JPH09294212A - Image processing unit and image processing method - Google Patents

Abstract

PROBLEM TO BE SOLVED: To improve image quality around a low density area by dividing image data in the low density area of a color space finer than the data in other areas, storing correction data to coordinates of a lattice point in the color space, transforming the data into coordinates of the lattice point and reading the correction data at the lattice point. SOLUTION: A color space is divided into lattice points and correction data of each color component are stored in each lattice point 300. In the case of desiring a corresponding color, gradation correction data of each color component of R, G, B to be required are checked for each lattice point 300 and the result is stored in a color correction table memory. A proper division number is decided based on the balance between the capacity of the color correction table and the image quality. That is, a pre-conversion of gradation number is performed so that received original color data ORG are color data on a lattice point of a color correction table memory, and then color correction is conducted by referencing the color correction table memory and then post- gradation number is performed. Thus, complicated interpolation arithmetic operation is omitted.

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 and an image processing method, and more particularly to an image processing apparatus and method for performing color correction processing on input color image data and outputting the color image data.

[0002]

2. Description of the Related Art Conventionally, an image processing apparatus for reading a color original or the like by using an image input unit such as a scanner and reproducing and displaying the read image data by using a display such as a CRT or a color printer. It has been known.

Image output devices such as displays and color printers have unique color reproduction characteristics.
In order to properly reproduce the color of a color image input using a scanner or the like regardless of the characteristics of the output device, there has been proposed a method of performing color correction processing in accordance with the color reproduction characteristics of the image output device used. As one of such color correction methods,
There is one disclosed in JP-A-63-2669. In this conventional technique, an RGB three-dimensional color correction table is prepared corresponding to all combinations of three color components of red (hereinafter referred to as R), green (hereinafter referred to as G), and blue (hereinafter referred to as B). are doing. In this color correction table,
Color correction contents for all positions in the color space represented by three-dimensional coordinates are stored in advance. The image processing apparatus performs color correction by referring to this color correction table.

This color correction method is not sufficiently practical because the storage capacity of the color correction table used becomes enormous. For example, when the input original color image data has a gradation number of 8 bits (256 gradations) for each color of R, G, B, the number of colors becomes 256 cubed to reach about 16.78 million colors. Assuming that the data after color correction is also 8 bits, the color correction table is 4 for R, G, and B colors.
A storage capacity of 8 megabytes is required.

On the other hand, in order to avoid increasing the capacity of the color correction table, the color correction table is composed only of data corresponding to limited grid points, and the color data between grid points is the data of grid points. There are also proposals of utilizing the interpolation calculation to perform color correction processing (for example, JP-A-4-14481 and JP-A-4-185075). According to this method, the storage capacity of the color correction table can be reduced, but the interpolation calculation requires a considerable amount of time. In particular, when the resolution of image data to be handled becomes higher and the gradation expression for each pixel becomes finer as in recent years, the calculation time required to output one image becomes long, and it takes time to complete the image output. The waiting time becomes extremely long. However, if the interpolation calculation is simplified, the color reproducibility will deteriorate.

Therefore, the applicant of the present application has proposed an image processing method which does not increase the capacity of the color correction table and does not require a long time for the interpolation operation of the color correction (for example, Japanese Patent Application Laid-Open No. Hei 7 (1999) -96). -30772). This image processing method divides the color space at a predetermined interval, prepares color correction data only for the grid points obtained by the division, and for image data other than the grid points, the That is, the color correction is performed by allocating to those grid points and performing no interpolation calculation. In this case, since the original image data is allocated to the grid points in the vicinity thereof, an error occurs every time the grid points are allocated. Therefore, when the image data of each pixel forming the image is sequentially processed, the allocation to the grid points is performed so that this error will be as small as possible on average.

[0007]

In the image processing method, when attention is paid to each pixel, there is an error in the color, but the error is eliminated within a certain range, and the error is output. This is an excellent technique that significantly reduces the calculation time required for image processing without degrading the image quality. However, if the number of gradations that can be expressed by the image output device that outputs the image is low, for example, in an inkjet printer, Like 2
When only a value can be expressed, the image quality may be slightly deteriorated in a region where the image density is low. This is because, in the low density region where the dot density is small and the dots are sparsely distributed, the appearance position of the dots is largely displaced even if a little noise (quantization error caused by the allocation to the grid points).

Further, in the above image processing, even if pixels of the same color in the original image are close to each other, both pixels may be assigned to different grid points. Therefore, in order to reduce the storage capacity of the color correction table, When the color space division is roughened and the number of grid points is reduced, the color gap between the output pixels is large,
As a result, the image quality may be degraded.

An object of the present invention is to solve these problems and to further improve the quality of an output image centering on a low density area without increasing the calculation processing for color correction.

[0010]

Means for Solving the Problem and Its Action / Effect The first image processing apparatus of the present invention which achieves the above object, performs color correction on a multicolor image represented by coordinate values in a two-dimensional or more color space. An image processing device that outputs the color image data for each pixel of the image, the input means inputting color image data in which the coordinate value is expressed using a predetermined gradation number;
The color space is divided into a smaller number of gradations than the number of gradations expressing the coordinate values, and in a predetermined low density area of the color space, the color space is divided into smaller areas than other areas, and the division is performed. For each of the dimensions, the grid point information storage means stores the coordinate values of the grid points obtained for the color space, and the correction data relating to the color of the color image data corresponding to each grid point. The color correction table and the coordinate values of the input color image data in the color space are stored in the grid point information storage means according to a method in which the distance from the grid points is not more than a predetermined value on average. Lattice point conversion means for converting the stored coordinate values of the lattice points and the correction data of the lattice points corresponding to the converted coordinate values are read from the color correction table and output as corrected color image data. And summarized in that and a color correction means for.

A first image processing method corresponding to this image processing apparatus is an image processing method for color-correcting and outputting a multicolor image represented by coordinate values in a two-dimensional or more color space. , For each pixel of the image, the color image data in which the coordinate value is expressed using a predetermined gradation number is input, and the color space is defined by the gradation number smaller than the gradation number expressing the coordinate value. The color space is divided into the predetermined low-density areas of the color space more finely than the other areas, and the coordinate values of the lattice points obtained by performing the division for each of the dimensions are set for the color space. A color correction table that stores correction data relating to the color of the color image data stored in correspondence with each of the grid points is prepared, and the coordinate value of the input color image data in the color space is From the grid point According to a method in which the distance is on average less than a predetermined value, the coordinate values of the stored grid points are converted, and the correction data of the grid points corresponding to the converted coordinate values are read from the color correction table, The main point is to output as corrected color image data.

In this image processing apparatus and image processing method, this color sky angle is divided by a number of gradations smaller than the number of gradations expressing coordinate values in a two-dimensional or more-dimensional color space, and this division is performed for each dimension. The color correction data in which the coordinate values of the grid points obtained by the above are stored and the correction data regarding the color of the color image data is stored corresponding to each grid point is prepared. For each pixel of the image, input the color image data in which the coordinate value is expressed using a predetermined number of gradations, and set the coordinate value according to a method in which the distance from the grid point is below a predetermined value on average. Convert to point coordinates. By such conversion, it is not necessary to perform complicated interpolation calculation using a plurality of grid point data. In addition, since the grid points are finely divided in the low density area, the data interval of the color correction table in the low density area is also fine. Therefore, the quantization error in converting the coordinate value of the input data into the coordinate value of the grid point becomes small in the low density area where it becomes a problem, and the deterioration of the image quality is suppressed. As a result, a color correction calculation suitable for the image output device is performed at high speed, good color reproduction is obtained, and deterioration of image quality is suppressed.

In the present invention, the low density area means an area where the density of dots output to the image output device is low. For example, when the final image output is an ink jet printer that expresses gradation by turning dots on and off, it refers to a region where the density of ink dots such as CMY is low. Further, when the output device is a CRT or the like, if the white dots are focused, the white dots are sparsely distributed (high density region in the entire image), and if the black dots are focused, the black dots are sparsely distributed. It is a region (low density region in the entire image) to be processed. Further, as will be described later, in a printer that includes high-density ink and low-density ink of the same color, and separates dark dots and light dots, a region in which light dots are sparsely distributed (low in the entire image). Concentration area)
Not only that, the area where the dark dots are sparsely distributed also corresponds to the low density area for the ink.

As a two-dimensional or more-dimensional color space, RG is used.
Not only B and CMY color spaces, but also various colors such as XYZ color space, L * a * b * color space, L * C * h color space, Munsell color space, etc. You can think of the color space. The number of gradations is 2 when these coordinate values are expressed by n-bit number digital information.
It is often expressed in terms of the nth power (for example, 256 for 8 bits), 100 or 17 gradations, or 2
It does not matter if the number of gradations is other than n. In the above configuration, the coordinates of the grid points are finely divided in the low density area, but the gradation interval of each color component of the color image data corrected by the color correction means becomes narrow in the low density area. As such, it is also suitable to choose. This allows
Since the quantization error of the pre-gradation number conversion in the low density area in the data after color correction becomes small, the deterioration of the image quality due to the division of the color space with a small number of gradations can be suppressed more reliably. .

The number of gradations of the image data output by this image processing is preferably larger than the final number of gradations suitable for the image output device. In this case, this image data is given to the image output device after being further converted into the final gradation number, but the quantization error of the gradation number conversion due to the above-mentioned allocation to the grid points. Is smaller than the error that occurs when converting to the final gradation number suitable for the output device. As a result, deterioration in image quality due to the introduction of gradation number conversion by allocation to grid points is suppressed.

The above-mentioned conversion of the image coordinate values to the coordinate values of the lattice points can be performed by using an error diffusion method.
It is also possible to convert the coordinates of the lattice points by the systematic dither method using the threshold matrix of the distributed dither. According to these methods, the quantization error can be properly dispersed.

Also, as one of the ways of taking a color space, RG
When the color image data is formed by combining the densities of a plurality of basic colors like B and CMY systems, the number of gradations expressing the coordinate values in the color space is directly related to the density of each color component of the color image data. The color correction table has a corresponding format and stores information about the gradation of each color component of the color image data, that is, the density. In this case, the grid point information divides the predetermined low-density area of the color space so that the gradation interval of each color component of the color image data is narrower in the predetermined low-density area than in the other density areas. It should be done.

The color correction table for correcting the color of the color image data can store, as the correction data, one that is adapted to the color reproduction characteristic of the image output device that finally processes the color image data. There are various possible purposes for color correction.
The correction in accordance with the color reproduction characteristics of the image output device is one of those often required in image processing.

A second image processing apparatus of the present invention is an image processing apparatus for color-correcting and outputting a multicolor image represented by coordinate values in a two-dimensional or more-dimensional color space, and each pixel of the image. With respect to, the color space is divided by input means for inputting color image data in which the coordinate value is expressed using a predetermined number of gradations, and the number of gradations smaller than the number of gradations expressing the coordinate value, And, in a predetermined low-density area of the color space, the color space is divided into smaller areas than other areas, and the coordinate values of grid points obtained by performing the division for each of the dimensions are stored in the color space. Point information storage means, a color correction table storing correction data relating to the color of the color image data corresponding to each grid point, coordinate values of the input color image data in the color space, The grid points The grid point conversion means for converting the grid point coordinate values stored in the grid point information storage means into grid points corresponding to the converted coordinate values according to a method in which the distance between the grid points is less than a predetermined value on average. Color correction data reading means for reading the point correction data from the color correction table;
A value corresponding to the main color is selected from the correction data of the read grid points, and the correction data of the adjacent grid points in which only the coordinate values corresponding to the main color are shifted from the grid point for each main color Of these, the value corresponding to the main color is read, interpolation is performed using both values, and the correction data obtained by replacing the correction data for the main color with the interpolation result is output as final corrected color image data. It is a gist to provide a color correction unit for performing the above.

An image processing method compatible with this image processing apparatus is an image processing method for color-correcting and outputting a multi-color image represented by coordinate values in a two-dimensional or more-dimensional color space. For the pixel, color image data in which the coordinate value is expressed using a predetermined number of gradations is input, the color space is divided by the number of gradations smaller than the number of gradations expressing the coordinate value, and In the predetermined low-density area of the color space, the color space is divided into smaller areas than other areas, and the coordinate values of the grid points obtained by performing the division for each of the dimensions are stored for the color space. A color correction table that stores correction data relating to the color of the color image data is prepared corresponding to the grid points, and the coordinate values of the input color image data in the color space are separated from the grid points. Is flat Specifically, the coordinate values of the stored grid points are converted in accordance with a method of being a predetermined value or less, and the correction data of the grid points corresponding to the converted coordinate values are read from the color correction table and read out. A value corresponding to the main color is selected from the correction data of the selected grid points, and the main data of the correction data of the adjacent grid points in which only the coordinate values corresponding to the main color are shifted from the grid point for each main color Read the value corresponding to the color and perform interpolation using both values.
The gist is that the correction data obtained by replacing the correction data for the main color with the interpolation result is output as final corrected color image data.

According to this image processing apparatus and image processing method, like the first image processing apparatus and image processing method,
Good results can be obtained with less deterioration of image quality in the low density region. Furthermore, in the second technique, using the correction data read from the color correction table, interpolation is performed only for the main colors, so that there is no substantial complication of calculation,
It is possible to suppress deterioration of image quality due to conversion of coordinate values in the color space into coordinate values of grid points. The process of this interpolation is
The pre-gradation number conversion may be performed only in the low density region where the influence of the quantization error causes a problem.

The interpolation process performed only for the main colors may be a simple one-dimensional interpolation operation such as linear interpolation. This is because the coordinate value conversion is originally performed so that the distance from the grid point is, on average, less than or equal to a predetermined value, so that the effect can be sufficiently obtained even by a simple interpolation calculation. Of course, there is no problem in performing high-order interpolation calculation using the coordinate values of other adjacent grid points.

In such an image processing apparatus, the coordinate value of the color image data is defined as a value corresponding to the density of each color component of the color image data, and the grid point conversion means performs gradation conversion of the grid point coordinate. The first gradation number converting means performs the number conversion, and the number of gradations for each color component of the corrected color image data output from the color correcting means is the number of gradations suitable for the image output device. Second gradation number converting means for converting the number of gradations, and the number of gradations obtained by converting the coordinate values by the first gradation number converting means is converted by the second gradation number converting means. The number of gradations after being processed can be larger.

