certain distribution ratio are set as both ends of the distribution. In the present embodiment, as shown in FIG. 15, the distribution ratio is set to 0.5%.
Of course, this ratio can be changed as appropriate. In this manner, by cutting the upper and lower ends by a certain distribution ratio, white points and black points caused by noise or the like can be ignored. That is, if such a process is not performed, even if there is even one point, a white point or a black point will be at both ends of the luminance distribution. Therefore, in many cases, the lowermost end is gradation “0”, and the uppermost end is Gradation "2
55 ". However, such a problem is eliminated by setting the end portion to be inside by 0.5% of the number of pixels from the upper end portion.

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 deleted) is calculated, and a reproducible luminance distribution is calculated. , The respective distribution numbers are accumulated in order from the upper-end luminance value and the lower-end luminance value inward to obtain a luminance value having a value of 0.5%.
Hereinafter, this upper end is referred to as ymax, and the lower end is referred to as ymin.
Call.

In the present embodiment, the upper end and the lower end of the luminance distribution are obtained through such processing. However, it is also possible to obtain both ends under statistical processing. For example, it is also possible to adopt a method in which what percentage or less of the average luminance value is set as an end.

The above processing corresponds to the distribution detection processing. Next, the luminance conversion processing for converting the image data based on the luminance values ymax and ymin thus obtained will be described. When the non-enlargement process is performed in step S106 as described above, the process refers to a predetermined flag in step S202, detects the flag, and ends the image processing without performing the following process.

The basic conversion of luminance is as follows. When the range of reproducible luminance is "0" to "255", the luminance y before conversion and the maximum value ymax and minimum value ymin of the luminance distribution range are converted. The luminance Y is obtained based on the following equation.

Y = ay + b (2) where a = 255 / (ymax−ymin) (3) ′ b = −a · ymin or 255−a · ymax (4) ′ If <0, Y = 0, Y> 2
If 55, Y = 255. Here, “a” is a slope, and “b” is an offset. According to this conversion formula, as shown in FIG. 16, a luminance distribution having a certain narrow width can be expanded to a reproducible range. Basically, in expanding the distribution range of luminance, the number of pixels does not change, and the areas of the histograms match.

When the luminance distribution is expanded by making the most of the reproducible range as described above, a highlight portion may be lost in white and a high shadow portion may be lost in black. In order to prevent this, in the present embodiment, the reproducible range is limited.
That is, the luminance value “5” is left as a range that does not expand to the upper and lower ends of the reproducible range. As a result, the parameters of the conversion equation are as follows.

A = 245 / (ymax−ymin) (8) b = 5−a · ymin or 250−a · ymax (9) In this case, in the range of y <ymin and y> ymax Causes no conversion.

In this embodiment, in order to hold the highlight portion and the high shadow portion, the range of “5” is uniformly set as the luminance value from the end to the non-enlarged region. The range may be narrowed if the image output device is such that the high shadow portion can be relatively easily reproduced, or the range may be expanded if the reproducibility is weaker. Further, the enlargement ratio may not be uniformly enlarged, but may be gradually limited in the border area.

FIG. 17A shows a case where the luminance distribution of the image is narrow. However, if the enlargement ratio (corresponding to a) of the luminance distribution is applied as described above, the reproduction is not performed. In some cases, a very large magnification can be obtained according to the possible range. Then, in a twilight state such as in the evening, it is natural that the contrast range from the brightest part to the dark part is narrow. I could end up. Since such conversion is not desired, a limit is set for the enlargement ratio, and a is set to 1.5.
(〜2) Limit so as not to exceed the above. Thereby, twilight comes to be expressed as twilight.

FIG. 17 shows a case where no limit is imposed on the enlargement ratio.
(A) is indicated by a dashed line, and no extra portion remains within a reproducible range after the conversion. However, when the enlargement range is limited, as shown by the two-dot chain line in FIG. 3B, there is a degree of freedom in where the converted distribution is to be obtained, and in some cases, the entire distribution is bright. It can be too dark or too dark.

