JPH11146219A - Image processing device and method and medium recording image processing program - Google Patents

Abstract

PROBLEM TO BE SOLVED: To provide an image processor with which side effect image processing is prevented as much as possible when correcting image data. SOLUTION: A computer 21, as a central image processor designates an area to apply image processing through a step S100, and while discriminating whether an object-pixel belongs to the designated area or not through steps S110-S140 while moving it. When that pixel belongs to that area, the designated image processing is executed. Thus, any adverse effect is prevented from being exerted upon the image data in the other area by correcting image data in a certain area, and the image data can be easily made beautiful as a whole.

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 a method for inputting actual image data consisting of pixels in a dot matrix such as a digital photographic image. And an image processing apparatus for converting image data of each pixel in a predetermined correspondence relationship.

[0002]

2. Description of the Related Art Various types of image processing are performed on image data of actual photographs such as digital photographic images. For example, image processing such as increasing the contrast, correcting the chromaticity, or correcting the brightness. These image processes are performed by converting the image data of each pixel so as to have a predetermined correspondence. In the example of correcting chromaticity,
A color conversion table is prepared, and output data is generated with reference to the same color conversion table using the conversion source image data as input data. As a result, in the case of skin color correction, the skin color portion of the image becomes vivid.

[0003]

In the above-described conventional image processing apparatus, when a certain chromaticity is to be corrected, such correction is applied to the entire image, and a satisfactory result is not always obtained. There was a problem that it was not always possible. For example, when a person's face is bluish and the bloody color is bad, if the user tries to improve the bloody color, the entire screen becomes reddish. That is, although an original effect can be obtained by performing a certain image correction, an undesirable image correction is also performed as a side effect.

[0004] The present invention has been made in view of the above problems, and has as its object to provide an image processing apparatus capable of minimizing side-effect image processing when correcting image data. .

[0005]

According to a first aspect of the present invention, there is provided an image processing apparatus comprising: inputting real image data composed of pixels in a dot matrix, and converting the image data of each pixel in a predetermined correspondence relationship; An image processing apparatus for performing conversion, comprising: a correspondence holding unit having a plurality of correspondences for converting the image data together with information on an application target thereof; and information on an application target of each correspondence held by the correspondence holding unit. A correspondence determining means for determining the correspondence to convert the image data of each pixel on the basis of the image data of each pixel by referring to the correspondence determined by the correspondence determining means from the correspondence holding means And image data conversion means for converting the image data.

According to the first aspect of the present invention, when an image is represented as a dot matrix pixel and each pixel is represented by image data, the image data of each pixel is assigned to a predetermined correspondence. Convert by relationship.
At this time, the correspondence holding means includes a plurality of correspondences for converting the same image data together with information on an application target thereof,
Correspondence determining means determines the correspondence to convert the image data of each pixel based on the information on the application of each correspondence held in the correspondence holding means, and the image data conversion means determines the correspondence. The correspondence determined by the means is referred to from the correspondence holding means to convert the image data of each pixel.

That is, a plurality of correspondences for converting image data are provided, which correspondence is to be applied to each pixel, and an appropriate correspondence is applied to each pixel to convert the image data. For example, the correspondence that makes the green of the trees vivid is applied to the green pixels of the trees, and the correspondence that makes the skin look good is applied to the pixels of the skin color, and the correspondence is appropriately changed.

[0008] In view of performing image processing, target image data is particularly suitable for a real image. The real image here is not necessarily 100% real image, and may be semi-synthesized or a plurality of real images. It may be mixed.

[0009] The correspondence holding means has a plurality of correspondences together with information on the application target. The correspondence relationship is to perform predetermined conversion by inputting image data, and each correspondence relationship has information to be applied. As the information itself to be applied, various kinds of information can be adopted. As an example, the invention according to claim 2 is the image processing apparatus according to claim 1, wherein the correspondence holding unit includes:
The correspondence relation to be applied to each part of the image is held, and information on a part to which each correspondence relation is to be applied is included. The correspondence determination means detects a part in the image of each pixel and detects the detected part. The correspondence relation to be converted is determined by comparing the correspondence relation holding means with the information on the parts for each correspondence relation.

In the invention according to claim 2 configured as described above, the correspondence to be applied differs for each part of the image, and the correspondence holding means has information on the part to which each correspondence is to be applied. ing. For this reason, the correspondence determination unit detects a part in the image of each pixel, and compares the detected part with information on a part for each correspondence held by the correspondence holding unit. If there is a match, the correspondence is determined to be converted.

That is, a different correspondence is prepared for each part of the image and applied to the image data. For example, the upper part of the image may include the correspondence with the sky blue color, and the lower part may not be converted. The unit of the part of the image in this case is not particularly limited, and may be a rectangle, a circle, a free figure, or the like. Further, it is not always necessary to specify a part with a certain range, and a predetermined range may be designated centering on a certain part. In this case, the degree of conversion may be gradually reduced as the distance from the center increases. Of course, one correspondence may be applied to a plurality of parts.

As another example of specifying an application object, the invention according to claim 3 is an image processing apparatus according to any one of claims 1 and 2, wherein the correspondence holding means includes a plurality of correspondences. And the chromaticity information to which each correspondence relationship is to be applied. The correspondence relationship determination means detects the chromaticity of each pixel, and the detected chromaticity and each correspondence possessed by the correspondence relationship holding means. The configuration is such that the correspondence to be converted is determined by comparing the chromaticity information for each relationship.

In the invention according to claim 3 configured as described above, a plurality of correspondences are held, and the correspondence to be applied differs for each chromaticity. Therefore, the correspondence holding means has chromaticity information to be applied for each correspondence, and the correspondence determination means detects the chromaticity of each pixel. Then, the detected chromaticity is compared with the chromaticity information for each corresponding relationship held by the corresponding relationship holding unit, and the corresponding relationship to be converted is determined. in this case,
The chromaticity is used to determine the correspondence to be applied, and the chromaticity does not always need to be corrected.

In order to determine the chromaticity, the input image data may be used as it is, or it may be determined after changing the so-called color space into image data in which the chromaticity can be more easily determined. . Further, the chromaticity is not necessarily limited to the chromaticity in a narrow sense, and it is possible to determine the chromaticity in a broad sense even when a predetermined feature amount that can be determined from image data is used.