A third image processing apparatus of the present invention is an image processing apparatus for color-correcting and outputting a multicolor image represented by coordinate values in a color space of two or more dimensions, and each pixel of the image. With respect to, the color space is divided by input means for inputting color image data in which the coordinate values are expressed in a predetermined gradation number, and the color space is divided by a gradation number smaller than the gradation number expressing the coordinate values. For each of the dimensions, the coordinate values of the grid points are stored in the color space, and the correction data relating to the color of the color image data is stored corresponding to each grid point. The color correction table and the coordinate values of the input color image data in the color space are stored in the grid point information storage means according to a method in which the distance from the grid points is not more than a predetermined value on average. Record Grid point conversion means for converting the coordinate values of the selected grid points, color correction data reading means for reading the correction data of the grid points corresponding to the converted coordinate values from the color correction table, and the color correction data reading The gist is that it is provided with an averaging processing means for averaging the correction data of each pixel read by the means based on the correction data of the pixels in the vicinity of each pixel.

An image processing method compatible with this image processing apparatus is an image processing method for color-correcting and outputting a multi-color image represented by coordinate values in a two-dimensional or more color space. For each pixel, color image data in which the coordinate value is expressed in a predetermined gradation number is input, the color space is divided by a gradation number smaller than the gradation number expressing the coordinate value, and the division is performed. The coordinate values of the grid points obtained by performing the above for each of the dimensions are stored in the color space, and a color correction table storing correction data relating to the color of the color image data is prepared corresponding to each of the grid points. Then, the coordinate value of the input color image data in the color space is converted into the coordinate value of the stored grid point according to a method in which the distance from the grid point is not more than a predetermined value on average. Shi The correction data of the lattice point corresponding to the converted coordinate value is read from the color correction table,
The gist is to perform a process of averaging the read correction data of each pixel based on the correction data of pixels in the vicinity of each pixel.

According to the image processing apparatus and the image processing method, the conventional complicated interpolation calculation can be omitted by the method of converting the coordinate value in the color space into the coordinate value of the grid point, and the good color reproduction can be realized. Is obtained. Further, the correction data after color correction is subjected to an averaging process (smoothing process) based on the correction data of neighboring pixels to convert the coordinate value of each pixel into the coordinate value of the grid point. Can be suppressed. This smoothing process may be performed only in the low density region where the influence of the quantization error in the conversion of the coordinate values poses a problem.

It is desirable that the number of gradations of the image data output by this image processing is larger than the final number of gradations suitable for the image output device. In this case, this image data is given to the image output device after being further converted into the final gradation number, but the quantization error of the gradation number conversion due to the above-mentioned allocation to the grid points. Becomes smaller by conversion to the final gradation number suitable for the output device. As a result, deterioration in image quality due to the introduction of gradation number conversion by allocation to grid points is suppressed. This point is the same as the first image processing apparatus and the image processing method.

The above-mentioned conversion of the image coordinate value to the coordinate value of the grid point can be performed by using an error diffusion method.
It is also possible to transform the coordinates of the lattice points using a threshold matrix of distributed dither. According to these methods, the quantization error can be properly dispersed. Further, it is also possible to adopt a configuration in which which method is used is switched based on the nature of the image. The error diffusion method is excellent in the dispersibility of errors, but generally requires more calculation processing. On the other hand, the distributed dither method is superior in terms of high-speed processing. Therefore, it is also desirable to switch between the two methods and make the best use of their respective characteristics. In this case, the averaging process may be performed in a region where the density of image data is low and when the error diffusion method is used.

Various pixels can be considered as the pixels to be averaged when the above averaging process is performed. For example, neighboring pixels in the case of performing the averaging process may be adjacent pixels along the direction in which the color image data is input. Normally, the image processing is performed in the order in which the image data is input. Therefore, if the pixels are adjacent pixels along the input direction, the processing becomes easy. As the adjacent pixel along the input direction, the pixel on the front side in the input direction may be used, or the pixel on the rear side may be used. Furthermore, it is also possible to take the average of adjacent pixels on both sides in the front and back. Further, as the neighboring pixels for which the averaging process is performed, it is possible to use pixels adjacent to each other in the direction of inputting the color image data and pixels in the direction intersecting with each other. As the averaging process, it is possible to use a weighted average value with a predetermined weighting, in addition to the simple average value.

Here, the coordinate value of the color image data is defined as a value corresponding to the density of each color component of the color image data, and the grid point conversion means converts the grid point coordinate to the gradation number conversion. And a gradation number for each color component of the corrected color image data output from the color correcting unit to a gradation number suitable for the image output device. A second gradation number converting means for performing a gradation conversion is provided, and the gradation number obtained by the conversion of the coordinate values by the first gradation number converting means is converted by the second gradation number converting means. The number of gradations can be larger than the number of gradations later. In this case, the quantization error by the first gradation number conversion means is reduced by the conversion of the gradation number by the second gradation number conversion means, and the influence of deterioration of the image quality due to the former quantization error is small. Become.

Here, the second gradation number conversion means is a means for binarizing and expressing gradation by the binarized distribution density of the dots, and the averaging means is binarized. When the dot density is less than or equal to a predetermined value, the averaging process can be performed. In the case of an image output device that performs binarization, since the density is expressed by the density of dots, if the averaging is performed when the density is below a predetermined value, the deterioration of the image quality in the low density area will occur. Can be prevented.

Further, in such an image processing apparatus, the averaging processing means is provided with means for comparing the color correction data read by the color correction data reading means with the color correction data of the adjacent pixels, and the compared both color corrections are performed. The averaging process may be performed when the difference between the data is less than or equal to a predetermined value. If the averaging process is performed uniformly, the sharpness of the image tends to be lost. In the place where an edge is originally present, the colors of adjacent pixels are often widely separated, so if averaging is performed when the difference in color correction data is less than or equal to a predetermined value, the original edge of the image is averaged. Is not performed, and the smoothness and sharpness of the image quality can both be achieved.

Whether or not to perform such averaging processing is determined for at least one of the colors forming the color image data, and the averaging processing is performed for each color based on the determination for this color. May be performed.

Alternatively, the averaging process may be performed when it is estimated that the distances in the color space of the color image data input to the adjacent pixels are equal to or less than the predetermined distance.

Various judgment methods can be used for estimating whether or not the separation of the color image data in the color space is less than or equal to a predetermined distance. For example, when the neighboring pixels are at least adjacent to the lattice points after being transformed by the lattice point converting means, it can be estimated that the distance between the neighboring pixels is less than or equal to a predetermined distance. Further, when the color image data of the adjacent pixels are both included in the unit space formed by the lattice points stored in the lattice point information storage means, the distance between the adjacent pixels is less than or equal to a predetermined distance. Can also be estimated. When pixels having the same or substantially the same color in the original image are assigned to different lattice points as a result of being transformed by the lattice point transforming means, the averaging process loses the fineness and resolution of the image. This is because the image quality will be improved without any effort.

When it is estimated that the distance between adjacent pixels in the color image data in the color space is less than the predetermined distance, it is determined whether another execution condition for the averaging process is satisfied, and the two adjacent pixels are judged. The averaging process can be performed only when it is determined that the distance between two pixels is less than or equal to the predetermined distance and the other execution conditions are satisfied. As other execution conditions, various conditions can be considered, such as when the image data has a predetermined density or less.

Also in the image processing apparatus for performing such averaging processing, the grid point information storage means stores the coordinates of grid points
In a predetermined low-density area of the color space, the color space may be divided into smaller areas than other areas and stored. As a result, the image quality in the low density region is further improved.

Furthermore, the fourth image processing apparatus of the present invention is
An image processing apparatus for color-correcting and outputting a multi-color image represented by coordinate values in a color space of three dimensions or more, wherein a color in which the coordinate value is represented by a predetermined gradation number for each pixel of the image. Input means for inputting image data and coordinates of grid points obtained by dividing the color space by a number of gradations smaller than the number of gradations expressing the coordinate values and performing the division for each dimension. Grid point information storage means that stores values for the color space; a color correction table that stores correction data relating to the color of the color image data corresponding to each grid point; and the color image data of the input color image data. A grid point converter for converting the coordinate values in the color space into the coordinate values of the grid points stored in the grid point information storage means according to a method in which the distance from the grid points is, on average, a predetermined value or less. A color correction data reading means for reading the correction data of the grid points corresponding to the converted coordinate values from the color correction table; and a value corresponding to the main color of the read correction data of the grid points. For each main color, the value corresponding to the main color is read out from the correction data of the adjacent grid points in which only the coordinate values corresponding to the main color are shifted from the grid point, and interpolation is performed using both values. Done,
The gist of the present invention is to provide a color correction unit that outputs the correction data in which the correction data for the main color is replaced with the interpolation result.

An image processing method compatible with this image processing apparatus is an image processing method for color-correcting and outputting a multicolor image represented by coordinate values in a two-dimensional or more color space. For each pixel, color image data in which the coordinate value is expressed in a predetermined gradation number is input, the color space is divided by a gradation number smaller than the gradation number expressing the coordinate value, and the division is performed. The coordinate values of the grid points obtained by performing the above for each of the dimensions are stored in the color space, and a color correction table storing correction data relating to the color of the color image data is prepared corresponding to each of the grid points. Then, the coordinate value of the input color image data in the color space is converted into the coordinate value of the stored grid point according to a method in which the distance from the grid point is not more than a predetermined value on average. Shi The correction data of the lattice point corresponding to the converted coordinate value is read from the color correction table,
A value corresponding to the main color is selected from the correction data of the read grid points, and the correction data of the adjacent grid points in which only the coordinate values corresponding to the main color are shifted from the grid point for each main color The gist is to read out the value corresponding to the main color, interpolate using the both values, and output the correction data in which the correction data for the main color is replaced with the interpolation result.

According to this image processing method, since the interpolation calculation is performed only for the main color, the amount of calculation is small, and the quantization error for the main color is eliminated by the interpolation. Therefore, without slowing down the processing speed,
It is possible to minimize the deterioration of the image quality that is visually recognized.

The grid point conversion using the grid points finely divided in the low density area, the interpolation processing for the main color, and the averaging processing between the adjacent grid points are performed.
It is also possible to carry out together. In this case, the respective advantages can be utilized to shorten the time required for the calculation and to suppress the deterioration of the image quality due to the grid point conversion to the minimum.

[0042]

DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS Next, preferred embodiments of the image processing apparatus of the present invention will be described in detail with reference to the drawings. FIG. 1 shows an image processing apparatus 3 as a first embodiment of the present invention.
An example of a zero-based color image processing system is shown. In this image processing system, the original color image data OR output from the image input device 10 such as a scanner is output.
G is input to the image processing device 30. Image processing device 3
The image data processed by 0 is finally output to the image output device 20 such as a printer, where a final image is obtained. Since the description of the embodiments is wide-ranging, (1) hardware of the image processing apparatus (2) outline of image processing-first, the description will be given in this order, and further, first and second examples will be described here. After that, (3) Outline of image processing-the second, third to sixth
Examples will be described.

(1) Hardware of Image Processing Device The image processing device 30 receives the input original color image data O
As image processing for matching RG with the color reproduction characteristics of the image output device 20, color correction and gradation number conversion are performed. The color correction is a process for correcting the output characteristics of the image output device, such as gamma correction. Further, the gradation number conversion means color correction when the number of gradations that can be output by the image output apparatus 20 is smaller than the number of gradations that can be output by the color image data ORG output from the image input apparatus 10. This is a process of converting the obtained color image data into a final number of gradations suitable for the image output device 20. For example, the image data ORG read from a scanner or the like has a gradation number of 256 (for 8 bits) for each color of R, G, B, and the image output device 20 performs an ink on / off ink jet printer. However, there may be a configuration in which the final number of gradations is two. In this case, the image processing device 30 converts the image data of 256 gradations into 2 gradations and outputs it as the final color image data FNL to the image output device 20. In the above explanation, the gradation number conversion is collectively called,
Actually, the pre-gradation number conversion for reducing the number of gradations by allocating the input original color image data ORG to the grid points having a small number of gradations before the color correction and the data after the color correction are stored in the printer. The gradation number conversion is performed by so-called halftone processing that binarizes in accordance with the expressible gradation number. In the following description, the former is called pre-gradation number conversion and the latter is called post-gradation number conversion. Each gradation number conversion will be described in detail later.

FIG. 2 is a block diagram showing an example of a concrete configuration of the color image processing system shown in FIG. Here, as the image input device 10, a scanner 12 that optically reads a color image from a document is used. The scanner 12 converts the read color image data into R, G,
The original color image data ORG composed of the three color components B is output. In the embodiment, each color of R, G, B is represented by 8-bit digital data,
The number of gradations is 256. In this case, the scanner 12
Indicates that the primary color image data is represented by the three primary colors of R, G, B, and where the color of each pixel is located in the three-dimensional color space having the respective colors of R, G, B as coordinate axes. However, no matter what color system such as L * a * b * is adopted, where is the color of a pixel located in the color space? That is, it can be expressed as coordinate values.

As the image input device 10, other than the scanner 12 as described above, for example, a video camera, a host computer for creating computer graphics, or other means can be used.

Further, in the image processing system of the embodiment, as the image output device 20, the color printer 22 which cannot control the gradation in pixel units is used. The color printer 22 needs to be binarized to reduce the number of gradations of each color component of the original color image data ORG output from the scanner 12 to two gradations corresponding to ON / OFF of each pixel. Was mentioned above.

As the image output device 20, other than this, for example, a color display 21 or the like can be used. Many of the color displays 21 for computers and the like have a smaller number of gray scales that can be displayed as compared with a normal home TV. Even if such a color display 21 is used, it is necessary to convert the gradation number of the original color image data ORG into the gradation number corresponding to the display 21.

Next, a specific configuration corresponding to the image processing device 30 will be described. In FIG. 2, the image processing device 30
A configuration using the computer 90 is shown as.
Inside the computer 90, a CPU (not shown),
RAM, ROM, etc. are provided, and an application program 9 is provided under a predetermined operating system.
5 is working. A video driver 91 and a printer driver 96 are incorporated in the operating system, and the final color image data FNL is output from the application program 95 via these drivers. An application program 95 for retouching an image reads the image from the scanner 12, performs a predetermined process on the image, and displays the image on the CRT display 93 via the video driver 91. This application program 95
However, when the print command is issued, the printer driver 96 of the computer 90 receives the image information from the application program 95, and the printer 22 can print the signal (here, a binarized signal for CMY).
Has been converted to. In the example shown in FIG. 2, inside the printer driver 96, a rasterizer 97 for converting color image data handled by the application program 95 into image data in dot units, an image output device for image data in dot units ( Here, the color correction module 98 that performs color correction according to the ink colors CMY used by the printer 22) and the characteristics of color development, the color correction table CT referred to by the color correction module 98, the dot unit from the image information after color correction A halftone module 99 for generating so-called halftone image information that expresses the density in a certain area depending on the presence or absence of ink is provided.