For this reason, in the present embodiment, the ratio (m1: m2) of the remaining area remaining at the upper end and the lower end within the range in which the luminance distribution before conversion can be reproduced.
Is converted so as to match the ratio (n1: n2) of the remaining area remaining on the upper end side and the lower end side after the conversion.
Hereinafter, a method of obtaining the parameter b in such a case will be described.

In the luminance distribution of the image before conversion, m1 = ymin m2 = 255-ymax where m1 + m2 = 255- (ymax-ymin) Therefore, if ydif = ymax-ymin, then m1 + m2 = 255-ydif In the luminance distribution of the image, n1 = Ymin n2 = 255−Ymax Similarly, n1 + n2 = 255− (Ymax−Ymin) = 255−a (ymax−ymin) = 255−a · dif.

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,
Therefore, b = Ymin−a · ymin = ymin ｛(255−a · dif) / (255−ydif) −a｝ (10) Further, when it is determined using Ymax, b = Ymax−a · ymax = 255− (255−ymax) (255−a · ydif) / (255−ydif) −a · ymax (11) The parameter b can be obtained, and step S204 ends.

By the way, it is irrational to execute the above conversion formula (Y = ay + b) every time luminance is converted.
That is, the possible range of the luminance y is “0” to “25”.
Since it can be only 5 ", the converted luminance Y can be obtained in advance for all possible values of the luminance y. Accordingly, this correspondence is obtained in step S206 and stored as a table as shown in FIG.

The image data can be changed when such a conversion table is formed. But,
It is extremely effective not only to enhance the contrast by expanding the range of the luminance but also to adjust the brightness accordingly. For example, when the peaks of the luminance distribution are shifted to the dark side as a whole as shown by a solid line in FIG. 19, it is better to move the peaks to the lighter side as a whole as shown by a chain line. In the case where the peaks of the luminance distribution are generally shifted toward the bright side as shown by the solid line in FIG. 20, it is preferable to move the peaks to the dark side as a whole as indicated by the chain line.

As a result of conducting various experiments, in this 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 corresponds to the following γ value. Brighten with gamma correction.

Γ = ymed / 85 (12) Alternatively, γ = (ymed / 85) ** (1/2) (13)

In this case, even if γ <0.7, γ =
0.7. Unless such a limit is set, a night image looks like daytime. If the image is made too bright, the image becomes whitish as a whole and the image tends to have a low contrast. Therefore, a process such as emphasizing with saturation is preferable.

On the other hand, when the median ymed is larger than “128”, the image is determined to be a bright image, and is darkened by γ correction corresponding to the following γ value.

Γ = ymed / 128 (14) Alternatively, γ = (ymed / 128) ** (1/2) (15) In this case, even if γ> 1.3, γ = 1.3.
A limit is set so as not to be too dark. It should be noted that if the color is too dark, the color will be too high and the image will be dark, and accordingly, a process such as weakening the saturation emphasis is preferable. However, such a darkening process may adversely affect a subject in a bright background. For example, in a landscape image in which the sky occupies half of the image or a commemorative photo on a sunny day, the face is often backlit and the face tends to be dark and crushed. In the case of these images, a dark portion and a bright portion are mixed, so that the standard deviation ystd of the luminance is often a relatively high value. Therefore, the standard deviation of the luminance ystd>
In the case of 70, the gamma correction for darkening is not performed.

Note that this γ correction may be performed on the luminance distribution before conversion or on the luminance distribution after conversion. FIG. 21 shows a correspondence relationship when γ correction is performed. If γ <1, the curve expands upward, and if γ> 1, the curve expands downward. Of course, the result of the γ correction may be reflected in the table shown in FIG. 18, and the correction is performed on the table data in step S208.

Lastly, the image data is converted in step S210. Up to this point, the correspondence relationship for converting the luminance has been obtained, and for example, the conversion relationship is not the component value (Rp, Gp, Bp) on the RGB coordinate axis. However, the conversion equation of equation (2) can also be applied in the correspondence relationship with the RGB component values (Rp, Gp, Bp). That is, the component values (r,
The component values (R, G, B) after the conversion for g, b) are obtained as follows: R = a · r + b (16) G = a · g + b (17) B = ab · b + b (18) You can also. This is apparent from the fact that both the equations (2) and (4) show a linear correspondence. Further, the luminances y and Y are from gradation “0” to gradation “25”.
5 ”, the RGB component values (r, g,
b) and (R, G, B) are also in the same range, and it can be said that the conversion table of the luminance y and Y described above may be used as it is.