On the other hand, various specific methods for providing a correspondence for converting image data can be adopted.
As an example, the invention according to claim 4 is based on claims 1 to
4. The image processing apparatus according to claim 3, wherein the correspondence holding unit has a color conversion table that stores a correspondence between the original image data and the converted image data.

In the invention according to claim 4, the image data of the conversion source and the image data after the conversion are stored in the color conversion table of the correspondence holding means. Refers to the same color conversion table using the original image data to obtain the converted image data. That is, the correspondence is stored as a table.

Of course, besides this, it is an arithmetic expression,
Although it is possible to hold the correspondence in the form of calculation parameters, there is an advantage that when a table is used, it is troublesome to refer to the table.

The plurality of correspondences are not necessarily limited to the case where there are a plurality of individual correspondences, but may be ones which can substantially obtain a plurality of conversion results. As an example, the invention according to claim 5 is the image processing apparatus according to any one of claims 1 to 4, wherein the correspondence holding unit changes the degree of application of the correspondence by changing a plurality of degrees. This is a configuration for realizing the correspondence.

In the invention according to claim 5 configured as described above, the correspondence holding means realizes a plurality of correspondences by changing the degree of application of the correspondence. For example, it is possible to set a value according to the degree of adaptation between image data to which the correspondence is not applied and image data to which the correspondence is applied. In this case, a linear association or a non-linear association may be used.

Further, when using such a degree of adaptation, if only one correspondence is provided, a state where the correspondence is converted and a state where the conversion is not substantially obtained are obtained. Thus, it can be said that a plurality of correspondences are provided, and in this case, more correspondences can be realized by changing the adaptation degree.

The correspondence provided in the correspondence holding means may be predetermined or may be appropriately determined. As an example of the latter, the invention according to claim 6 is based on claims 1 to 6. The image processing apparatus according to claim 5, wherein the correspondence holding unit includes a correspondence designation unit that designates a correspondence to be applied to the image data.

In the invention according to claim 6 configured as described above, the correspondence to be applied to the image data is designated by the correspondence designation means of the correspondence holding means. The correspondence has two elements, that is, the conversion content and the application target, and the correspondence holding means may be any one. In this case, specify the part of each image,
It is also possible to select the contents of conversion at the designated part. Further, an item such as skin color correction or sky color correction may be selected as an image processing option.

As a specific example of such a correspondence, the invention according to claim 7 is an image processing apparatus according to any one of claims 1 to 6, wherein the correspondence holding means includes the image processing device. The configuration is such that the correspondence for changing the brightness based on the data is held.

In the invention according to claim 7 configured as described above, the correspondence holding means holds the correspondence for changing the brightness based on the image data, and the image data conversion means uses this correspondence. Is performed to change the brightness of a predetermined pixel. For example, a conversion is performed to brighten pixels in a certain part of the image.

According to another aspect of the present invention, in the image processing apparatus according to any one of the first to seventh aspects, the correspondence holding means stores the chromaticity based on the image data. It is configured to hold the correspondence to be changed.

In the invention according to claim 8 configured as described above, the correspondence holding means holds the correspondence for changing the chromaticity based on the image data, and the image data conversion means uses this correspondence. Is performed to change the chromaticity of a predetermined pixel. For example, if there is a flesh-colored pixel in the image, conversion is performed to enhance redness.

According to a ninth aspect of the present invention, in the image processing apparatus according to any one of the first to eighth aspects, the correspondence holding means is configured to perform color vividness based on image data. It is configured to hold the correspondence that changes the length.

In the ninth aspect of the present invention, the correspondence holding means holds the correspondence for changing the vividness of the color based on the image data, and the image data conversion means converts the correspondence. The conversion that changes the vividness of the image is performed using the conversion. For example, when it is desired to enhance a colorful subject in comparison with the surrounding background, conversion that makes the subject more vivid is performed.

By the way, if it is clearly decided whether or not to apply a predetermined correspondence, the boundary may be apparent to the eye. For this reason, 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 image data conversion means gradually changes the pixels in the transition area between the areas having different correspondences. The image data is converted so that the correspondence changes.

According to the tenth aspect of the present invention, a transition area is provided between areas having different correspondences, and image data conversion is performed so that the correspondence of pixels in the transition area changes gradually. Means converts the image data. This prevents a step from occurring.

The manner in which the correspondence gradually changes in the transition area can be appropriately selected, and may be a linear transition or a non-linear transition.

As described above, the method of providing a plurality of correspondences and changing the correspondences according to pixels does not need to be limited to a substantial device, and it is easily understood that the method also functions as the method. it can. For this reason, the invention according to claim 11 is an image processing method for inputting real image data composed of pixels in a dot matrix and converting the image data of each pixel in a predetermined correspondence relationship. A plurality of correspondences to be converted are provided together with the information of the application target, and a correspondence to convert the image data of each pixel is determined based on the information of the application target of each correspondence. It is configured to convert data.

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

The idea of the invention for performing image processing by the above-described method includes various aspects. That is, it can be appropriately changed, for example, by hardware or software. In the case of software for performing image processing as an embodiment of the idea of the present invention, the software naturally exists on a software recording medium on which such software is recorded, and it must be said that the software is used. As an example, the invention according to claim 12 is a computer-readable storage medium storing an image processing program for inputting real image data composed of dot matrix pixels and converting the image data of each pixel in a predetermined correspondence relationship. A plurality of correspondences for converting the image data are provided together with the information of the application target, and the correspondence to convert the image data of each pixel is determined based on the information of the application target of each correspondence. The configuration is such that the image data of each pixel is converted according to the corresponding relationship.

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 invention, and it is necessary to store a part on a software recording medium. May be in a form that is read as appropriate in accordance with.

[0037]

As described above, according to the present invention, since the correspondence for converting image data can be changed for each pixel, desired image processing can be realized without adversely affecting other parts. It is possible to provide an image processing apparatus capable of performing the above.

Further, according to the second aspect of the present invention, since the correspondence is changed depending on the part of the image, image processing in which the location is designated can be performed, and the operation and the like become easy.

Further, according to the invention of claim 3,
Since the target can be selected based on the chromaticity, it can be realized relatively easily even when pixels to be subjected to image processing exist in a plurality of portions.

Further, according to the invention according to claim 4,
Since the correspondence is stored as a color conversion table, image conversion is relatively easy.

Further, according to the invention of claim 5,
By changing the adaptation degree, one correspondence can be used as a plurality.

Further, according to the invention of claim 6,
Since the correspondence to be applied can be specified, the degree of freedom is improved.