The pre-gradation number conversion described above is performed by the color correction module 98, and the post-gradation number conversion is performed by the halftone module 99. Color correction module 98
The processing in (1) corresponds to the grid point conversion processing.

Next, the structure of the printer 22, which is an output device, will be briefly described. FIG. 3 is a schematic configuration diagram of the printer 22. As shown, this printer 22
A mechanism for conveying the paper P by the paper feed motor 23, a mechanism for reciprocating the carriage 31 in the axial direction of the platen 26 by the carriage motor 24, and a print head 28 mounted on the carriage 31 for driving to eject ink and dots. It is composed of a mechanism for controlling the formation and a control circuit 40 for exchanging signals with the paper feed motor 23, the carriage motor 24, the print head 28 and the operation panel 32.

On the carriage 31 of the printer 22,
A black ink cartridge 71 and a color ink cartridge 72 containing cyan, magenta, and yellow inks can be mounted. A total of four ink ejection heads 61 to 64 are formed on the print head 28 below the carriage 31, and at the bottom of the carriage 31,
An introduction pipe 65 (see FIG. 4) that guides the ink from the ink tank is erected on the head for each color. Carriage 3
When the cartridge 71 for black ink and the cartridge 72 for color ink are mounted on the cartridge 1 from above, an inlet tube is inserted into a connection hole provided in each cartridge, and ink is supplied from each ink cartridge to the ejection heads 61 to 64. Becomes possible.

A mechanism for ejecting ink will be briefly described. As shown in FIG. 4, the ink cartridges 71, 7
When the cartridge 2 is mounted on the carriage 31, the ink in the ink cartridge is sucked out through the introduction pipe 65 by utilizing the capillary phenomenon, and is sent to the respective color heads 61 to 64 of the print head 28 provided below the carriage 31. Be guided. When the ink cartridge is installed for the first time,
Ink is supplied to each color head 61 to 64 by a dedicated pump.
However, in the present embodiment, illustration and description of the configuration of the pump for suction, the cap that covers the print head 28 during suction, and the like are omitted.

As shown in FIG. 4, each color head 61 to 64 is provided with 32 nozzles n for each color, and each nozzle is one of the electrostrictive elements and is excellent in responsiveness. The piezo element PE is arranged. FIG. 5 shows the structure of the piezo element PE and the nozzle n in detail. As shown in the drawing, the piezo element PE is installed at a position in contact with an ink passage 80 that guides ink to the nozzle n.
As is well known, the piezo element PE is an element which has a crystal structure which is distorted by application of a voltage and which converts electric-mechanical energy at extremely high speed. In this embodiment, by applying a voltage having a predetermined time width between the electrodes provided at both ends of the piezo element PE, the piezo element PE expands for the voltage application time as shown in the lower part of FIG. One side wall 80 is deformed. As a result, the volume of the ink passage 80 contracts in accordance with the expansion of the piezo element PE, and the ink corresponding to this contraction becomes particles Ip and is ejected from the tip of the nozzle n at high speed. When the ink particles Ip soak into the paper P mounted on the platen 26, printing is performed.

In the printer 22 having the above-described hardware structure, the platen 26 and other rollers are rotated by the paper feed motor 23 to convey the paper P, while the carriage 31 is reciprocated by the carriage motor 24, and at the same time, the print head. The piezo elements PE of the 28 color heads 61 to 64 are driven to eject the respective color inks, thereby forming a multicolor image on the paper P.

The mechanism for conveying the paper P is the paper feed motor 2
A gear train (not shown) for transmitting the rotation of No. 3 not only to the platen 26 but also to a paper transport roller (not shown) is provided. Further, the mechanism for reciprocating the carriage 31 is such that an endless drive belt 36 is stretched between a carriage shaft 24 and a slide shaft 34 that is installed in parallel with the shaft of the platen 26 and slidably holds the carriage 31. Pulley 38 to
Position detection sensor 3 for detecting the origin position of carriage 31
9 and so on.

A CP (not shown) is provided inside the control circuit 40.
Although U and the like are provided, color correction and the like according to the output characteristics of the printer 22 are processed inside the computer 90, and the printer 22 does not perform any processing relating to conversion of the number of gradations or color correction. Not not. The processing executed inside the printer 22 is only required to receive the data output from the computer 90 and drive the piezo elements PE of each color of the print head 28 in synchronization with the above-described paper feeding and the reciprocating movement of the carriage 31. is there. Therefore, detailed description of the control circuit 40 of the printer 22 and its processing will be omitted.

(2) Outline of Image Processing Next, an outline of image processing will be described. The image processing adopted in the present embodiment covers a wide range, such as pre-gradation number conversion, color correction, and host gradation number conversion. In addition, there are many variations of these processes such as the error diffusion method, the minimum mean error method, the systematic dither method, the color correction by software, the color correction by hardware, etc. explain. [A] Outline of Image Processing [B] Outline of Image Processing Common to First to Seventh Embodiments [C] Details of Image Processing Common to First to Third Embodiments [D] First to Third Embodiments Example [E] Details of Image Processing Common to Fourth to Seventh Embodiments [F] Fourth to Seventh Embodiments

[A] Outline of Image Processing The correction table CT referred to by the color correction module 98 inside the computer 90 is R, G, B as shown in FIG.
3 is a color table in which a three-dimensional color space composed of three colors is divided into a grid pattern. When the printer driver 96 is installed in the operating system, this table CT is read from, for example, the hard disk and expanded in the RAM of the computer. At each grid point of this color table, for example, a color original for reading such as the scanner 12 and an output color image printed on a recording paper using, for example, the color printer 22 have the same color. CM in which gradation value data of R, G, B is converted into gradation values
Y color gradation correction data is stored.

The computer 90 performs correction processing (pre-gradation number conversion) on the original color image data ORG input from the scanner 12 using the correction data stored in the correction table CT, and the color corrected color is further corrected. The data is transferred to the image output device 20 such as a color printer 22.
The post-gradation number conversion processing for converting the final gradation number in accordance with the gradation number N is performed as described above.
The final color image data FNL converted in this way may be directly output to the color printer 22, or may be stored in the memory of the computer 90 as the final color image data FNL for one screen. After that, the data may be output to the color printer 22.

[B] Outline of Image Processing Common to First to Eighth Embodiments FIG. 7 is a functional block diagram of the image processing apparatus 30 realized by the color correction module 98 and the halftone module 99 of the computer 90. It is shown. In FIG. 7, the image processing device 30 includes a pre-gradation number conversion unit 140.
A color correction unit 142, a post gradation number conversion unit 146,
The color correction table memory 134 stores the above-mentioned color correction table CT. First,
The correction data stored in the color correction table memory 134 will be described.

As shown in FIG. 6, the input original color image data ORG is defined as a coordinate value in a color space having coordinate axes corresponding to R, G, and B color components. For each coordinate axis, the number of gradations of each color component is set as a coordinate value. Then, this color space is divided into a grid shape, and the correction data of each color component is stored for each grid point 300.

There are various methods for determining the correction data to be stored in the color correction table memory 134. The process of determining it and storing it in the table CT will be briefly described. Usually, first, various R, G, and B values are given to a target output system (for example, the color printer 22), and the actually output result is subjected to color measurement. Then, the values of R, G, B given to the output system and the R, G obtained by measuring the output result
Check the correspondence with the values of G and B. The printer 22
The actual ink color in C. is CMY, but here, in order to simplify the explanation, all are treated as R, G, and B. R, G,
The conversion from B to C, M, Y will also be described in detail later.

Next, looking at the correspondence relationship in reverse, the gradation number correction data of each color component of R, G, B necessary for obtaining the corresponding color is examined for each grid point 300 and corrected. The data is stored in the color correction table memory 134 as data.

In this way, the color correction table memory 13
In 4, the color correction result corresponding to each grid point 300 is stored. Although the image quality of the color space improves as the number of divisions increases, the color correction table memory 134
Therefore, the appropriate number of divisions is determined from the balance between the color correction table capacity and the image quality.

The image processing apparatus 30 of the embodiment uses the correction data thus stored in the color correction table memory 134 to perform the image processing on the original color image data ORG as follows.

First, the pre-gradation number conversion unit 140 uses a predetermined gradation number conversion method to calculate the number of gradations of the R, G, B color components of the original color image data ORG in the color space shown in FIG. The pre-gradation number conversion is performed to convert to the gradation coordinate value of the optimum lattice point inside. Then, the converted data is output to the color correction unit 142 as grid point color image data GCD. Here, R is N for each color component of the original color image data ORG.
The pre-gradation number conversion is performed so that r gradation, G becomes Ng gradation, and B becomes Nb gradation, and the results are output as Rk, Gk, and BK.

In the pre-gradation number conversion section 140,
As a method of converting the number of gradations, various methods such as a multivalued error diffusion method, a minimum average error method, and a multivalued systematic dither method can be adopted. The details will be described later.

The color correction unit 142 reads the gradation correction data of the grid points corresponding to the input grid point color image data GCD from the color correction table memory 134. Then, the color components Rk, G of the grid point color image data GCD
The gradation numbers of k and Bk are corrected and output as color correction data CCD to the post gradation number conversion unit 146. here,
Color correction data The data of each color component of CCD is Rc, Gc,
Expressed as Bc.

Then, the post-gradation number conversion unit 146 uses the error correction method or the minimum average error method to convert the color correction data CCD obtained from the color correction unit 142 to the post-gradation number up to the gradation number to be finally converted. Number conversion, and the final color image data FN in which each color data is represented by Rp, Gp, Bp
Output as L.

Therefore, this final color image data F
By outputting the NL to the color printer 22, for example, the color printer 22 can print a color image having good color reproducibility on the recording paper.

Among the above-mentioned constitutions, it is known in the related art to divide the color space into a grid and prepare the color correction table memory 134 in which the color correction data is recorded for each grid point (for example, for example. JP-A-7-30772). That is, the image processing device 30 is configured such that the color correction table memory 1
Before referring to 34, using the pre-gradation number conversion unit 140,
The pre-gradation number conversion is performed so that the input original color image data ORG becomes the color data on the grid points of the color correction table memory 134, and then the color correction table memory 134 is referenced to perform the color correction, and the post floor Tone conversion (for example, halftoning processing) is performed. This makes it possible to omit a complicated interpolation calculation when referring to the color correction table CT, and to significantly reduce the calculation time required for the color correction processing.

A supplementary description will be supplemented for each of the processes described above. First, the case where the error diffusion method or the minimum average error method is used for the pre-gradation number conversion processing in the pre-gradation number conversion unit 140 will be described as an example. Now, in the original color image data ORG input from the scanner 12, R = 12, G = 2
It is assumed that color areas having gradations of 0 and B = 24 are continuous in a certain area. Then, it is assumed that the color data of this color region is input to the image processing device 30.

FIG. 8 shows the original color image data ORG.
Eight grid points near the coordinate position in the color space of are shown. It is assumed that each lattice point is located at each vertex of the cube shown in FIG. 8 and its coordinate position is represented by the following equation (1).

[0074]

[Equation 1]

In the color correction table memory 134, color correction values for each of the R, G, and B color components for the color data of each grid point 300 are prepared. Further, in the example shown in FIG. 8, the original image data ORG is (R, G, B) = (12,
The data is represented by coordinate values of 20, 24).

When the color correction processing is performed by using the conventional technique of performing the interpolation calculation, the correction values of the color data at the eight grid points near the coordinate position in the color space of the original image data ORG are referred to. Then, using the referred eight color correction values, an interpolation calculation process for obtaining a weighted average according to the distance between the coordinate position of the original color image data ORG and each grid point is executed, and the color correction process is performed. . In the example shown in FIG. 8, since each grid point is equidistant from the coordinate position of the original color image data ORG, in the interpolation calculation, the color correction values of eight grid points are simply averaged to obtain R, G. , B, the correction data of each color component is obtained.

On the other hand, the color correction processing of the present invention is performed in the following procedure. First, the pre-gradation number conversion unit 140
Performs the pre-gradation number conversion using the error diffusion method or the minimum average error method so that the input original color image data ORG becomes any color data on the eight grid points. Next, the color correction unit 142 refers to the color correction table memory 134 and performs color correction processing.

The error diffusion method and the minimum average error method used in the pre-gradation number conversion unit 140 of the embodiment are arranged so that the average value of local colors in a certain area is equal to the original image data as much as possible. It works so as to convert the data into data values of neighboring grid points. Therefore, in the example of FIG. 8 in which the coordinate positions of the original color image data ORG are equidistant from each grid point, the result of pre-gradation number conversion shows that each of the eight grid point data is approximately 8 minutes. , The mixed color areas are obtained in equal proportions. As a result, as a result of performing color correction with reference to the color correction table CT on the color data subjected to the pre-gradation number conversion processing, a color area in which the color correction values of eight grid point colors are mixed with a probability of being substantially equal is obtained. Will be done.

As described above, the results obtained by the color correction processing of the conventional interpolation calculation and the color correction processing of the present embodiment are obtained when the average values of the colors in an appropriate area are compared. Are almost equal. That is, this color correction unit 14
In the case of 2, the quantization noise generated by the pre-gradation number conversion is added in each pixel unit. Nevertheless, when the local average value is taken, the conventional interpolation calculation is performed. In this case, almost the same color correction result can be obtained.

When the number of gradations that can be expressed by the image output device 20 is sufficiently large, there is a problem of deterioration in image quality due to the quantization noise generated by the above-described pre-gradation number conversion processing. In the case of the present embodiment, after this pre-gradation number conversion processing, the post-gradation number conversion unit 146 is used to make the color-corrected image data correspond to the number of gradations that can be expressed by the image output device 20. Since the post-gradation number conversion processing for converting to the final gradation number is performed, quantization noise is also generated in the conversion of the gradation number. Therefore, if the quantization noise generated in the pre-gradation number conversion in the pre-processing stage can be made sufficiently smaller than the quantization noise generated in the post-gradation number conversion in the final stage, there will be no practical problem. .
Therefore, in the apparatus of the embodiment, the pre-gradation number conversion unit 14
The number of convertible gradations of 0 is formed to be sufficiently larger than the number of convertible gradations of the post gradation number converting unit 146, thereby preventing deterioration of image quality due to the pre gradation number converting process. are doing.

Particularly, in the present embodiment, since the error diffusion method or the minimum average error method is used for the tone number conversion in the post tone number conversion unit 146, both the pre-tone number conversion and the post tone number conversion are performed. Through, the mechanism that tries to minimize the error when taking the local average value of the region works.
Therefore, by setting the number of convertible gradations in the pre-gradation number conversion processing to be sufficiently larger than the number of convertible gradations in the post gradation number conversion processing as in the embodiment, a plurality of color correction tables can be displayed. It is possible to obtain an image quality comparable to that of the conventional correction processing in which the interpolation calculation is performed by referring to the lattice points.