Therefore, in step S210, the conversion table corresponding to the equations (16) to (18) is referred to for the image data (r, g, b) of all pixels, and the converted image data (R, G, B) is obtained. Is repeated.

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

Assuming that a photograph is taken by the scanner 11 or the like, image data representing the photograph in the form of RGB gradation data is taken into the computer 21, and the CPU operates as shown in FIGS.
Is executed to enhance the contrast of the image data.

First, in step S102, the image data is thinned out within a predetermined error range, and the luminance y of the selected pixel is obtained to obtain a distribution. The distribution cannot be used as it is. 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. Unless the image is a binary image or a non-natural image, step S1
In step 10, it is determined whether or not there is a frame in the image data.
The range of% is removed to obtain both ends ymax and ymin of the distribution.

When both ends ymax and ymin of the luminance distribution are obtained, Y = ay + b (2) a = 245 / (ymax-ymin) (8) For b, one of the following: b = 5-a · ymin Or 250−a · ymax (9) b = ymin ｛(255−a · dif) / (255−ydif) −a｝ (10) b = 255− (255−ymax) (255−a · ydif) / (255−ydif) −a · ymax (11) From the relational expression, the parameters a,
In step S206, the conversion relationship from the luminance y to the luminance Y is stored in a table. Step S
In step 208, gamma correction is performed as necessary, and in step S210, image data for all pixels is converted with reference to the completed conversion table.

Of course, when the image is not a binary image or a natural image as described above, such image processing is not performed.
When the image processing of the present invention is performed, a bright and dark image is obtained by correcting the brightness so as to be widened even though the contrast is very low in the state of the photograph. Will be able to

In the above-described embodiment, the limitation of the enlargement ratio and the like are fixed. However, the computer 21 may be configured so that the user can select it via a predetermined GUI. Further, it is also possible for the user to designate a part of the image data and execute the contrast enhancement processing only within the range.

As described above, after obtaining the luminance y for the pixels of the image data while thinning out in step S102, for example, the ends inside the upper end and the lower end by a predetermined distribution ratio are set to the ends of the luminance distribution. By taking this into account (step S116), it becomes possible to obtain a parameter a corresponding to the enlargement ratio and a parameter b corresponding to the offset amount within the reproducible range of the luminance. On the other hand, the conversion destination luminance Y can be automatically converted by using a relational expression such as Y = ay + b.

It should be noted that the calculation speed of the input image of the video camera 14 may not be enough. Therefore, in such a case, the luminance distribution is detected by detecting the luminance for each shooting scene, and a luminance expansion table is created by assuming that there is a similar tendency in the scene. The luminance distribution may be enlarged for each frame in accordance with the enlargement table. 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 part 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 cycle.

FIG. 9 is a diagram showing 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 showing 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 showing an image having 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 a luminance distribution and an edge obtained by the edge processing.

FIG. 16 is a diagram showing a range of luminance in which a luminance distribution can be expanded and reproduced.

FIG. 17 is a diagram illustrating a case in which the enlargement ratio of the luminance distribution is limited.

FIG. 18 is a diagram showing a conversion table for expanding a luminance distribution.

FIG. 19 is a diagram showing the concept of brightening by γ correction.

FIG. 20 is a diagram showing the 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 (21)