Furthermore, according to the invention of claim 7,
The brightness can be changed depending on the correspondence to be applied.

Further, according to the invention of claim 8,
The chromaticity can be changed depending on the correspondence to be applied.

Further, according to the ninth aspect of the present invention,
The vividness can be changed depending on the correspondence to be applied.

Further, according to the tenth aspect, even when different correspondences are applied in one image, it is possible to prevent a step from occurring at a boundary portion.

Further, according to the eleventh aspect of the present invention, it is possible to provide an image processing method capable of obtaining the same effect, and according to the twelfth aspect of the present invention, to provide a medium on which an image processing program is recorded. can do.

[0048]

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 using an image processing apparatus according to an embodiment of the present invention, and FIG. 2 is a schematic block diagram showing an example of a specific hardware configuration.

In FIG. 1, an image input device 10 outputs real image data representing a photograph or the like as pixels in a dot matrix to an image processing device 20, and outputs the image data to the image processing device 20.
0 designates an image processing application target and contents, and then executes image processing for the target pixel. Image processing device 2
Numeral 0 outputs image-processed image data to the image output device 30, and the image output device outputs the image-processed image by dot matrix pixels.

A specific example of the image input device 10 corresponds to the scanner 11, the digital still camera 12 or the video camera 14 shown in FIG. 2, and a specific example of the image processing device 20 includes a computer 21, a hard disk 22, a keyboard 23 and a mouse 27. A computer system including a CD-ROM drive 24, a floppy disk drive 25, and a modem 26 corresponds to the computer system. A specific example of the image output device 30 corresponds to a printer 31, a display 32, and the like. In the case of the present embodiment, image processing is performed in a predetermined correspondence while designating a target pixel in order to correct a defect in an image, so that real image data such as a photograph is preferable as the image data. The modem 26 is connected to a public communication line, is connected to an external network via the public communication line, and can download and install software and data.

In this embodiment, the image input device 10
The scanner 11 and the digital still camera 12 output RGB (green, blue, red) gradation data as image data, and the printer 3 as the image output device 30.
1 requires CMY (cyan, magenta, yellow) or CMYK binary data obtained by adding black to the input as gradation data, and the display 32 has RGB data.
Is required as an input. On the other hand, an operating system 21a is running in the computer 21, and a printer driver 21b and a display driver 21c corresponding to the printer 31 and the display 32 are provided.
Is incorporated.

The image processing application 21d is controlled by the operating system 21a to execute processing, and executes predetermined image processing in cooperation with the printer driver 21b and the display driver 21c as necessary. Therefore, the specific role of the computer 21 as the image processing device 20 is to input RGB gradation data, create RGB gradation data subjected to optimal image processing while evaluating an image, and generate a display driver 21c. Is displayed on the display 32 via the printer driver 21b, and is converted to CMY (or CMYK) binary data via the printer driver 21b and printed by the printer 31.

As described above, in this embodiment, a computer system is incorporated between image input / output devices to perform image evaluation and image processing. However, such a computer system is not always required. The present invention is applicable to a system that performs various types of image processing on image data. For example, as shown in FIG. 3, the digital still camera 12a incorporates an image processing device that executes image processing corresponding to each predetermined application, and displays the converted image data on the display 32a or prints the image on the printer 31a. It may be a system that causes it. Further, as shown in FIG. 4, in a printer 31b that inputs and prints image data without passing through a computer system, each pixel is converted from image data input through a scanner 11b, a digital still camera 12b, a modem 26b, or the like. It is also possible to determine whether or not the image data belongs to the application target and execute the corresponding image processing.

The above-described image evaluation and the accompanying image processing are performed in the computer 21 by an image processing program corresponding to the flowchart shown in FIG. The processing content is roughly described as follows. In the first step S100, processing for designating a target to be applied for each image processing is performed,
In steps 110 to S140, predetermined image processing is executed while moving the target pixel for each pixel in the dot matrix as shown in FIG. At this time, in step S110, it is determined whether or not the target pixel is the target of the image processing specified in step S100. In step S120, the image data is converted according to the determination result. Obviously, the image processing is substantially performed by this conversion. Steps S130 and S140 correspond to the process of moving the target pixel. Hereinafter, a detailed description will be given along this large flow.

Which pixel should be subjected to the image processing depends on the contents of the image processing. In this embodiment, as shown in FIGS. 7 and 8, a portion of the image to be processed is designated. I do. As shown in FIG.
An operation area 41 for operating the window frame is provided along the upper side, an image display area 42 is provided in the center, and a processing menu area 43 for specifying image processing is provided along the lower side. It is.

While the image to be processed is displayed in the display area 42, a rectangular area is designated by the mouse 27, and image processing in the processing menu area 43 is selected by the mouse 27. In the processing menu area 43, as image processing that can be executed, “contrast” correction, “brightness” correction, “sharpness” correction, and “saturation” correction are provided, and arrows indicating the degree thereof are provided. Clicking the up arrow of the contrast with the mouse 27 specifies image processing for enhancing the contrast, and clicking the down arrow specifies image processing for reducing the contrast.

A plurality of image processings may be selected for each area, and the selected image processings can be selected by clicking “list display / edit” in the processing menu area 43 as shown in FIG.
The upper left coordinates and lower right coordinates of the area as shown in FIG. The level indicates the degree of image processing, and is data for increasing or decreasing the degree of processing according to the number of clicks of the up arrow and the down arrow. The processing type indicates the type of image processing as described later. In this example, the “contrast” correction is 1
The "brightness" correction is 2 and the "sharpness" correction is 3
In this case, the “saturation” correction is set to 4, meaning each target. The processing type and level will be described later together with the description of the image processing. When the selected image processing is to be deleted, the user clicks each specification to highlight it and presses the delete key of the keyboard 23.
In addition, editing may be performed in accordance with the operation of a normal application.

In this embodiment, since the image processing application 21d is executed at the application level, it is possible to display an image to be processed in the window 40 and specify an area. However, similar image processing may be realized at the driver level such as the printer driver 21b, and in this case, the image may not always be displayed on the window.

FIG. 10 shows an example of specifying an image processing object regardless of the window display, and shows a window 50 that is selected as an option when the printer driver 21b is started. While FIG. 7 to FIG. 9 specify a part of the image, in this example, the chromaticity of the image is specified and a target pixel is selected. Here, the chromaticity will be described.