Actually, when each color component of the original color image data ORG has 256 gradations, the number of gradations that can be converted by the pre-gradation number conversion unit 140 is 32 for each color component and the post gradation number conversion unit 146. As a result of color correction and gradation number conversion, the number of gradations that can be converted in 2 is set to 2 for each color component, and as a result, the gradation is converted into 2 gradations after performing interpolation calculation as in the conventional case. It was confirmed that a good reproduced image could be obtained that could not be identified at all. Further, it was confirmed that even if the number of convertible gradations in the pre-gradation number conversion processing is reduced to about 8 gradations, an image quality without practical problems can be obtained.

As described above, the memory capacity of the color correction table memory 134 can be reduced as the number of converted gradations in the pre-gradation number conversion processing is decreased. For example, the output data after the color correction by the color correction unit 142 is m in terms of the number of color components,
Assuming that each color component has n-bit data, the memory capacity required for the color correction table memory 134 is such that the number of gradations that can be converted in the pre-gradation number conversion processing is L gradations (q
Bit) is represented by the following equation.

[0084]

[Equation 2]

If the number of color components m is 3, each color component is 8 bits (1 byte), and the number of gradations is 32 gradations (5 bits) for each color, Q = 3 × 8 × 2 ^ (5 × 3) ) = 98304 × 8 [bits] = 96K [byte] It should be noted that 1K [byte] is 1024 [byte].

Similarly, the memory capacity of the color correction table is calculated for each of the cases in which the number of gradations that can be converted into the number of pre-gradations is 16 gradations for each color and 8 gradations.
12 Kbytes for 6 gradations, 1.5 for 8 gradations
A small memory capacity of K bytes will suffice. On the other hand, when the original image data is 8 bits for each color (256 gradations), all the original color image data O that can be represented
If you try to prepare a color correction table for RG, 1
Data of about 6 Mbytes is required.

In the above description, the pre-gradation number conversion unit 140
Although the case where the error diffusion method or the minimum average error method is used as an example of the method of converting the number of gradations in the above has been described, the pre-tone number conversion unit 140 may use, for example, a systematic dither method or the like. It is also possible to convert the number of gradations by using another method.

In this case, in the pre-gradation number conversion section 140, the mechanism for minimizing the error when the local average value of the area is taken does not work, so that the error diffusion method is used. The image quality may be further deteriorated. This may cause a problem in the case of performing a large number of gradation conversions such that the number of gradations of the original image data is 256 and the number of gradations is reduced to 8. However, when the difference between the number of gradations used for expressing the original image data and the number of gradations that can be converted by the pre-gradation number conversion unit 140 is not so large, for example, the number of gradations of the original color image data ORG. Is 64 when it is reduced to 32 gradations or 16 gradations by the pre-gradation number conversion processing, or with a sufficiently high resolution for the output device,
If the spatial frequency of the quantization noise is high enough,
The effect of the quantization noise generated here was not so problematic when actually outputting various images. In such a case, a simpler gradation number conversion method such as an ordered dither method may be adopted instead of the error diffusion method. When a simple tone number conversion method such as the systematic dither method is adopted, the calculation time required for tone number conversion can be shortened.

[C] Details of Image Processing Common to First to Third Examples Next, more specific examples of the image processing apparatus as the above-mentioned embodiment will be described. Here, from the scanner 12 shown in FIG. 2, the original color image data ORG of 8 bits for each of R, G, B colors and 256 gradations is input to the computer 90 as the image processing apparatus 30, and the printer driver 96 of the computer 90 is It is assumed that the original color image data ORG is image-processed and the final color image data FNL is output to the color printer 22. The color printer 22 used here is one that prints with two gradations of ON (with dots) / OFF (without dots) of each color dot using three color inks of cyan C, magenta M, and yellow Y. . The specific configuration of printing in the printer 22 is
Already explained.

FIG. 9 shows a concrete block diagram of the image processing apparatus 30 used in this case. The pre-gradation number conversion unit 140 and the color correction unit 142 color-correct the input original color image data ORG, and further convert the color system from R, G, B to C, M, Y to obtain color correction data. Output as CCD. Then, the color correction data CCD is binarized by using the post gradation number conversion unit 146 corresponding to the number of gradations that can be displayed by the printer 22, and the final color image data F is obtained.
Output as NL.

Here, the correction data is set in the color correction table memory 134 as follows. In order to determine the correction data, first, only the post gradation number conversion unit 146 (halftone module 99 shown in FIG. 2) is taken out from the image processing apparatus shown in FIG. 9 and the target color printer 22 is combined. Make up the system. Then, after various C, M, and Y values are given to the post-gradation number conversion unit 146 and binarized, the result output to the target color printer 22 is subjected to color measurement. Then, the correspondence relationship between the C, M, and Y values given to the post gradation number conversion unit 146 and the R, G, and B values obtained by measuring the output result of the color printer 22 is checked.

Next, by reversing the correspondence relationship, the C, M, and Y values required to obtain the R, G, and B value colors corresponding to the grid point color data in the color space are obtained. Is set in the color correction table memory 134 as color correction data. The above processing is performed to prepare the color correction table CT prior to the actual image processing.

Then, the pre-gradation number conversion unit 140 converts the respective color components of R0, G0, B0 of the input original color image data ORG into 16 gradations for R and G and 8 gradations for B. Pk as a grid point color image data GCD after conversion
The Gk and Bk color components are output. This is because Nr and Ng are 16 and Nb is 8 in the embodiment shown in FIG.
This is an example of the case. For this reason, in the present embodiment, multi-value quantization is performed by the error diffusion method or the minimum average error method, but an existing method may be used for the multi-value quantization process itself.

Next, a specific example of the multi-value conversion will be described by taking the case where the B component is converted to 8 gradations as an example. Now, it is assumed that the B component of the original color image data ORG is represented by a binary number of 8 bits and the number of gradations is 256 gradations from 0 to 255. , Shown below 8
Type (i = 0, 1, ... 7) grid point color data pre_
It is assumed that B is octal-valued.

Pre_B [0], pre_B [1],
, Pre_B [7] Specifically, it is assumed that the B component of the original color image data ORG is subjected to pre-gradation number conversion as follows. In this example,
The eight values are substantially evenly spaced, but in the first and second embodiments described later, the lower the density region, the finer the value is set.

Pre_B [0] = 0; pre_B [1] = 36; pre_B [2] = 73; pre_B [3] = 109; pre_B [4] = 146; pre_B [5] = 182; pre_B [6] = 219; pre_B [7] = 255; In the following description, the grid point color data is pre [i].
May be abbreviated.

Further, seven kinds of threshold values used for the pre-gradation processing are defined as follows. slsh_B [0], slsh_B [1], ..., sls
h_B [6]

Then, each threshold value is set as follows. pre_B [i] <slsh_B [i] <pre_B
[I + 1] (i = 0, 1, 2, ..., 6)

The threshold value is often set as follows. slsh_B [i] = (pre_B [i] + pre_B
[I + 1]) / 2 In this case, the respective threshold values are as follows.

Slsh_B [0] = 18; slsh_B [1] = 54; slsh_B [2] = 91; slsh_B [3] = 127; slsh_B [4] = 164; slsh_B [5] = 200; slsh_B [6] = 236;

The original color image data ORG input as image data to the image processing device 30 is normally input in order from the left end pixel to the right end pixel with the pixel at the upper left corner of the image as the starting point pixel. Then, after the pixels for one row are input, the data moves to the left end of the row one pixel below, and the subsequent data is input to the right end in the same manner. By repeating such an image input operation, image data for one screen is input.

Therefore, the pre-gradation processing by the pre-gradation number conversion unit 140, that is, the original color image data OR
The order in which G is octalized is set according to the input order of such image data. That is, the pixel at the upper left corner of the image is used as a starting pixel, and the octalization work is performed in order from the left end pixel to the right end pixel. The operation of octalizing toward the right end is repeated to octalize the image data for one screen.

In this case, as shown in FIG.
All the pixels above 00 and the pixels on the left side of the same line are the pixels for which multi-value conversion has already been completed. Then, all the pixels below the target pixel 400 and the pixels on the right side of the same line are pixels that have not been multi-valued.

As a method for performing the above-described pre-gradation processing, that is, octal processing, consider the case where the error diffusion weighting matrix shown in FIG. 11A is used. This weight matrix indicates that the error generated in the pixel of interest 400 in FIG. 11A is distributed at a ratio of 2 to the pixel on the right, 1 to the pixel below, and 1 to the pixel below right. is there.

FIG. 12 shows a flowchart of the octalization process (pre-gradation conversion process) performed by the pre-gradation number conversion unit 140 using this matrix using the error diffusion method.

Let us say that the pixel of interest is expressed as the p-th row and the q-th column, and the original color image data ORG of the B component of this pixel of interest is expressed.
Is represented by dataB [p] [q], and the case where this is octalized will be considered. The quantization error generated by octalization of the pixel at the p-th row and the q-th column is expressed as err [p] [q]. First step: octalization step (steps S1 to S6) First, the pixel data of interest is binarized by comparing it with a threshold value to obtain grid point color data pre_B [p] [q]. Note that this will be referred to as grid point color data pre_B below.
Abbreviated. This processing will be described briefly. If the original color image data ORG is less than the threshold value slsh [0] (step S1), the grid point color data pre_ of the target pixel
B is the smallest grid point color data pre [0] (step S4). Similarly, the original color image data dat
aB [p] [q] is between which threshold (slsh
[I] to slsh [i + 1]) is determined (step S2), and the grid point color data pre_ of the target pixel is determined.
A process of setting B as the corresponding grid point color data pre [i] is performed (step S5). If the original color image data ORG is greater than or equal to the threshold value slsh [7] (step S3), the grid point color data pre_B of the target pixel is set to the largest grid point color data pre [7] (step S6). . Note that dataB [p] [q] shown in FIG.
Is not the target pixel data itself, but is the data that has already been corrected by the error diffusion of the already octalized pixel color in the vicinity. The error diffusion method will be described in the third step.

Second step; error calculation step (step S7) Error err [p] generated in the pixel of interest due to octalization
Find [q]. Original color image data dataB [p]
[Q] does not originally exist on the grid point, and since it is assigned to the value of the grid point by the above-described octalization process, a quantization error occurs. This quantization error err [p]
The size of [q] is calculated.

Third Step: Error Diffusion Step (Step S8) The error is diffused to neighboring pixels which have not been octal-coded yet. Here, according to the weight matrix of FIG.
Half the error is the pixel on the right side of the target pixel ([p + 1]
[Q]), and 1/4 is the lower pixel ([p] [q + 1])
, 1/4 is the lower right adjacent pixel ([p + 1] [q +
1]), and the original color image data d
It is added to ata_B. The pixel-of-interest data dataB [p] [q] used in the first step described above is the data after undergoing error diffusion in this way.

Although the above is an example of octalization by the error diffusion method, a concrete example of performing this by the average error minimum method will be described below.

FIG. 13 is a flowchart of octalization processing using the minimum mean error method. First step: error correction step (step S10) In this step, the error of 8
The pixel data of interest is corrected by the digitization error. The error diffusion matrix is the same as that shown in FIG. That is, when viewed from the pixel of interest, 1/2 is added to the quantization error that occurred in the pixel to the left of the pixel, and 1/4 thereof was added to the quantization error that occurred in the pixel immediately above, and the pixel diagonally to the left 1/4 of the quantization error generated in step 1 is added.

Second step; octalization step (step S
11 to S16) Since this process is the same as steps S1 to S6 shown in FIG. 12, description thereof will be omitted.

Third step; error calculation (step S1
7) In this step, the error e generated in the target pixel due to the octalization
Find rr [p] [q]. The details are exactly the same as step S7 shown in FIG.

The difference between the error diffusion method shown in FIG. 12 and the minimum average error method shown in FIG. 13 is whether the error diffusion operation is performed immediately after the error calculation is completed or immediately before the pixel of interest is N-valued. However, both are equivalent except for the handling at the image edge.

Regarding the weight matrix of the error diffusion method, in addition to the example of FIG. 11 (a), the example of FIG. 11 (b) in which the matrix size is made larger, and FIG.
It is possible to adopt various types as required, such as the examples of 1 (c) and (d). FIG. 11D is the most simplified example, and the error diffusion target is only one pixel on the right side.

As an example of the multi-valued error diffusion method, Japanese Patent Laid-Open No.
18177, JP-A-3-34767, JP-A-3-80767, JP-A-3-147480, etc., and various techniques can be adopted as necessary.

[Embodiment of pre-gradation number conversion unit 140 using systematic dither method] The pre-gradation number conversion unit 140 is configured to use the error diffusion method or the average error minimum method, as well as the systematic dither method. It is also possible to use the gradation number conversion method of. FIG. 14 shows an example of processing when the systematic dither method is used as the pre-gradation number conversion unit 140.

Here, B of the original color image data ORG is used.
When the component has a value of 64 gradations from 0 to 63, the pre-gradation number converting unit 140 using the systematic dither method is used.
It is assumed that 17 gradations are obtained by. A dither matrix of 2 × 2 size is used. After adding systematic dither noise that changes in a two-pixel cycle in both vertical and horizontal directions to the data (step S20), by comparing with the threshold value (steps S21 to S23), 0, 1, 2, ...
The number of gradations is converted into a value of 7 gradations (S24 to S26).

That is, 16 threshold values slsh_B
[I] is set as slsh_B [i] = (i + 1) × 4-2 (i = 0, 1, ..., 15), and then the systematic dither noise (which is uniquely determined by the pixel positions p and q ( dither_noise [p% 2] [q% 2]) is added to the target pixel data data_B [p] [q] (step S20). % Is a surplus operator, and p% 2 is "p is 2
It means "a remainder when divided by", and becomes 0 if q is an even number and 1 if q is an odd number. dither_noise [p%
The value of 2] [q% 2] is, for example, dither_noise [0] [0] = 1 dither_noise [0] [1] = − 1 dither_noise [1] [0] = − 2 dither_size [1] [1] = Set it as 0. After that, the data is set to the threshold value slsh.
_B [i] is compared (steps S21 to S23), and the blue component preB of the grid point color data is obtained according to the size, and 17 gradations are obtained (S24 to S26).