[Claims] An image processing apparatus for decomposing an image into pixels in a dot matrix form and performing predetermined image processing on image data representing information of each pixel, wherein the image processing apparatus obtains a luminance distribution in the image data and reproduces the luminance distribution. An image processing apparatus for converting image data so as to expand the distribution according to a possible range. 2. The image processing apparatus according to claim 1, wherein: a luminance distribution detecting means for detecting a luminance distribution of image data in a pixel unit in the predetermined image; and a reproducible image based on the detected luminance distribution. An image processing apparatus comprising: an image data conversion unit that determines an expandable degree of the same luminance distribution within a range and converts image data. 3. The image processing apparatus according to claim 2, wherein the image data converting means sets the luminance y before the conversion and the maximum value of the luminance distribution range when the reproducible luminance range is yrange. The luminance Y of the conversion destination from ymax and the minimum value ymin
Is calculated based on the following equation: Y = ay + b where a = yrange / (ymax−ymin) b = −a · ymin or yrange−a · ymax In the above conversion formula, if Y <0, then Y = 0 and Y> ra
If nge, Y = yrange. 4. The image processing apparatus according to claim 2, wherein the luminance distribution enlarging means obtains a maximum distribution luminance of the conversion source luminance y,
An image processing apparatus, wherein a luminance Y of a conversion destination is obtained by γ correction based on a belonging range of the maximum distribution luminance. 5. The image processing apparatus according to claim 2, wherein the conversion destination luminance y is calculated and stored within a range of the conversion source luminance y, and the conversion is performed. An image processing apparatus characterized by occasionally invoking and converting a correspondence. 6. The image processing apparatus according to claim 1, wherein when the image data is represented by a plurality of component values corresponding to the luminance, the calculation of the luminance is performed by using the same component value. An image processing device characterized by being obtained by linear addition. 7. The image processing apparatus according to claim 1, wherein a luminance distribution is obtained by performing thinning-out corresponding to a predetermined extraction rate on the image data. . 8. The image processing apparatus according to claim 7, wherein a predetermined number of extractions is secured on a shorter side in the vertical and horizontal directions. 9. The image processing apparatus according to any one of claims 1 to 8, wherein, when calculating a luminance distribution, an inner portion by a predetermined distribution ratio from an actual end is regarded as an end. Image processing device. 10. The image processing apparatus according to claim 1, wherein a range of the luminance distribution to be enlarged is set inside by a predetermined amount from an end of an actual reproducible range. An image processing apparatus characterized by the above-mentioned. 11. The image processing apparatus according to claim 1, wherein a limit is set for an enlarged range of the luminance distribution. 12. The image processing apparatus according to claim 11, wherein a corresponding position of a luminance distribution range before conversion and a corresponding position of a luminance distribution range after conversion within a reproducible range are held. Characteristic image processing device. 13. The image processing apparatus according to claim 12, wherein 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 even after the conversion. An image processing apparatus characterized by enlarging the image. 14. The image processing apparatus according to claim 1, wherein the binary image data is determined based on the luminance distribution, and if the binary image data is used, the luminance distribution is expanded. An image processing apparatus characterized in that the processing is not performed. 15. The image processing apparatus according to claim 14, wherein when the luminance distribution is concentrated at both ends within a reproducible range, the image is determined to be monochrome binary image data. Image processing device. 16. The image processing apparatus according to claim 1, wherein a frame portion of the image data is determined based on the projected luminance distribution, and if there is a frame portion, the data of the frame portion is determined. An image processing apparatus characterized in that the image processing apparatus is not used for expanding a luminance distribution. 17. The image processing apparatus according to claim 16, wherein a luminance distribution concentrated on an end portion within a reproducible range is determined to be a frame portion. 18. The image processing apparatus according to claim 1, wherein the luminance distribution is not expanded when the image data is not a natural image. 19. The image processing apparatus according to claim 18, further comprising a natural image determining unit that determines that the image data is not a natural image when the luminance distribution exists in a spectrum. apparatus. 20. An image processing method for performing predetermined image processing on image data representing information of each pixel by decomposing an image into pixels in a dot matrix form, and as a whole based on luminance at each pixel. Tabulated luminance distribution, and if the tabulated luminance distribution is not widely distributed over the effective luminance range that the image data can take, each of the image data is distributed so that the luminance distribution is widely distributed over the same luminance range. An image processing method characterized by converting information on luminance of a pixel. 21. A medium storing an image processing program for decomposing an image into dot matrix pixels, inputting image data representing information of each pixel by a computer, and performing predetermined image processing. Inputting the data and summing the overall brightness distribution based on the brightness at each pixel; and, if the summed brightness distribution is not widely distributed over the effective brightness range that the image data can take, Converting the luminance information of each pixel in the image data so that the luminance distribution is widely dispersed in the same luminance range.