As a specific example of chromaticity, xy chromaticity is calculated. Now, assuming that the RGB gradation data of the target pixel in the RGB color system is (R, G, B), r = R / (R + G + B) (1) g = G / (R + G + B) (2), chromaticity coordinates x, y in the XYZ color system
X = (1.1302 + 1.6387r + 0.6215g) / (6.7846-3.0157r-0.3857g)… (3) y = (0.0601 + 0.9399r + 4.5306g) / (6.7846-3.0157r-0.3857) g)... (4) Here, since the chromaticity represents the absolute ratio of the color stimulus value without being influenced by the brightness, it can be said that it is possible to determine what kind of object the pixel is based on the chromaticity. .

For example, assuming that a human image is an object of an image, it can be said that it is sufficient to extract pixels of a flesh-colored portion.
The chromaticity of the skin color is included in the range of 0.35 <x <0.40 (5) 0.33 <y <0.36 (6). Then, it can be said that the pixel is not mistaken even if it is considered as a pixel indicating human skin. Similarly, if the chromaticity of the blue sky and the chromaticity of the green of the trees are also determined as the chromaticity, the sky pixels and the green pixels of the trees can be generally specified regardless of the brightness. FIG. 11 shows an xy chromaticity distribution relationship, in which a skin color area, a blue sky color area, and a green color area of trees can be grasped as such a rectangular area. Is shown. As shown in FIG. 10, the distribution range of the xy chromaticity may be directly designated and individually designated.

On the other hand, FIG. 12 shows a method of designating a center point in deciding an area in the same manner. In the case of flesh color, x = 0.375 (7) y = 0.345 (8) is obtained as the center point from equations (5) and (6). Therefore, if the center point is within a circle having a constant radius, it may be determined that the object belongs to the above-mentioned area.

In the window 50 displayed as an option of the printer driver 21b, a specific example for a predetermined chromaticity is displayed in order to specify an object by the chromaticity, and the operator wants to intentionally clean the blue sky or change the green of the trees. It is designed to reflect requests for vividness. In this case, an arrow button for instructing whether to emphasize or weaken the pixel of each chromaticity is also provided.

In this example, the range of pixels is designated by chromaticity and the chromaticity is used for image processing such as whether or not to emphasize the chromaticity. No need to match. For example, image processing such as brightening flesh color pixels or enhancing contrast of green pixels may be used. That is, the chromaticity can be used as an index for determining whether or not a pixel is a processing target. In this example, the chromaticity is only used for selecting image processing.

As described above, the processing of step S100 for specifying the target to be applied for each image processing constitutes the correspondence specifying means. However, it is needless to say that the present invention is not limited to these.
The method for designating the part and the chromaticity can be changed as appropriate, and it is also possible to designate with other elements such as brightness or sharpness. As shown in FIG. 9, a correspondence table for designating a predetermined area and associating the image processing with each other is stored in a storage area in the computer system. Needless to say, it also constitutes the correspondence holding means.

In the next step S110 and subsequent steps, while moving the target pixel, it is determined whether or not the pixel is the target pixel of the image processing designated as described above. Whether or not the target pixel is the target of the image processing may be compared with the coordinates of the target pixel by referring to the upper left coordinates and lower right coordinates of each column in the area / table correspondence table shown in FIG. If there is a corresponding column, it is determined that the pixel is a target pixel to which the image processing is applied. When the determination is made based on the chromaticity as shown in FIG. 10, the chromaticity of the target pixel is calculated according to the equations (1) to (4), and whether the pixel belongs to the chromaticity range selected by the option of the printer driver 21b. Determine whether or not.

Whether or not the image processing is to be applied is determined based on the region or chromaticity as described above. However, if an alternative determination such as belonging to or not belonging to these ranges is performed, the adjacent area is determined. Is likely to cause some visual step. For example, if an attempt is made to brighten a certain rectangular area, if the area is not brightened immediately outside the rectangular area, a brightness step occurs at the boundary, and the rectangular area is clearly recognized.

For this reason, in this embodiment, a parameter of the degree of applicability k of the image processing is used. As shown in FIG. 13, if it is determined in step S111 that it belongs to the predetermined area, the applicability k is set to “1” in step S112, but the applicability k is set to the transition area around the area. It is changed in the range of “0” to “1”. In other words, if it is determined in step S113 that the area is within the transition area, in step S114 the applicability k is made closer to "1" if the area is closer to the area, and closer to "0" if it is farther. If it does not belong to the predetermined area or the transition area, the applicability k is set to “0” in step S115. On the other hand, in the example shown in FIG. 12, the center point of the chromaticity is specified. However, as shown in FIG. May be set in the range of the radius r0 to the radius r1 so that the applicability k gradually approaches “0” in the peripheral portion.

In step S110, the applicability is determined, and in steps S111 to S115, the applicability k is determined. Steps S110 to S as described above
The software processing of 115 and the hardware for realizing the same constitute a correspondence determination unit. In the present embodiment, the determination result is obtained as the applicability k of each image processing. However, it is needless to say that the alternative determination of whether to apply the image processing can be realized.

In the next step 120, image processing is executed on the target pixel, paying attention to the applicability k. Here, specific contents of the image processing prepared in the present embodiment will be described.

The contrast indicates the width of the luminance of the entire image. When it is desired to correct the contrast, there is a main demand to increase the width of the contrast. The sum of the distribution of luminance at each pixel of a certain image as a histogram is shown by a solid line in FIG. When taking the distribution shown by the solid line, the difference between the brightness of the bright pixel and the brightness of the dark pixel is small, but if the brightness distribution spreads as shown by the dashed line, the difference between the brightness of the bright pixel and the brightness of the dark pixel becomes large. That is, the range of contrast is widened. Here, FIG. 17 shows luminance conversion for enlarging the contrast. Assuming that the conversion is performed in a relationship of Y = ay + b (9) between the luminance y of the conversion source and the luminance Y after the conversion, when a> 1, the difference between the pixel of the maximum luminance ymax and the minimum luminance ymin of the conversion source is It becomes larger after the conversion, and the distribution of the luminance is expanded as shown in FIG. In this case, it is more preferable to determine the slope a and the offset b according to the luminance distribution. For example, if a = 255 / (ymax−ymin) (10) b = −a · ymin or 255−a · ymax (11), the brightness distribution having a certain narrow width can be reproduced. Can be spread. However, when the luminance distribution is expanded by making the most of the reproducible range, a highlight portion may be lost in white, or a high shadow portion may be lost in black. To prevent this, a luminance value of about "5" may be 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) (12) b = 5-a · ymin or 250−a · ymax (13) In this case, in the range of y <ymin and y> ymax Causes no conversion.