In comparison with the example of 8-gradation by the average error minimum method shown in FIG. 13, the error diffused from the surrounding binarized pixels is used as the target pixel data dat in the average error minimum method.
In addition to a_B [p] [q], when the systematic dither method is adopted, periodic noise dither_noise [p% 2] [q is uniquely determined by the position of the pixel of interest.
% 2] is added. Accordingly, the method shown in FIG. 14 does not require the process corresponding to the error calculation step (step S17) performed by the minimum average error method. Comparing the case of using the systematic dither method as shown in FIG. 14 with the average error minimum method or the error diffusion method, the error diffusion calculation is not required, and therefore the speed is further increased and the error memory is stored. There is a great merit that a memory etc. is unnecessary and hardware resources are saved.

On the other hand, in the error diffusion method, the mechanism for minimizing the error in the case of taking the "regional average value" does not work. Therefore, if the pre-gradation number conversion means reduces the number of gradations too much, the image quality may deteriorate. However, as shown in FIG. 14, the number of gradations of the original image data is about 1/4. 2 × if reduced to
Even with a small matrix size of 2, pseudo contours etc. will not occur, and deterioration of resolution will be suppressed to a minimum,
Great advantage.

Generally, processing speed, required memory capacity,
An optimum pre-gradation number conversion method may be adopted in consideration of image quality. For example, the pre-gradation number conversion method to be adopted can be determined from the following viewpoints. -If the resolution of the final output device is sufficiently high and a slight decrease in resolution during pre-gradation number conversion does not pose a problem,
Larger matrix size systematic dither method ・ If the original image data does not have a large number of gradations and there is no need to significantly reduce the number of gradations by pre-gradation number conversion, a smaller matrix size organization Dither method ・ When the image quality is the highest priority, or when it is desired to reduce the number of gradations significantly by pre-gradation number conversion to reduce the capacity of the color correction table, the error diffusion method, of course, other than the error diffusion method and the systematic dither method The gradation number conversion method may be used.

Further, only the B component is systematic dither method, R, G
The components may be configured to perform pre-gradation number conversion by the error diffusion method. In general, the resolution of the human eye for the B component is lower than that for R and G, and such a configuration is also effective.

The case where the B component included in the original color image data ORG is converted into 8 gradations by using the pre-gradation number conversion unit 140 has been described above. The color components of, that is, the R component and the G component are also converted into 16 gradations by the same method.

As a result, each of the R, G, and B components of the original color image data ORG input to the pre-gradation number conversion unit 140 has the grid point color image data GCD as shown in the following equation.
Is converted into the number of pre-gradations. The R component is pre_R [0], pre_R [1] ... pr
The 16-value G component of e_R [15] is pre_G [0], pre_G [1] ... pr
The 16-value B component of e_G [15] is pre_B [0], pre_B [1] ... pr
Eight values of e_G [7].

[Specific Example of Color Corrector 142] Color Corrector 14
2 performs color correction processing on the multi-valued (pre-gradation processing) grid point color data, and also performs conversion work of the color system from R, G, B to C, M, Y. That is,
In the example shown in FIG. 7, the color correction unit 142 only performs the color correction between RGB for convenience of description, but in reality, the color correction unit 142 performs R correction in addition to color correction. , G,
The conversion of the color system from B to C, M, Y is also performed at the same time.

Hereinafter, two cases, that is, the case where the color correction unit 142 and the color correction table memory 134 are formed by software and the case where they are formed by hardware, will be described as an example.

FIG. 15A shows the color correction table memory 1
34 is an embodiment in which 34 is realized by software using the notation of C language. The color correction table for each of the C, M, and Y color components is a three-dimensional array C_table [Nr].
[Ng] [Nb], M_table [Nr] [Ng]
[Nb], Y_table [Nr] [Ng] [Nb]. Here, Nr, Ng, and Nb represent the number of gradations of each color (see FIG. 7). In this example, the size of each table is
It is 16 × 16 × 8. Since these tables are unsigned char type arrays that handle only positive integers with 8 bits, the color correction result is 8 bits, 0
Data in the range of up to 255 can be stored. The conversion data from RGB to CMY is stored in advance in this three-dimensional array.

FIG. 15B refers to the three-dimensional color correction table of FIG. 14A, and R, G, B grid point color data pre_R [i], pre_G [j], pre_B.
Let [k] be C corresponding to the ink amount in the printer 22.
It is an embodiment of the color correction unit 142 for converting to MY value. C
In this method using a language, the C, M, Y values after color correction can be obtained simply by referring to the array declared in FIG. 15A based on the grid point color data.

Next, as an example in which the color correction table memory 134 is realized by hardware, an example in which the color correction table CT is stored in a semiconductor memory is shown in FIG.
ROM for C134C, ROM for M134M, ROM for Y
Reference numeral 134Y is a ROM in which the color correction results of the C, M, and Y color components are respectively stored, and R is used as address data.
If a value determined according to the GB grid point color data is given, the corresponding corrected cyan data, magenta data,
Yellow data is output.

FIG. 17 is a more detailed embodiment of the ROM 134C for C of FIG. 15, in which the address bus has 11 bits of A0 to A10 and the data bus has 8 bits of RO of D0 to D7.
This is an example using M. Grid point color data pre_R
Corresponding to [i], pre_G [j], and pre_B [k], i, j, and k values are converted into binary numbers, and lower 4 bits (A0 to A3) and middle 4 of the address bus are respectively obtained.
Bits (A4 to A7), upper 3 bits (A8 to A10)
Has been given to. Each bit number corresponds to each RGB gradation number (16 gradations for R and G, and 8 gradations for B).

That is, in the case of this embodiment, 0 ≦ i ≦ 15,
Since 0 ≦ j ≦ 15 and 0 ≦ k ≦ 7, 4 bits for i,
It is sufficient to allocate 4 bits for j and 3 bits for k. From the data bus, the corresponding cyan color correction data is output to the data bus as an 8-bit value having a value between 0 and 255.

In the configuration shown in FIG. 16, three separate ROMs are prepared for each CMY component, but if the number of bits of the address bus is increased and a color selection signal is added thereto, a larger capacity can be obtained. It is possible to use only one large ROM. Also, it is not a ROM but a writable RA
If M is used, the contents of the table can be freely rewritten.

In the configuration shown in FIGS. 15 and 16 above,
Color correction is performed by the color correction unit 142 referring to the three-dimensional table memory 134 in terms of software or hardware.

[Specific Example of Post-Gradation Number Conversion] After color correction, the post-gradation number conversion unit 146 (halftone module 99) finally performs color correction in the color correction unit 142.
Y Each data is binarized by the error diffusion method or the minimum average error method. For this part, the existing method may be applied as it is. The binarization process by the error diffusion method is
This is almost the same as the multi-value conversion process in the pre-gradation number conversion unit 140 shown in FIG. 12, except that octalization is changed to binarization. The octalization in steps S1 to S6 in FIG. 9 is changed to binarization, and data_B [p] [q] is changed to data_C [p].
It may be replaced with [q] and binarized as shown in FIG. Here, if the cyan data is binarized to the value 255, cyan dots are present, and if it is binarized to the value 0, no dots are set. Subsequent error calculation and error diffusion processes are the same as those in steps S7 and S8 of FIG. 12, so description thereof will be omitted.

However, if the size of the error diffusion weight matrix is made too small in this binarization process, a unique dot pattern that leads to image quality deterioration is likely to occur. For this reason, it is better to use a matrix having a slightly larger size (for example, the one shown in FIG. 11B) than that used for the multi-value conversion in the pre-gradation conversion. Further, instead of the error diffusion method, a method in which the multi-value quantization by the average error minimum method of the embodiment of FIG. 13 is changed to the binarization may be used.

One of the major features of the configuration described above is that the size of the error diffusion weight matrix used in the multi-value quantization in the pre-gradation number conversion unit 140 is as shown in FIG.
Even in the case of a very small size as shown in (d), a sufficiently high image quality can be obtained. Normally, when the binarization processing is performed, as shown in FIGS. 11C and 11D, when the error diffusion is performed using a small matrix such that the error diffusion target is 2 pixels or less adjacent to each other, the dots become linear. The pattern peculiar to error diffusion, which appears in a row, becomes conspicuous and causes deterioration in image quality. However, the pre-gradation number conversion unit 14 of this embodiment is
In the case where the number of gradations to be converted is 8 or more, as in 0, even if the error diffusion matrix having a small size shown in FIGS. 11C and 11D is used, the image quality is not so deteriorated.
Therefore, the pre-gradation number conversion unit 140 can use a matrix having a smaller size than the error diffusion matrix used in the post-gradation number conversion unit 146 in the subsequent stage.
Most of the calculation amount in the error diffusion processing is shown in FIG.
This is an error diffusion process like step S8 of 2 and the amount of calculation is almost proportional to the size of the error diffusion weight matrix. Therefore, the multi-value conversion process in the pre-gradation number conversion unit 140 can be performed with an extremely small amount of calculation. For example, the amount of calculation in the pre-gradation number conversion unit 140 is extremely small as compared with the binarization processing in the post-gradation number conversion unit 146. Further, the color correction by the color correction unit 142 having the above configuration is
Since the contents of the color correction table memory 134 are simply referred to, the pre-gradation number conversion unit 140 and the color correction unit 1
The total data processing at 42 can be done very fast.

Further, in the above configuration, the color correction section 142 is
The color correction table memory 134 shown in FIGS. 15 and 16 was used to perform color correction and conversion from RGB to CMY at the same time. However, for a printer that also uses black ink K in addition to the three colors of CMY, CMYK 4 It is also possible to perform conversion into color components. For example, when the color correction table realized by the software shown in FIG. 15A is expanded to the CMYK four-color table, as shown in FIG.
A table for four colors of YK should be prepared. In this way, by increasing the number of color correction tables to be prepared, it is possible to easily deal with the case where the number of color components necessary for color correction increases.

Further, in the above description, the case where the original color image data ORG is composed of three color components of RGB has been described, but the original color image data ORG may be, for example, CMY or C.
Any color system such as L * a * b * or XYZ of IE may be used, and as shown in FIG. 9, color image data is represented by another color system in the color correction unit 142. You may make it convert into.

[D] First to Third Embodiments Each embodiment of the present invention will be described based on the configuration of the image processing apparatus 30 described in detail above. (1) First Embodiment In the above description of the image processing apparatus 30, the color space is as shown in FIG. 6 for the sake of simplicity.
It is assumed that a color correction table that is divided at equal intervals and that has data for each grid point 300 is prepared. In the first embodiment, the color space is divided finely in the low density area of the used ink CMY of the printer 22 which is the final output device. As described above, if the number of gradations after conversion of the pre-gradation number conversion is sufficiently larger than the final number of gradations of the post-gradation number conversion, the quantization noise of the pre-gradation number conversion is sufficiently small. Moreover, if the error diffusion or the minimum average error method is adopted, the local average value becomes 0, so that the practical effect of the quantization noise on the entire output image is small. However, since the quantization noise of the pre-gradation number conversion is present even if it is minute, it appears as a disorder of the appearance position of the on-dot in the output image. Therefore, strictly speaking, the effect of this quantization noise is not a substantial problem in the high density and medium density areas where the on-dot density is high, but it is considered to be the cause of the image quality deterioration in the low density area where the dot density is low. It can be said that Therefore, in order to solve this problem, in the present embodiment, as shown in FIG. 20, a color correction table CT having grid points 500 whose intervals are narrow in the low density region is used. Correspondingly, in the pre-gradation number conversion, the original image data is converted into the gradation value (grid point color data) of this grid point 500.

As a specific example, the original image data is 0-2.
When taking gradation values up to 55, the gradation values are converted into 0, 16, 32, 4 for each color of R, G, B by pre gradation value conversion.
8, 80, 96, 112, 128, 144, 160, 1
76, 192, 208, 224, 240, 248, 25
5 is quantized into 18 gradation levels. In this example, the quantization step, that is, the interval between adjacent grid points is basically 1
However, in the vicinity of the gradation value 255, that is, in the low density region, the quantization step is as small as 8 or 7. In the first embodiment, the grid points are used in FIG.
The pre-gradation number conversion was performed by the error diffusion method shown in 2.

As described above, the point that the quantization step is reduced in the low density region is a major feature of this embodiment. In the conventional interpolation calculation method, when the printer 22 such as an ink jet printer is used as the output device, it is appropriate that the grid point spacing in the low density region is large and the grid point spacing in the middle and high density regions is narrow. . This is because the probability of dots coming into contact with each other or overlapping with each other in the low density region is low, line formation between input and output data is high, and sufficient accuracy can be obtained even in the first-order interpolation calculation. This is because the linearity is greatly disturbed in the medium and high density regions where the dot density is high. Contrary to this conventional wisdom, the present embodiment uses a color correction table CT in which the grid point intervals in the low-density area are narrowed and is set so that the amount of change in output data is small in the low-density area. There is. As a result, since the quantization noise of the pre-gradation number conversion in the low density area is reduced, the image quality deterioration due to the quantization noise in the low density area is reduced, and the image quality of the output image is further improved. Moreover, since the grid point interval is reduced only in the low density area, the color correction table memory 13
The capacity of 4 does not increase so much.

By the way, the data after the pre-gradation number conversion is next color-corrected according to the color correction table CT. In general, since the conversion characteristic of the color correction table CT is not linear, the quantization step of the pre-gradation number conversion has a different shape after color correction. Depending on the conversion characteristics of the color correction table CT,
Even if the quantization step of pre-gradation number conversion is sufficiently small in the low density area, the quantization step of the data after color correction is
For each ink color, the quantization step may not be small in the low density region. In this case, the color correction table CT is divided into RGs as shown in FIG.
Even if finely divided in the low-density area on each axis of B, the effect of quantization noise appears in the low-density area in the finally obtained image.

Therefore, not only the quantization step of the pre-gradation number conversion in the low density area is sufficiently small, but also the quantization step of the data after color correction thereof is sufficiently small in the low density area. It is desirable to modify the quantization step of pre-tone conversion. This correction is made, for example, in FIG.
As shown in, it can be performed by the following steps. First, the quantization step of the pre-gradation number conversion is tentatively set so as to be sufficiently small in the low density area as described above (step S31), and the color correction table CT is correspondingly set.
After creating, actually try color correction (step S
32). The quantization step of the color-corrected data obtained as a result of this trial is checked (step S33). If the quantization step is greater than or equal to a predetermined upper limit value for eliminating the influence of the quantization error in the low density region in any part of the low density region (step S34), if it is larger than the predetermined upper limit value. Of the quantization steps of the pre-gradation number conversion, the portion related to the above-mentioned large portion is set to be smaller (step S35), and the grid point interval of the color correction table is also corrected accordingly (step S36).