In converting image data, it is not necessary to calculate each time. Assuming that the range of the luminance takes a value of “0” to “255”, a conversion result is prepared in advance for each luminance value, and a conversion table is formed as shown in FIG. However, in this case, the conversion of the luminance is used only.If the image data has the luminance as an element, the conversion table can be used. However, if the image data has only the indirect element, the conversion is performed. Cannot use the table. In computer systems, image data is often gradation data (R, G, B) representing the brightness of each of the red, green, and blue elements in gradation. Such gradation data (R, G, B) does not have a luminance value directly, but needs L
Although it is necessary to perform color conversion to the uv color space, this is not a good idea due to problems such as the amount of calculation. For this reason, the following conversion formula for directly obtaining luminance from RGB used in the case of a television or the like is used.

Y = 0.30R + 0.59G + 0.11B (14) Assuming that linear conversion between gradation data and luminance y is possible, gradation data before conversion ( R
0, G0, B0) and the converted gradation data (R1, G1, B1), the equation (9) can be applied. R1 = aR0 + b (15) G1 = aG0 + b (16) B1 = AB0 + b (17) As a result, FIG.
It can be understood that gradation data should be converted using the conversion table shown in FIG.

Next, an image processing method for correcting the brightness will be described. Assuming a histogram of the luminance in the same manner as above, if the peak of the luminance distribution is entirely shifted to the dark side as shown by the solid line in FIG. It is preferable to move the peak. Conversely, when the peak of the luminance distribution is entirely shifted toward the bright side as shown by the solid line in FIG. 19, the peak is moved to the entire dark side as indicated by the broken line. And good. In such a case, FIG.
Instead of performing a linear luminance conversion as shown in Fig. 7,
What is necessary is just to perform luminance conversion using what is called a gamma curve as shown in FIG.

In the correction using the γ curve, the overall brightness becomes higher when γ <1, and the entire brightness becomes lower when γ> 1.
This degree may be gradually changed in step S100 by the number of clicks on the up arrow and the down arrow of the brightness correction column in the processing menu area 43 shown in FIG.

It is also possible to automatically set the value of γ as in the case of correcting the contrast. As a result of various experiments, 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 brightened by γ correction corresponding to the following γ value.

Γ = ymed / 85 (18) or γ = (ymed / 85) ** (1/2) (19) However, 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 darkened by γ correction corresponding to the following γ value.

Γ = ymed / 128 (20) or γ = (ymed / 128) ** (1/2) (21) In this case, even if γ> 1.3, γ = 1.3.
A limit is set so as not to be too dark.

Note that a conversion table as shown in FIG. 16 may be formed for the γ correction.

In the edge enhancement processing for correcting the sharpness of the image, the luminance Y ′ after enhancement is calculated as follows: Y ′ = Y + Enhance · (Y−Yunsharp) (22) Is done. Here, Eenhance is the degree of edge enhancement, and Yunsharp is the result of performing unsharp mask processing on the image data of each pixel. Here, the unsharp mask processing will be described. FIG.
Shows an unsharp mask 60 of 5 × 5 pixels as an example. The unsharp mask 60 is positioned at the center “1”.
The value “00” is used as the weight of the pixel Y (x, y) to be processed in the matrix image data, and the peripheral pixels are used for weighting corresponding to the numerical values in the cells of the same mask and multiplying. . When using this unsharp mask 60,

[0084]

(Equation 1)

The multiplication is performed based on the following arithmetic expression. (23)
In the formula, “396” is the total value of the weighting coefficients, and in the case of unsharp masks of different sizes, it is the total value of each cell. Mij is a weight coefficient described in the unsharp mask cell, and Y
(X, y) is image data of each pixel. Note that ij is indicated by horizontal and vertical coordinate values with respect to the unsharp mask 41.

The meaning of the edge emphasis operation calculated based on the equation (22) is as follows. Yunsharp
Since (x, y) is obtained by adding the peripheral pixel to the target pixel with a lower weight, so-called "unsharp" image data is obtained.
What has been blunted in this way has the same meaning as that obtained by applying a so-called low-pass filter. Therefore,
“Y (x, y) −Yunsharp (x, y)” has the same meaning as a high-pass filter obtained by subtracting a low frequency component from all original components. Then, by multiplying the high-frequency component passed through the high-pass filter by the edge enhancement Eenhance and adding it to “Y (x, y)”, the high-frequency component is increased in proportion to the edge enhancement Eenhance. The result is that the edges are enhanced. Considering a situation where edge enhancement is required, since the image is a so-called edge portion of an image, the amount of processing can be drastically reduced by calculating only when the difference between image data between adjacent pixels is large. .

Also in this case, the degree of edge enhancement Eenhanc
e may be changed by the number of times the user clicks the up arrow or the down arrow of the sharpness correction column in the processing menu area 43 shown in FIG. 7 in step S100. It is also possible to automatically set the edge enhancement degree Eenhance.

At the edge portion of the image, the difference of the gradation data between adjacent pixels becomes large. This difference is a luminance gradient, which will be referred to as an edge degree. The degree of change in the luminance of the image can be calculated if the vector is obtained by dividing the vector into a horizontal component and a vertical component. In an image consisting of pixels in a dot matrix, eight pixels are adjacent when the target pixel is centered, but only between pixels that are adjacent in the horizontal and vertical directions to simplify the calculation. The degree of change is obtained, the length of the vector is integrated as the edge degree g of the target pixel, and the average value is calculated by dividing the integrated edge degree by the number of pixels. That is, assuming that the number of pixels is E (I) pix, the degree of sharpness SL of the object image is as follows.

[0089]

(Equation 2)

The calculation can be performed as follows. In this case, an image having a lower SL value has a lower degree of sharpness (blurry in appearance), and an image having a higher SL value has a higher degree of sharpness (clearer in appearance).