In this way, finally, the quantization step of the pre-gradation number conversion and the corresponding color correction table CT are set so that the quantization step after color correction becomes smaller than the upper limit value in the low density area. Correct the grid point spacing. The quantization step and color correction table CT thus modified
By performing the pre-gradation number conversion and the color correction by using, the influence of the quantization noise of the pre-gradation number conversion in the low density region can be surely reduced.

(2) Second Embodiment A second embodiment of the present invention will be described in which the pre-gradation number conversion section 140 performs the gradation number conversion by the dither method in the same configuration as the first embodiment. To do. In this example,
The conversion of the number of pre-gradations is performed by the process shown in FIG. In this embodiment, a 4 × 4 dither matrix is used. FIG. 23 shows an example of the dither matrix. In this example, original color image data Da (0 to 0) of N gradations (256 gradations in this embodiment) for each color of RGB is used.
255) is converted into pre-gradation number into M gradation (6 gradations in this embodiment). The grid point color image data obtained by the pre-gradation number conversion is represented by GC. In the explanation below,
DiTh represents a dither matrix number, and takes a value of 1 to 16 as shown in FIG. Further, the grid points prepared for the pre-gradation number conversion are narrowly divided in the low density region, and RSLT [0] = 0, RSLT [1] = 70, RLT
SLT [2] = 130, RSLT [3] = 190, RS
LT [4] = 235 and RSLT [5] = 255 are set. In addition, the distance Dist [i] between each lattice point is defined as Dist [i] = RSLT [i + 1] −RSLT [i] i = 0, 1 ,. The original color image data Da has a value of 255.
In order to guarantee the result of the calculation for obtaining the offset offst described later when is taken, Dist [5] = 1 is defined.

When the pre-gradation number conversion shown in FIG. 22 is started, the original color image data Da of the pixel of interest is first obtained.
Is input (step S40). afterwards,
A process for obtaining a value DDH obtained by normalizing the dither matrix number DiTh corresponding to this pixel in a value range of 0 to 1 is performed (step S41). The value DDH is calculated by the following equation (3).

[0147]

(Equation 3)

In the process of obtaining the dither matrix number DiTh, the value of the position [p% 4, q% 4] is set from the matrix shown in FIG. 23, where p is the position in the scanning direction of the pixel of interest and q is the position in the sub-scanning direction. It is done by asking. Here,% is a remainder operator. To obtain the dither matrix number DiTh from the matrix of FIG. 23, [0,0]
A function whose elements are [3, 3] (for example, GetMatr)
ix [x, y]) may be defined in advance.

After the value DDH obtained by normalizing the dither matrix number DiTh is obtained in this manner, the value 0 is set to the variable X indicating the converted gradation number (step S42), and then the gradation number X is determined. A process of comparing the grid point value RSLT [X] with the original color image data Da is performed (step S43). In this description, although the original color image data Da to be compared does not specify a color component,
Actually, the comparison is performed for each color component. If the original color image data Da is not less than or equal to the value RSLT [X] of the grid point, the variable X is incremented by 1 (step S44), and both are compared again. That is, the values corresponding to the grid points are successively increased until the original color image data Da becomes equal to or smaller than the grid point value RSLT [X].

As a result, the original color image data Da
Becomes equal to or smaller than the value RSLT [X] of the grid point (step S43), the next step is the original color image data Da and step S4.
A process for calculating the offset off from the grid point compared in 3 is performed (step S45). The gap offst is
The original color image data Da is the distance D between the grid points that sandwich it.
It is calculated by the following equation (4) as a value normalized to ist [X-1].

[0151]

(Equation 4)

Then, subsequently, a process of comparing the gap offst with a value DDH obtained by normalizing the dither matrix number DiTh is performed (step S46). If the two are compared and the offset is larger, the original color image data Da is assigned to a grid point value RSLT [corresponding to the variable X in order to assign the original color image data Da to a grid point having a larger value among the grid points sandwiching the original color image data Da. X] is set to the result value RSL (step S47), and if the offset offst is smaller, the variable X is assigned to assign the original color image data Da to the grid point on the smaller value side of both grid points sandwiching it. A process of setting the value RSLT [X-1] of the grid point corresponding to -1 to the result value RSL is performed (step S48). After that, a process of moving the pixel of interest to the next pixel is performed (step S4).
9), the above process is repeated until the end of the original color image data.

According to the above processing, the number of gradations of the original color image data Da can be converted from 256 gradations to 6 gradations, and moreover, by using the dispersed type dither matrix (FIG. 23), the gradation can be appropriately adjusted. It can be converted into scattered grid point color image data. In the example using FIG. 14, such variation is created by adding data by the dither matrix to the original color image data as noise, but in the present embodiment,
When deciding which one of both grid points that sandwich the original color image data Da is assigned, a variation using a dither matrix is generated. That is, in this embodiment, since the value DDH normalized using the dither matrix number DiTh prepared as the dither matrix is used to determine the distance from the adjacent grid point, for example, the adjacent pixels are separated. Even if the original color image data Da has the same value, they may be assigned to different grid points. When using the systematic dither method,
Compared with the average error minimum method and the error diffusion method, the error diffusion calculation is not required, so the time required for image processing can be further shortened, and a memory etc. for storing the error becomes unnecessary, and the hardware The great advantage is that resources are saved.

(3) Third Embodiment As shown in FIG. 24, the image processing apparatus 30 according to the third embodiment has the pre-gradation number conversion unit 140 described in the first embodiment.
In FIG. 9, a main data output section 140a and a sub data output section 140b are provided, and an interpolation calculation section 148 is further provided. That is, in order to reduce the quantization noise of the pre-gradation number conversion, the image processing device 30 of the second embodiment adds the pre-gradation number conversion to the color correction table C in addition to the configuration of the first embodiment.
The configuration is such that interpolation calculation is performed in the process of color correction by T.

In the conventional method of interpolation calculation performed in the three-dimensional space of R, G, B, the coordinate values of eight vertices of a rectangular parallelepiped are used.
The point interpolation method, the 6-point interpolation method and the 4-point interpolation method in which the number of vertices to be taken into consideration for simplifying the calculation are thinned out (for example, Japanese Patent Publication No.
8-16180), and these are all 3
Dimensional interpolation method. On the other hand, in this embodiment, a one-dimensional interpolation method is used. That is, in this embodiment, R, G, B
Among the three color data of (3), only the input color component (hereinafter, referred to as the main color) that has the greatest influence on the desired output color is interpolated using the quantized values before and after. For example, when converting RGB input data to CMY output data, the main color of C is R and the main color of M is G and Y.
As for the main color of B, each complementary color component is used as a main color, and interpolation is performed using the preceding and following quantized values only for that color component. This is because the main color component has a large effect on the output result of each output color component, and the output result does not change much even if the values of the color components other than the main color change slightly. Is used to obtain sufficient accuracy by interpolation calculation only for the main color components.

A specific description will be given. Here, the original image data is represented by (R, G, B), and the data after the pre-gradation number conversion is represented by (R ', G', B '). In addition, the data (R ',
G ', B'), the C, M, Y color components of the data obtained by applying the color correction table CT to LUT_C (R ',
G ', B'), LUT_M (R ', G', B '), LU
It is represented by T_Y (R ', G', B '). The data after the final color correction conversion is represented by (C ″, M ″, Y ″).

Now, the original image data (R, G,
As shown in FIG. 25, B) is for the grid points of the pre-gradation number conversion, R'1 <R <R'2, G'1 <G <G'2, B'1 <B <B'2. It is assumed that the data (R'1, G'2, B'1) is obtained as a result of pre-gradation number conversion of this original data (R, G, B) (hereinafter, this data will be the main Data).

In this case, in the pre-gradation number conversion, in addition to the main data (R'1, G'2, B'1), only the main data and the main color components are on the opposite side of the original data. The data on the grid points is output as sub data. The main data is common to each color component, but the sub data is different for each color component. Next, the color correction table is applied to both the main data and the sub data for each color component, and two values necessary for one-dimensional interpolation are acquired as follows. LUT_C (R'1, G ', where R is the main color of C
2, B'1) and LUT_C (R'2, G'2, B'1) G is the main color of M, and LUT_M (R'1, G '
2, B'1) and LUT_M (R'1, G'1, B'1) LUT_Y (R'1, G'with B as the main color of Y
2, B'1) and LUT_Y (R'1, G'2, B'2) In this way, in addition to applying the color correction table to the main data (R'1, G'2, B'1), For each color, the color correction table CT is applied to the data in which only the main color component is changed to that of the sub data, and two values required for one-dimensional interpolation are acquired.

Next, as the final stage of color correction, one-dimensional interpolation calculation is performed as follows to obtain the final data (C ″,
M ", Y").

C "is LUT_C (R'1, G'2, B'1)
And LUT_C (R'2, G'2, B'1) interpolation calculation result,
M ″ is LUT_M (R′1, G′2, B′1) and LUT_M
Interpolation result of (R'1, G'1, B'1), Y "is LUT
_Y (R'1, G'2, B'1) and LUT_Y (R'1, G'2,
B'2) is an interpolation calculation result. Here, the interpolation calculation of each color component is performed using a weighting coefficient according to the distance between the original data and the main data and the sub data for each color component. For example, regarding the R (C) component, if the original data is separated from the main data and the sub data by the distances d1 and d2 as shown in FIG. 26, C ″ is given by the following equation (5).
Required by.

[0161]

(Equation 5)

The one-dimensional interpolation only for the main colors as described above is simple in calculation, and the quantization noise of the pre-gradation number conversion can be effectively reduced. The interpolation calculation may be performed only in the low density area.

[E] Details of Image Processing Common to Fourth to Eighth Embodiments In the first to third embodiments described above, the color correction table C is used.
When T is created, grid points obtained by finely dividing the color space in the low density area are used. In contrast, the third
In the following examples, the color correction table CT is created by dividing the color space at equal intervals, as shown in FIG. In the following embodiment, instead of the configuration in which the color space is finely divided in the low density area, a configuration including a smoothing processing unit 150 is adopted as shown in FIG. Hereinafter, the image processing common to each embodiment will be described focusing on the configuration of the smoothing processing unit 150.

In the image processing apparatus 30A described below,
In order to reduce the quantization noise in the pre-gradation number conversion, smoothing processing is performed after the pre-gradation number conversion and color correction.
In this process, each pixel in the image after color correction is sequentially focused on in the raster scan order, and a smoothing filter having a weighting coefficient is applied to the data of the pixel of interest and the data of some neighboring pixels A weighted average of the data is obtained, and the weighted average value is set as new data of the pixel of interest. This is repeated for all pixels.

FIG. 28 shows some examples of smoothing filters. FIG. 3A shows an example of a one-dimensional three-pixel smoothing filter, and the weighting coefficient of that pixel is shown in each pixel. As shown in the figure, a smoothing filter having a uniform weighting factor of 1/3 is applied to the pixel of interest Pi and two adjacent pixels Pi-1 and Pi + 1 on the same line. That is, these three pixels Pi-1, Pi,
The data of Pi + 1 is (Ci-1, Mi-1, Yi-1),
Assuming that (Ci, Mi, Yi) and (Ci + 1, Mi + 1, Yi + 1), the final data (C, M, Y) of the pixel of interest Pi is C = (Ci-1 + Ci + Ci + 1) / 3 M = (Mi-1 + Mi + Ci + 1) / 3 Y = (Yi-1 + Yi + Yi + 1) / 3 The weighting factors may be unequal, such that the weighted pixel is heavy and the adjacent pixels are lightly weighted. 28B to 28D show an example of a two-dimensional smoothing filter that refers to pixel data of adjacent lines. Fig. (B)
Refers to 6 pixels including the line immediately preceding the line to which the pixel of interest Pi belongs, and shows an example having uniform weighting factors. FIG. 6C refers to 9 pixels including the upper and lower lines, and also shows an example of a filter having a uniform weight coefficient. Further, FIG. 6D shows an example of a filter in which only the weighting factors are made unequal from FIG.

By performing such smoothing processing, it is possible to solve the problem that the image quality of the final output image is deteriorated due to the influence of the quantization error caused by the pre-gradation number conversion. Also, in post-gradation number conversion,
When the conventional distributed dither method is used, the effect of the quantization error in the pre-gradation number conversion appears and the image quality may deteriorate, but by performing smoothing before the post-gradation number conversion, Even if the distributed dither is used for the gradation number conversion, the image quality is not deteriorated. Therefore,
In addition to the error diffusion method, the minimum average error method, and the centralized dither, the distributed dither can be adopted as the post-gradation number conversion.

By the way, the smoothing process reduces the quantization noise of the pre-gradation number conversion in the region where the gradation is uniform or continuous, but on the other hand, there is a problem that the clarity of the edges of characters and figures is lost. is there. Therefore, it is desirable to use edge detection together and modify the smoothing processing of the pixels of the detected edge so as not to impair the clarity of the edge. For example, the difference between the value of the target pixel and the value of the adjacent pixel is calculated, and if this difference is larger than a predetermined value, it is determined as an edge, and in the smoothing process for the value of the target pixel, the pixel adjacent to the target pixel is not referred to. To do.

Such processing will be described in detail in the embodiment described later, but the outline thereof is as follows. In the case of the one-dimensional three-pixel smoothing shown in FIG. 28A, a predetermined value which is a criterion for determining whether an edge is A is set.
Then, the processing of the following expression (6) may be performed on the value of the C color component. Here, Ci is the value of the C color component of the target pixel i, and Ci-1 and Ci + 1 are the values of the C color component of the pixels adjacent before and after the target pixel.

[0169]

(Equation 6)

Similar processing is performed for M and Y color component values. As a result, regarding the pixel of the edge portion where the absolute value of the gradation difference from the adjacent pixel is the predetermined value A or more, since the adjacent pixel is not referred to in the smoothing process, the image quality of the image of the area other than the edge Is improved by smoothing, and the clarity of the edge is not lost.

[F] Fourth to Seventh Embodiments (1) Fourth Embodiment In the image processing apparatus 30A of the fourth embodiment, the pre-gradation number conversion unit 140, the color correction unit 142, and the smoothing processing unit 150 are used.
Is realized by the processing shown in FIG. The smoothing processing unit 150 is included in the color correction module 98 in the configuration shown in FIG. In this embodiment, the position of the pixel of interest in the main scanning direction is h, and the position in the sub scanning direction is v.
As shown. Further, in the following description, each color component of the original color image data ORG before the gradation number conversion by the pre-gradation number conversion unit 140 is represented by Rs [h, v], Gs [h,
v], Bs [h, v], and each color component of the grid point color image data GCD after the pre-gradation number conversion is Rn.
These are represented as [h, v], Gn [h, v], and Bn [h, v]. By the pre-gradation number conversion, the number of gradations is reduced to 16 or 8 in each color in this embodiment.
It can be seen that the data Rn [h, v] and the like indicate which grid point is assigned. In the fourth embodiment, the color correction table CT is a table including conversion from RGB to four colors of CMYK, and each color component of this color correction data GCD is represented by Cc [h, v], M.
It is described as c [h, v], Yc [h, v], Kc [h, v]. Note that in FIG. 29, [h, v] may be omitted for convenience of illustration.