On the other hand, since the sharpness of the image is intuitive, the sharpness SL is obtained in the same manner for the image data of the optimal sharpness obtained experimentally, and the value is used as the ideal sharpness SLopt. At the same time, the degree of edge enhancement Eenhance is obtained as follows: Enhance = ks · (SLopt−SL) ** (1/2) (25) Here, the coefficient ks changes based on the size of the image. When the image data is composed of height dots and width dots in the vertical and horizontal directions, ks = min (height, width) / A (26) What is necessary is just to ask. Here, min (h
height, width) is the height dot and wi
The smaller one of the dth dots is indicated, and A is a constant “768”. Of course, these are obtained from the experimental results, and it goes without saying that they can be appropriately changed. However, good results are basically obtained by increasing the degree of emphasis for a larger image.

In this way, the edge emphasizing process can be executed manually or automatically.

The saturation image processing is performed as follows. Assuming that the saturation is enhanced by adopting a saturation enhancement parameter Sratio, if the image data has the saturation parameter as described above, the parameter may be converted, but as described above. Since only RGB component values are provided as grayscale data, saturation values cannot be obtained unless conversion to a color space in which saturation values are directly component values is performed. However, it is necessary to temporarily convert the RGB image data into image data in the Luv space, return the image data to RGB again after saturation enhancement, and the amount of calculation must be increased.
Therefore, the saturation is enhanced using the RGB gradation data as it is.

When each component is a component value of a hue component having a substantially equal relationship as in the RGB color space, R = G
If = B, it is gray and achromatic. Therefore, R
Assuming that the minimum component of each component of GB simply reduces the saturation without affecting the hue of each pixel, the minimum value of each component is subtracted from all component values. It can be said that the saturation can be enhanced by enlarging the difference value.

Each component (R, G, B) of RGB gradation data
If the component value of blue (B) at is the minimum value,
Using the saturation enhancement parameter Sratio, conversion is performed as follows.

R ′ = B + (RB) × Sratio (27) G ′ = B + (GB) × Sratio (28) B ′ = B (29) As a result, the RGB color space and Luv Since it is not necessary to perform two rounds of color conversion to make one round trip to and from the space, the calculation time can be reduced. In this embodiment, a method of simply subtracting the minimum value component from the other component values for the achromatic component is employed, but another conversion formula is employed for subtracting the achromatic component. It does not matter. However, in the case where only the minimum value is subtracted as in the equations (27) to (29), there is an effect that the amount of calculation is simplified because no multiplication / division is involved.

Even when the formulas (27) to (29) are adopted, good conversion is possible, but in this case, if the saturation is enhanced, the luminance tends to be improved and the whole image tends to be brighter. Therefore, the conversion is performed on the difference value obtained by subtracting the luminance equivalent value from each component value.

Assuming that the saturation enhancement is as follows: R ′ = R + △ R (30) G ′ = G + △ G (31) B ′ = B + △ B (32) , △ B are obtained as in the following equation based on the difference value from the luminance. That is, ΔR = (R−Y) × Sratio (33) ΔG = (G−Y) × Sratio (34) ΔB = (B−Y) × Sratio (35) '= R + (R−Y) × Sratio (36) G ′ = G + (G−Y) × Sratio (37) B ′ = B + (B−Y) × Sratio (38) The preservation of the brightness is apparent from the following equation.

Y ′ = Y + △ Y (39) ΔY = 0.30 △ R + 0.59 △ G + 0.11 △ B = Sratio ｛(0.30R + 0.59G + 0.11B) −Y｝ = 0 (40) When the input is gray (R = G = B), the luminance Y
= R = G = B, the adjustment value △ R = △ G = △ B = 0
And there is no achromatic color. Equation (36)
By using the equation (38), the luminance is preserved, and even if the saturation is emphasized, the whole is not brightened.

Of course, the saturation enhancement index Sratio in this case
May be changed according to the number of times the user clicks the up arrow or the down arrow of the saturation correction column in the processing menu area 43 shown in FIG. 7 in step S100. However, the saturation emphasis index Sratio can also be automatically set.

First, the saturation of a pixel is determined in a simplified manner. For this, the following calculation is performed as the saturation alternative value X.

X = | G + B−2 × R | (41) Originally, the saturation is “0” when R = G = B,
The maximum value is obtained at the time of mixing at a predetermined ratio of a single color of RGB or any two colors. Although it is possible to directly represent the saturation from this property directly, it is possible to obtain the maximum value of the saturation by a simple equation (41) if the color is a single color of red and yellow which is a mixed color of green and blue. It is "0" when the components are uniform. In addition, green and blue single colors also reach about half of the maximum value. Of course, X '= | R + B−2 × G | (42) X ′ = | G + R−2 × B | (43)

If the distribution of the histogram for the alternative value X of the saturation is obtained, the saturation is distributed in the range from the minimum value “0” to the maximum value “511”. Distribution. Next, a saturation index for this image is determined based on the aggregated saturation distribution. From the saturation distribution, a range occupied by the higher-order “16%” is obtained, and the lowest saturation “A” within this range represents the saturation of this image. If A <92, S = −A × ( 10/92) +50 (44) If 92 ≦ A <184 S = −A × (10/46) +60 (45) If 184 ≦ A <230 S = −A × (10/23) +100 (46) If 230 ≦ A, the saturation emphasis index S is determined as S = 0 (47). FIG. 23 shows the relationship between the saturation “A” and the saturation enhancement index S. As shown in the drawing, the saturation index S is large when the saturation “A” is small and is small when the saturation “A” is large in the range of the maximum value “50” to the minimum value “0”. It will change gradually. From the saturation index S, the saturation enhancement index S
Conversion to ratio may be obtained as Sratio = (S + 100) / 100 (48). In this case, when the saturation enhancement index S = 0, the saturation enhancement parameter Sratio = 1 and the saturation enhancement is not performed.

Finally, a method of designating a pixel range by chromaticity and enhancing the chromaticity as shown in FIG. 10 will be described. Basically, when enhancing the color tone, the luminance of the pixel is reduced. Adopt a method of strengthening. Therefore, a tone curve of γ correction as shown in FIG. 20 is used. Of course, the value of γ may be changed according to the degree of emphasis. In the case of automatic setting, the average value (Rs.ideal, Gs.idea, blue sky, green of trees, and skin color for an image close to the ideal value)
l, Bs.ideal) and its average (Rs.idea)
l, Gs.ideal, Bs.ideal) and the average value (Rs.ave, Gs.ave, Bs.ave) based on the aggregation result of the image are determined, and the difference is corrected by the correction amounts ΔR, ΔG, ΔB. And it is sufficient.

The image processing method prepared in the present embodiment has been described above.
In step S111 to step S115, the applicability k is determined in steps S111 to S115.
In step S120, the image data is converted based on the determination result.