When the processing shown in FIG. 29 is started, the pre-gradation number conversion unit 140 first performs the pre-gradation number conversion (step S50). The pre-gradation number conversion is performed for each color component Rs [h, v], Gs corresponding to the original color image data ORG.
[H, v] and Bs [h, v] are assigned to grid points prepared in advance and the number of gradations is reduced. The method has been described in detail in the first to third embodiments.
In FIG. 29, the pre-gradation number conversion is performed by the function PreConv ().
As shown. Each color component of the grid point color image data GCD obtained by this pre-gradation number conversion is Rn [h,
v], Gn [h, v], and Bn [h, v].

Next, based on each color component of the grid point color image data obtained by the pre-gradation number conversion, the color correction table CT stored in advance in the color correction table memory 134.
And color correction is performed (step S51). This processing corresponds to the processing by the color correction unit 142. In accordance with the color correction, conversion from RGB to four colors of CMYK, which is the ink color that is finally output from the color printer 22, is also performed. Color correction data CCD obtained by color correction processing
Each color component of Cc [h, v], Mc [h, v], Yc
[H, v] and Kc [h, v]. In FIG. 29, since the color correction processing is referred to (looked up) in the color correction table CT, the function RefLUT ()
As shown.

After each color component of the color correction data CCD is obtained in this way, smoothing processing is performed by the smoothing processing unit 150 of this embodiment. In this smoothing process,
First, it is determined whether or not the smoothing process is performed between the pixel [h, v] of interest and the pixels adjacent thereto (step S52). As described above, various methods are conceivable as to when to perform the smoothing process. However, in this embodiment, as shown in FIG. Data Rn [h, v], Gn [h, v], Bn [h, v]
And the difference between each pixel and the data of the immediately preceding pixel [h−1, v] adjacent thereto is less than or equal to 1 for each color component,
It is determined that smoothing processing will be performed. That is, in the case where each color component of the pixel of interest and each color component of the pixel immediately preceding in the main scanning direction are on the same or adjacent grid points by the conversion by the pre-gradation number conversion unit 140. , It is determined that the smoothing process is to be performed. Based on this determination, it is determined that the smoothing process is not performed on the edge or the like that is inherent in the image.

When it is determined that the smoothing process is not performed, the color-corrected color components Cc of the target pixel are
[H, v], Mc [h, v], Yc [h, v], Kc
The output data Cs, Ms, Ys, and K are directly used as [h, v].
s (step S53), the post gradation number conversion unit 146
Output to On the other hand, when it is determined that the smoothing process is performed, each color component Cc after the color correction of the immediately preceding pixel is performed.
[H-1, v], Mc [h-1, v], Yc [h-1,
v], Kc [h−1, v] and the respective color components of the pixel of interest are calculated (step S54), and this is used as output data, and the post-gradation-number conversion unit 146 performs post-gradation-number conversion. Move to. In the present embodiment, the post-gradation number conversion process is a binary process in which the output of the color printer 22 forms or does not form ink dots.
Performs half-toning processing such as error diffusion. A description of this process is omitted.

In the image processing apparatus 30A of the fourth embodiment configured as described above, smoothing is performed by the smoothing processing section 150, so that the influence of the quantization error caused by the pre-gradation number conversion can be reduced and the quantization error can be reduced. It is possible to prevent the deterioration of the image quality due to. Moreover, based on the data after the conversion of the pre-gradation number, it is compared with the pixel immediately before the pixel of interest, and even if one of the respective color components is assigned to a grid point which is more than adjacent grid points apart. , Smoothing processing is not performed. As a result, the sharpness such as the edge originally present in the image is not lost by the smoothing process. Further, since the determination as to whether or not to perform the smoothing is made by comparing with the data of the pixel processed immediately before, there is no waste in the data stored for comparison, and the storage capacity can be small. be able to. Further, since it is only necessary to compare with one adjacent pixel, the amount of calculation can be reduced and the overall processing speed can be increased.

In the fourth embodiment described above, whether or not smoothing is performed is determined by the separation of each color component of the data after the conversion of the pre-gradation number.
You can think of various variations. For example,
As shown in FIG. 31, the data Rn after the pre-gradation number conversion is performed.
Data Rn0 [h, v] indicating the range to which the original color image data from which the determination is made belongs instead of the determination based on [h, v], Gn [h, v], Bn [h, v]. , G
The determination may be performed using n0 [h, v] and Bn0 [h, v]. At this time, the data Rn0 [h, v], Gn0 [h,
v] and Bn0 [h, v] indicate which one of the rectangular parallelepiped surrounded by the eight adjacent grid points the pixel of interest belongs to, the condition shown in FIG. This is equivalent to determining whether the existing pixel and the pixel adjacent thereto (the pixel immediately before in the main scanning direction) belong to the same rectangular parallelepiped surrounded by eight grid points or a rectangular parallelepiped adjacent thereto. Under the conditions shown in FIG. 30, if the grid points assigned by the pre-gradation number conversion for each color component are the same or adjacent to each other, the smoothing processing is performed regardless of the relationship as the original color image data. However, in the example of FIG. 31, it is a condition that the original color image data is in the same space surrounded by adjacent grid points or the space adjacent to this.

Further, as shown in FIG. 32, when the original color image data of the pixel of interest and the pixel adjacent thereto belong to the same rectangular parallelepiped space surrounded by adjacent grid points without considering the adjacent range. It is also possible to determine that smoothing should be performed only on.

Further, in these examples, the distance between adjacent pixels is set to a value of 1 or less, but the condition for performing smoothing may be relaxed to perform smoothing even if the distance is 2 or more. Since the number of gradations after the conversion of the number of pre-gradations differs for each color component, the value of 2 or more can be set only for the judgment of a specific color. Alternatively, the following equation (7)
As shown in the above, the judgment may be made based on the sum of the distances between the color components.

[0180]

(Equation 7)

In this case, even if a specific color is assigned to positions separated from adjacent grid points, smoothing will be performed if other colors are assigned to the same grid point. . Alternatively, it is also possible to consider the condition that the sum of squares of separation of each color component is equal to or less than a predetermined value, as in the following Expression (8).

[0182]

(Equation 8)

Note that the original color image data Rs [h, v], Gs [h, v], Bs [h, not shown in FIG.
It is also possible to make a judgment by using v]. In the example,
Since the original color image data is data of 256 gradations of 0 to 255, in FIG. 33, it is determined that smoothing is performed when the distance between each color component is 16 gradations or less between adjacent pixels. And The separation for each color component can be freely increased or decreased from 16 gradations. It is desirable that this distance, which is a criterion for judgment, be determined by actually performing processing on a predetermined image and verifying it. It is also desirable to use it in combination with the above-described judgment on the data after the conversion of the number of pre-gradations.

The effect of the quantization error caused by the conversion of the number of pre-gradations is most strongly felt especially in the color printer 22 in the low density area of the image finally obtained, that is, the highlight area. Therefore, it is also practical to perform smoothing only near the highlight. FIG. 34 shows an example in which it is determined that smoothing is performed only in the highlight area. In this case, since it is determined that the original color image data is in the low density area, the pixel of interest [h,
v] and its adjacent pixel [h−1, v] of the original color image data Rs, Gs, Bs are both 224 gradations or more of 0 to 255 gradations, smoothing processing is performed. . It should be noted that in this example, the value of 224 gradations used as the determination threshold value can be variously changed.
The appropriate value can be determined by processing various images.

(2) Fifth Embodiment Next, a fifth embodiment will be described. In the image processing apparatus 30A of the fifth embodiment, the pre-gradation number conversion unit 140, the color correction unit 142, and the smoothing processing unit 150 are realized by the processing shown in FIG. In this embodiment, the smoothing process is performed not only between pixels adjacent in the main scanning direction but also between pixels adjacent in the sub scanning direction. When the processing shown in FIG. 35 is started, first, the pre-gradation number conversion section 140 (step S60) and the color correction section 142 color correction (step S head 61) described in FIG. 29 are performed. Then, it is determined whether or not smoothing is performed in the main scanning direction (step S62). The content of this determination is as described in the fourth embodiment and its modification, and various criteria can be adopted.

It is determined whether or not smoothing is performed (step S62), and when it is determined that smoothing is not performed between pixels adjacent in the main scanning direction, color correction data Cc [h, v ], Mc [h, v],
A process of doubling Yc [h, v] and Kc [h, v] as they are and temporarily storing them as data Cs, Ms, Ys, and Ks is performed (step S63). On the other hand, when it is determined that the smoothing is performed between the adjacent pixels in the main scanning direction, the color correction data is averaged between the adjacent pixels as described in the fourth embodiment. The color correction data of the pixel of interest and the pixel adjacent thereto are added and temporarily stored as data Cs, Ms, Ys, Ks (step S64).

Then, it is determined whether or not smoothing is performed between pixels adjacent in the sub-scanning direction (step S65). As shown in FIG. 36, whether or not to perform smoothing determines whether the pixel [h, v-1] is one pixel before in the sub-scanning direction.
And the difference between the grid point color image data is less than or equal to 1 for each color component. Also for this judgment, the various judgments shown in the above-described fourth embodiment and its modification can be applied. Note that both determinations (steps S62 and S65) do not necessarily have to have the same content. When the resolution of the color printer 22 which is the final image output device is different in the vertical direction and the horizontal direction, it is also practical that the determination as to whether or not to perform smoothing is different in the main scanning direction and the sub scanning direction. is there.

When it is determined that the smoothing is not performed between the pixels adjacent in the sub-scanning direction, the color correction data Cc [h, v], Mc [h, v], Yc of the pixel of interest.
A process of adding [h, v], Kc [h, v] to the previously obtained data Cs, Ms, Ys, Ks is performed (step S66). On the other hand, when it is determined that the smoothing is performed between the pixels adjacent to each other in the sub-scanning direction, the previously obtained data Cs, Ms, and Ys are used to average the color correction data between the adjacent pixels. , Ks, the color correction data Cc [h, v-1], Mc [h, v-1], Y of the pixel on the line immediately preceding the pixel of interest (adjacent pixels in the sub-scanning direction).
A process of adding c [h, v-1] and Kc [h, v-1] is performed (step S67).

After that, the data Cs, Ms, Ys, and Ks obtained in step S66 or S67 are divided by a value of 3 to obtain an average value, and the smoothed data Cs, Ms, and
As Ys and Ks (step S68), the process shifts to the post gradation number conversion processing by the next post gradation number conversion unit 146.

According to this structure, the smoothing process can be performed not only for the pixels adjacent in the main scanning direction but also for the pixels adjacent in the sub-scanning direction, which is caused by the quantization error due to the pre-gradation number conversion. It is possible to prevent deterioration of the image quality in both the main scanning direction and the sub-scanning direction, and further improve the image quality.

(3) Sixth Embodiment Next, as a sixth embodiment of the present invention, the image output device 20 will be described.
In addition to the cyan ink C, the magenta ink M, the yellow ink Y, and the black ink K, the color printer is a light cyan ink C2 having a lower density than the cyan ink C2, a light magenta ink M2 having a lower density than the magenta ink M.
The smoothing in the case where the ink can be discharged will be described. This color printer has, in the hardware configuration shown in FIG. 2, six heads of respective colors 61 to 66 corresponding to the above six colors of ink in a carriage 31, and a black ink K in a color ink cartridge 72.
It is equipped with 5 shades of light and dark ink. This shade 5
The composition of the color inks is shown in FIG. Where cyan,
The light cyan ink C2 and the light magenta ink M2 having a low density for magenta are provided so that when these inks are ejected at a low density to express a low density area of the original color image data, the ink is discharged in that area. This is because the dots are visually recognized and the so-called granular feeling is felt to deteriorate the image quality. In such an area where the density of the original color image data is low, the image quality is greatly improved by printing using the ink of low density. The yellow ink Y does not have a low density ink because the yellow ink Y has a high lightness, and originally, a graininess hardly occurs.

For cyan and magenta, low density ink (hereinafter referred to as light ink) and normal density ink (hereinafter, referred to as light ink)
FIG. 38 shows the dot recording rate with the dark ink).
In this embodiment, in a region where the density of the original color image data is low, printing is performed mainly with the light ink, and as the density of the original color image data is increased, the usage amount of the dark ink is gradually increased. Reduce the amount of ink used.
When the density is equal to or higher than a predetermined density, printing is performed using only dark ink.

In the image processing apparatus 30A of the sixth embodiment having such a configuration, the pre-gradation number conversion section 140, the color correction section 1
42, the smoothing processing unit 150 is realized by the processing shown in FIG. When this process is started, first, each color component Rs, Gs, Bs of the original color image data is subjected to a process corresponding to the pre-gradation number conversion in the pre-gradation number conversion unit 140 (step S70), and after the conversion. The color correction table CT is referred to based on each color component Rn, Gn, Bn of the grid point color image data, and the process corresponding to the color correction in the color correction unit 142 is performed (step S71). At this time, the color correction table CT is changed from R, G, B to C, M,
It is prepared as a conversion table for 6 colors of Y, K, C2 and M2.

Next, it is determined whether or not the smoothing process is performed on the target pixel (step S72).
This processing can basically use the judgment described in the fourth embodiment and its modification. When it is determined that smoothing is not performed, the color-corrected color components Cc [h, v], Mc [h, v], Yc [h, of the target pixel are corrected.
v], Kc [h, v], C2c [h, v], M2c
The output data Cs, Ms, Ys, and K are directly used as [h, v].
s (step S73), and the post gradation number conversion unit 146
Output to On the other hand, when it is determined that the smoothing process is performed, each color component Cc after the color correction of the immediately preceding pixel is performed.
[H-1, v], Mc [h-1, v], Yc [h-1,
v], Kc [h-1, v], C2c [h-1, v], M
2c [h-1, v] and each color component Cc [h, of the pixel of interest
v], Mc [h, v], Yc [h, v], Kc [h,
v], C2c [h, v], and M2c [h, v] are calculated (step S74), and this is used as output data for the next post tone number conversion by the post tone number conversion unit 146. Transition.

According to this embodiment, in addition to the effects of the fourth embodiment, in the color printer 22 which is provided with the light cyan ink C2 and the light magenta ink M2 and which appropriately ejects the light ink and the dark ink to perform printing, Smoothing can be performed even with six color inks to prevent deterioration in image quality due to quantization errors in pre-gradation number conversion.