As described above, in the processing menu area 43 shown in FIG. 7, the type of image processing and the emphasis level are selected. Therefore, in step S100, a conversion table corresponding to each emphasis level is created on the premise of the above-described image processing, and stored in a predetermined storage area in the computer system. Then, in step S120, the conversion is performed as follows with reference to the conversion table.

Each component of the RGB gradation data before conversion (Rpr
e, Gpre, Bpre) and each component (Rpo) of the converted RGB tone data with reference to a predetermined conversion table.
st, Gpost, Bpost) and the final image data is (Rfinl, Gfinl, Bfinl): Rfinl = kRpost + (1-k) Rpre (49) Gfinl = kGpost + (1- k) · Gpre (50) Bfinl = k · Bpost + (1−k) · Bpre (51) This means that the applicability k is “0”
This means that the image processing gradually has a weight in the transition region that changes from “1” to “1”, and the step does not occur.

Of course, the conversion tables for all the image processing to which the target pixel is applicable are sequentially performed according to the equations (49) to (49).
Equation (51) is applied, and if the applicability k is “0”, the image data conversion need not be performed. Also, Equation (49)
Although equation (51) is an operation for each of the RGB components, the target component may be a part. Further, when the applicability k is not used, each component (Rpost, Gpost, Bpost) of the RGB gradation data obtained by simply changing only the type of the conversion table may be used as it is.

In the above description, the target to be applied is specified for each image processing. That is, when viewed from the whole, only a specific area is subjected to image processing. However, it is naturally possible to perform another image processing on a specific area while performing a certain image processing on the whole.

Here, each component (Rtotal, Gtotal, Btotal) is obtained as a conversion result of image processing for the entire image, and each component (Rpart, Gpart, Bpart) is obtained as a conversion result of image processing for a specific area. If the final image data (Rfinl, Gfinl,
Bfinl) uses the same degree of applicability k ′, and Rfinl = k ′ · Rpart + (1−k ′) · Rtotal (52) Gfinl = k ′ · Gpart + (1−k ′) · Gtotal (53) Bfinl = k ′ · Bpart + (1−k ′) · Btotal (54)

For example, FIG. 24 schematically shows a selection mode in a situation where it is desired to brighten a backlighting person while emphasizing overall color vividness in the photograph of FIG. That is, the conversion result (Rtotal, Gtotal, Bto
tal) and the conversion result (Rpart, Gpart, Bpart) for the hatched area in the lower right for the human image are superimposed using the applicability k ′. in this case,
If the degree of applicability k 'in the area specified for the human image is 0.5, and the applicability k' is changed in the range of 0 <k '<0.5 in the transition area around the figure, Good. On the other hand, the applicability k ′ in the region designated for the human image is set to be a maximum of 1.0, and the applicability k ′ is 0 <k ′ <1. If the change is made in the range of 0, it is possible to prevent the conversion of the whole image from being applied in the area specified for the human image. Of course, the results of many more image processings can be applied in the same manner.

Thereafter, the target pixel is moved in step S130, and it is determined in step S140 whether or not the processing has been completed for all the target pixels. If the processing has been completed, the image processing ends.

Although the method of automatically setting the degree of emphasis in each image processing has been described, it is necessary to uniformly sample image data before moving the target pixel as described above. Aggregation is performed, the degree of emphasis is automatically set based on the aggregation result, and a conversion table may be created.

When an option based on chromaticity is selected by the printer driver 21b, the emphasis level can be set by such automatic setting, so that operability can be improved.

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

It is assumed that a photographic image is read by the scanner 11 and printed by the printer 31. Then, first,
Operating system 21a on computer 21
Is running, the image processing application 21
d is started, and the scanner 11 starts reading a photograph. When the read image data is captured by the image processing application 21d via the operating system 21a, the image processing application 21d executes image processing based on the flowchart shown in FIG.

First, in step S100, the read photographic image is displayed in the display area 42 of the window 40 as shown in FIG. In this state, the operator designates the sky blue portion as a rectangular region with the mouse 27 as shown in FIG. 8 and clicks the up arrow several times in the processing menu area 43 so as to emphasize the saturation. In addition, a rectangular area is selected so that the center person image is included, and the up arrow is clicked several times in the processing menu area 43 so as to emphasize the brightness. That is, the image processing for enhancing the saturation by designating the sky area of the background and the image processing for increasing the brightness by designating the area of the person are designated.

When the designation is completed by closing the window, a conversion table is created based on the designation. That is, referring to the area table correspondence table shown in FIG. 9, the type of image processing is determined with reference to the type of processing, and the degree of emphasis is determined based on the level, and a conversion table is created. Here, a conversion table for saturation enhancement processing and a conversion table for increasing brightness are created.

Thereafter, the target pixel is set to the initial position, and it is determined in step S110 whether or not the coordinates of the target pixel are included in each area of the area table correspondence table shown in FIG. Of course, in this case, the transition area is also considered, and the applicability k is obtained. As shown in FIG. 8, if the target pixel is within the hatched area, “1” is set as the applicability k. In step S <b> 120, the conversion table of the target image processing is referred to, and The image data is converted based on the equation (51). In the example shown in FIG. 8, the rectangular area specifying the background part and the rectangular area specifying the human image partially overlap each other.
The image data is converted by applying the expressions 9) to (51). Of course, the image data is also converted in the transition area so that a step does not occur with the undesignated peripheral portion.

The above process is repeated while moving the pixel to be processed in step S130 until it is determined in step S140 that all pixels have been executed. As a result, image processing is performed on the sky blue portion to enhance the saturation and make it look like a blue sky, and even when the person image portion is bright and in a backlight state, it is easy to see as if shooting with the flash turned on It becomes an image. Of course, even if the person image becomes bright, the sky portion does not become bright, and the saturation is not particularly emphasized in the person image portion.

Thereafter, the processed image data is displayed on the display 32 via the display driver 21c. If the image data is good, the image data is printed by the printer 31 via the printer driver 21b. That is, the printer driver 21b inputs RGB gradation data on which image processing has been executed as specified for each specified area, performs rasterization corresponding to the print head area of the printer 31 through predetermined resolution conversion, and , And converts the rasterized data from RGB to CMYK, then converts the CMYK gradation data to binary data and outputs it to the printer 31.

On the other hand, if the image processing application 21d does not perform image processing, but performs printing processing from a predetermined application and starts the printer driver 21b, the read image cannot be displayed in the display area 41 of the window 40. Often. In this case, the printer driver 21b can display an option selection screen as shown in FIG. 10, and the operator wants to clean the human skin while looking at the photograph before reading or to make the green of the trees vivid. Items such as desired image processing are selected in advance. This process corresponds to the process of specifying the application target in step S100 shown in FIG.

After selecting the option, the printer driver 2
1b prepares a conversion table internally, determines the chromaticity of each pixel of the input image data, and determines whether the chromaticity is the chromaticity of the option selected. Then, in the case of the target, referring to the conversion table (4
The conversion is performed based on the expressions 9) to (51), and the converted image data is converted into CMY image data that can be output to the printer 31.

As a result, pixels of human skin and green pixels of trees in the original image are corrected to be bright, and as a result, printed so as to look vivid.

As described above, the computer 21, which is the center of the image processing, designates the area to which the image processing is to be applied in step S100, and determines in step S110 to S140 whether the object pixel belongs to the designated area while moving the pixel. Since the specified image processing will be executed if it belongs while judging whether or not the image data of one area is corrected, the image data of another area will not be adversely affected. Beautification can be easily realized.

[Brief description of the drawings]

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

FIG. 2 is a block diagram of specific hardware 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 illustrating image processing in the image processing apparatus of the present invention.

FIG. 6 is a diagram illustrating a state in which a processing target pixel is moved.

FIG. 7 is a diagram illustrating a display state in which a processing target area and processing contents are designated;

FIG. 8 is a diagram showing a state where two processing target areas are selected.

FIG. 9 is a diagram showing an area-table correspondence table that stores a specified area and an image processing target;

FIG. 10 is a diagram showing a display state of a screen for selecting options for a processing target and processing contents.

FIG. 11 is a diagram illustrating a rectangular area specified by xy chromaticity.

FIG. 12 is a diagram illustrating a situation where a center point is designated by xy chromaticity.

FIG. 13 is a flowchart for setting an applicability.

FIG. 14 is a graph showing a change in applicability when a center point is designated by xy chromaticity.

FIG. 15 is a diagram illustrating a distribution range when the luminance distribution is expanded.

FIG. 16 is a diagram showing a conversion table when expanding a luminance distribution.

FIG. 17 is a diagram showing a conversion relationship for expanding a luminance distribution.

FIG. 18 is a diagram illustrating a concept of increasing brightness by γ correction.

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

FIG. 20 is a diagram illustrating a correspondence relationship of luminance changed by γ correction.

FIG. 21 is a diagram showing an unsharp mask of 5 × 5 pixels.

FIG. 22 is a schematic diagram of a tallying state of a saturation distribution.

FIG. 23 is a diagram illustrating a relationship between a saturation A and a saturation enhancement index S.

FIG. 24 is a diagram illustrating a processing target area in a case where image processing applied to the whole and image processing applied to a part are performed.

[Explanation of symbols]

 Reference Signs List 10 image input device 20 image processing device 21 computer 21a operating system 21b printer driver 21c display driver 21d image processing application 22 hard disk 23 keyboard 24 CD-ROM drive 25 floppy disk drive 26 modem 30 ... Image output device

Claims (12)

[Claims] An image processing apparatus for inputting real image data composed of dot matrix pixels and converting the image data of each pixel in a predetermined correspondence, wherein the correspondence for converting the image data is represented by Correspondence holding means provided with a plurality of pieces of information to be applied, and a correspondence for judging a correspondence to convert image data of each pixel based on information of an application target of each correspondence held by the correspondence holding means. An image processing apparatus comprising: a determination unit; and an image data conversion unit that converts the image data of each pixel by referring to the correspondence determined by the correspondence determination unit from the correspondence holding unit. . 2. The image processing apparatus according to claim 1, wherein said correspondence holding means holds a correspondence to be applied to each part of the image and has information on a part to which each correspondence is applied. The correspondence determining means detects a part in the image of each pixel, and compares the detected part with information of each part in the correspondence holding means to convert the corresponding part. An image processing apparatus characterized by determining: 3. The image processing apparatus according to claim 1, wherein the correspondence holding unit holds a plurality of correspondences and information of chromaticity to which each correspondence is to be applied. And the correspondence determination means includes:
An image characterized by detecting the chromaticity of each pixel and comparing the detected chromaticity with the chromaticity information for each corresponding relationship held by the corresponding relationship holding means to determine a corresponding relationship to be converted. Processing equipment. 4. The image processing apparatus according to claim 1, wherein said correspondence holding unit includes:
An image processing apparatus comprising: a color conversion table that stores a correspondence relationship between conversion source image data and converted image data. 5. The image processing apparatus according to claim 1, wherein the correspondence holding unit includes:
An image processing apparatus for realizing a plurality of correspondences by changing a degree of application of a correspondence. 6. The image processing apparatus according to claim 1, wherein the correspondence holding unit includes:
An image processing apparatus comprising: a correspondence specification unit that specifies a correspondence to be applied to the image data. 7. The image processing device according to claim 1, wherein the correspondence holding unit includes:
An image processing apparatus, wherein a correspondence for changing brightness based on the image data is held. 8. The image processing apparatus according to claim 1, wherein the correspondence holding unit includes:
An image processing apparatus for maintaining a correspondence for changing chromaticity based on image data. 9. The image processing apparatus according to claim 1, wherein the correspondence holding unit includes:
An image processing apparatus for storing a correspondence that changes the vividness of a color based on image data. 10. The image processing apparatus according to claim 1, wherein the image data conversion means changes the correspondence of pixels in a transition area between areas having different correspondences. An image processing apparatus characterized in that image data is converted such that the image data is converted. 11. An image processing method for inputting real image data composed of pixels in a dot matrix and converting the image data of each pixel in a predetermined correspondence, wherein the correspondence for converting the image data is determined by A plurality of the image data of each pixel is determined based on the information of the application target of each corresponding relationship, and the image data of each pixel is converted based on the determined corresponding relationship. Characteristic image processing method. 12. A medium in which an image processing program for inputting a real image data consisting of pixels in a dot matrix form by a computer and converting the image data of each pixel in a predetermined correspondence relationship is recorded. A plurality of correspondences for converting the image data are provided together with the information of the application target, and the correspondence to convert the image data of each pixel is determined based on the information of the application target of each correspondence, and the correspondence of each pixel is determined based on the determined correspondence. A medium storing an image processing program for converting image data.