(4) Seventh Embodiment Next, a seventh embodiment of the present invention will be described. In the above-described embodiment, whether or not smoothing is performed, whether the data before the pre-gradation number conversion is used or the data after the pre-gradation number conversion is used, the pixel of interest and a pixel adjacent thereto (for example, It was judged by the distance between adjacent pixels in the main scanning direction or the sub scanning direction. On the other hand, in the seventh embodiment, the smoothing process is performed for each color component, and the determination as to whether or not the smoothing is performed is also performed for each color.

In the image processing apparatus 30A of the seventh embodiment, the pre-gradation number conversion section 140, the color correction section 142 and the smoothing processing section 150 are realized by the processing shown in FIG. When this process is started, first, the color components Rs, Gs, and Bs of the original color image data are converted into the pre-gradation number conversion unit 140.
Processing corresponding to the pre-gradation number conversion is performed (step S80), and the color correction table C is then calculated based on the respective color components Rn, Gn, Bn of the converted grid point color image data.
The process corresponding to the color correction in the color correction unit 142 is performed with reference to T (step S81).

Next, processing is performed to determine the distance between the pixel of interest and the adjacent pixel of the R component of the grid point color image data after the pre-gradation number conversion (step S8).
2) If the distance on the grid point is larger than the value 1, the color-corrected data Cc [h,
v] is set as the data Cs to be output as it is (step S83). On the other hand, if the distance between the R components of both pixels on the grid point is less than or equal to 1, smoothing is performed, and the cyan component Cc of the data after color correction is determined.
A process of taking an addition average of [h, v] and the cyan component Cc [h-1, v] of the color-corrected data of the pixel immediately before in the main scanning direction is performed (step S84).

In the above processing, the smoothing judgment for cyan C is made based on the judgment for the R component of the original color image data. That is, in the conversion from RGB to CMY, R and C, G and M and B are determined. This is because it has a strong correlation with Y.

Therefore, after the above processing for cyan, smoothing for magenta ink M is judged by the distance on the grid point for G component (steps S85-87), and smoothing for yellow ink Y is performed for B component. Is determined based on the distance on the grid point (steps S88 to 90). After the above processing, the process shifts to post tone number conversion.

The image processing apparatus 30A of the seventh embodiment having the above-described structure has the same effects as those of the fourth embodiment, and
Since it is possible to determine whether or not to perform smoothing for each color, it is possible to achieve both the effect of smoothing and the sharpness of the boundary by not performing this even when only a specific color tone is converted.

The preferred embodiment of the present invention has been described above. However, the present invention is not limited to this embodiment, and various modifications, improvements or modifications can be made without departing from the scope of the invention. can do.

For example, in the above fourth to seventh embodiments,
The determination of smoothing was made by paying attention to the distance between the pixel of interest and the pixel on the front side in the main scanning direction or the pixel on the front side in the sub-scanning direction of that pixel. , V] or [h, v + 1]. Further, the smoothing range may be a range other than the range shown in FIG. 28. In the above embodiment, the smoothing is judged or performed for all the color components, but the smoothing process may be performed only for a specific color. For example, since the yellow ink Y has high brightness and is hard to be visually recognized, the yellow ink Y can be excluded from the smoothing target from the beginning. In this case, the processing speed can be further increased. Further, in a printer equipped with both dark and light inks, it is also suitable to exclude light inks that are less likely to cause graininess from smoothing.

In the fourth to seventh embodiments, the details of the pre-gradation number conversion, which is a prerequisite for smoothing, have not been particularly described, but the pre-gradation number conversion by the error diffusion method or the average error minimum method is performed. It is obvious that it can be combined with the pre-gradation number conversion by the dither method described in the second embodiment. By performing the smoothing, the deterioration of the image quality due to the dither method can be sufficiently compensated, and a good image output can be obtained.

[Brief description of drawings]

FIG. 1 is a block diagram showing a schematic configuration of an image processing system of the present invention.

FIG. 2 is a block diagram showing an example of a mode for realizing the image processing system shown in FIG.

FIG. 3 is a schematic configuration diagram showing a configuration of a color printer 22 as an example of the image output device 20.

FIG. 4 is an explanatory diagram illustrating the structure of the print head 28.

FIG. 5 is an explanatory diagram illustrating the principle of ink ejection.

FIG. 6 is an explanatory diagram showing an example of a color space divided in a grid pattern.

7 is a block diagram showing the functions of the image processing apparatus 30 shown in FIG. 1 by blocks.

FIG. 8 is an explanatory diagram showing positions in the color space of color image data and neighboring grid point color data.

FIG. 9 is a block diagram showing the functions of the image processing apparatus in blocks when a color printer is assumed as the image output apparatus.

FIG. 10 is a schematic explanatory diagram of a pre-gradation number conversion process of image data.

FIG. 11 is an explanatory diagram of a weight matrix when the pre-gradation number conversion process is performed using the error diffusion method.

FIG. 12 is a flowchart showing an outline of processing when an error diffusion method is used in the pre-gradation number conversion means.

FIG. 13 is a schematic flowchart of a process when the pre-gradation number conversion means uses the minimum average error method.

FIG. 14 is a flowchart showing an outline of processing when the systematic dither method is used in the pre-gradation number converting means.

FIG. 15 is an explanatory diagram illustrating an outline of processing when the color correction unit 142 is configured by software.

FIG. 16 is a schematic configuration diagram when the color correction unit 142 is configured in hardware.

FIG. 17 is an explanatory diagram showing a more detailed embodiment of the C ROM shown in FIG. 16;

FIG. 18 is an explanatory diagram showing an outline of processing for realizing a post gradation number conversion means.

FIG. 19 is an explanatory diagram showing an outline of processing when color correction is performed by obtaining four CMYK color components from RGB three color data.

FIG. 20 is an explanatory diagram showing an example of grid points used in the image processing apparatus 30 of the first embodiment, the grid points being finely divided in the low-density area of the color space.

FIG. 21 is a flowchart illustrating an outline of processing when correcting a color correction table.

FIG. 22 is a flowchart showing a pre-gradation number conversion process in the image processing apparatus 30 of the second embodiment.

FIG. 23 is an explanatory diagram showing an example of a dither matrix referred to by the dither method adopted in the pre-gradation number conversion processing of the second embodiment.

FIG. 24 is a block diagram showing the functions of the image processing apparatus 30 of the third embodiment in blocks.

FIG. 25 is an explanatory diagram for describing one-dimensional interpolation processing performed for main colors.

FIG. 26 is an explanatory diagram illustrating the principle of primary interpolation processing.

FIG. 27 is a block diagram showing the functions of an image processing apparatus 30A common to the fourth to seventh embodiments in blocks.

FIG. 28 is an explanatory diagram showing a specific example of a smoothing filter.

FIG. 29 is a flowchart showing an image processing routine in the fourth embodiment.

FIG. 30 is an explanatory diagram showing details of the determination as to whether or not to perform smoothing.

FIG. 31 is an explanatory diagram showing another example of determination of whether or not to perform smoothing.

FIG. 32 is an explanatory diagram showing another example of determination of whether or not to perform smoothing.

FIG. 33 is an explanatory diagram showing another example of determination of whether or not to perform smoothing.

FIG. 34 is an explanatory diagram showing another example of determination of whether or not to perform smoothing.

FIG. 35 is a flowchart showing an image processing routine in the fifth embodiment.

FIG. 36 is an explanatory diagram showing details of the determination as to whether or not to perform smoothing in the fifth embodiment.

FIG. 37 is an explanatory diagram showing ink components in a printer that uses the six-color ink of the sixth embodiment.

FIG. 38 is a graph showing recording rates of light ink and dark ink in the sixth embodiment.

FIG. 39 is a flowchart showing an image processing routine in the sixth embodiment.

FIG. 40 is a flow chart showing an image processing routine in the seventh embodiment.

[Explanation of symbols]

 10 ... Image input device 12 ... Scanner 20 ... Image output device 21 ... Color display 22 ... Color printer 23 ... Paper feed motor 24 ... Carriage motor 26 ... Platen 28 ... Print head 30 ... Image processing device 30A ... Image processing device 31 ... Carriage 32 ... Operation panel 34 ... Sliding shaft 36 ... Drive belt 38 ... Pulley 39 ... Position detection sensor 40 ... Control circuit 61 ... Ink ejection head 65 ... Introducing pipe 71 ... Black ink cartridge 72 ... Color ink cartridge 80 ... Ink Passage 90 ... Computer 91 ... Video driver 93 ... CRT display 95 ... Application program 96 ... Printer driver 97 ... Rasterizer 98 ... Color correction module 99 ... Halftone module 134 ... Color correction table memory 134 ... C ROM 134M ... M ROM 134Y ... Y ROM 140 ... pre-gradation number conversion unit 140a ... main data output unit 140b ... sub-data output unit 142 ... color correction unit 146 ... post-gradation number conversion unit 148 ... interpolation Calculation unit 150 ... Smoothing processing unit 300 ... Lattice point 400 ... Pixel of interest 500 ... Lattice point

─────────────────────────────────────────────────── ─── Continuation of the front page (51) Int.Cl. ⁶ Identification code Internal reference number FI Technical indication H04N 1/405 H04N 1/40 C 1/407 101E

Claims (38)

[Claims] 1. An image processing apparatus for color-correcting and outputting a multicolor image represented by coordinate values in a two-dimensional or more-dimensional color space, wherein each pixel of the image has the coordinate value of a predetermined level. Input means for inputting color image data expressed using a key number, dividing the color space by a number of gradations smaller than the number of gradations expressing the coordinate values, and a predetermined low density of the color space. In the area, the color space is divided into smaller areas than in other areas, and the grid point information storage means stores the coordinate values of the grid points obtained by performing the division in each of the dimensions, and the grids. Corresponding to the point, a color correction table storing correction data relating to the color of the color image data, and a coordinate value of the input color image data in the color space, an average distance from the grid point. Where Following the procedure becomes the value or less, and the lattice point conversion means for converting the coordinate values of the stored grid points to the grid point information memory means, the correction data of the lattice point corresponding to the converted coordinate values,
An image processing apparatus comprising: a color correction unit that reads out from the color correction table and outputs as corrected color image data. 2. The image processing apparatus according to claim 1, wherein the grid point conversion means is means for converting the coordinates of grid points by using an error diffusion method. 3. The image processing apparatus according to claim 1, wherein the grid point conversion means is means for converting the coordinates of grid points using a threshold matrix of distributed dither. 4. The image processing apparatus according to claim 1, wherein the color image data is expressed in a format in which the number of gradations expressing the coordinate values corresponds to the density of each color component of the color image data. The color correction table stores information about the gradation of each color component of the color image data, and the grid point information storage unit stores the color image data stored in the color correction unit. Image processing, characterized in that the predetermined low-density region of the color space is divided so that the gradation interval of each color component is narrower in the predetermined low-density region than in other density regions. apparatus. 5. The image processing apparatus according to claim 4, further comprising converting the gradation number of each color component of the corrected color image data output by the color correction unit, and suitable for an image output apparatus. A gradation number conversion unit for converting the final gradation number is provided, and the gradation number obtained by the coordinate value conversion performed by the grid point conversion unit is converted by the gradation number conversion unit. An image processing device characterized by being larger. 6. The image processing apparatus according to claim 1, wherein the color correction table is a color reproduction of an image output apparatus that finally processes color image data output by the color conversion unit as the correction data. An image processing device that stores information that matches the characteristics. 7. An image processing apparatus for color-correcting and outputting a multicolor image represented by coordinate values in a two-dimensional or more-dimensional color space, wherein each pixel of the image has the coordinate value of a predetermined level. Input means for inputting color image data expressed using a key number, dividing the color space by a number of gradations smaller than the number of gradations expressing the coordinate values, and a predetermined low density of the color space. In the area, the color space is divided into smaller areas than in other areas, and the grid point information storage means stores the coordinate values of the grid points obtained by performing the division in each of the dimensions, and the grids. Corresponding to the point, a color correction table storing correction data relating to the color of the color image data, and a coordinate value of the input color image data in the color space, an average distance from the grid point. Where Following the procedure becomes the value or less, and the lattice point conversion means for converting the coordinate values of the stored grid points to the grid point information memory means, the correction data of the lattice point corresponding to the converted coordinate values,
A color correction data reading means for reading from the color correction table and a value corresponding to a main color of the read correction data of the grid points are selected, and for each main color, the grid point corresponds to the main color. Of the correction data of the adjacent grid points where only the coordinate values are shifted, the value corresponding to the main color is read, interpolation is performed using both values, and the correction data for the main color is replaced with the interpolation result. An image processing apparatus comprising: a color correction unit that outputs data as final corrected color image data. 8. The image processing apparatus according to claim 7, wherein the interpolation in the color correction means is linear interpolation. 9. The image processing apparatus according to claim 7, wherein the coordinate value of the color image data is defined as a value corresponding to the density of each color component of the color image data,
The grid point conversion means is first gradation number conversion means for converting the grid point coordinates as gradation number conversion, and further, for each color component of the corrected color image data output from the color correction means. Second gradation number converting means for converting the number of gradations of the first gradation number into a gradation number suitable for the image output device, and a floor obtained by converting the coordinate value by the first gradation number converting means. An image processing apparatus, wherein the number of tones is larger than the number of tones after being converted by the second tone number conversion means. 10. An image processing apparatus for color-correcting and outputting a multicolor image represented by coordinate values in a two-dimensional or more-dimensional color space, wherein each pixel of the image has the coordinate value of a predetermined floor. Input means for inputting color image data represented by a key number, and the color space is divided by the number of gradations smaller than the number of gradations expressing the coordinate value, and the division is performed for each dimension. Grid point information storage means for storing the coordinate values of the selected grid points for the color space; a color correction table for storing correction data relating to the color of the color image data corresponding to each grid point; The coordinate value of the color image data in the color space is set to the coordinate value of the grid point stored in the grid point information storage unit according to a method in which the distance from the grid point is less than or equal to a predetermined value on average. A grid point conversion means for conversion, the correction data of the lattice point corresponding to the converted coordinate values,
A color correction data reading means for reading from the color correction table, and an averaging process for averaging the correction data of each pixel read by the color correction data reading means on the basis of the correction data of pixels in the vicinity of each pixel. An image processing apparatus including a processing unit. 11. The image processing apparatus according to claim 10, wherein the grid point conversion means is means for converting coordinates of grid points by using a threshold matrix of distributed dither. 12. The grid point conversion means is means for converting the coordinates of grid points by using an error diffusion method.
0. The image processing apparatus according to item 0. 13. The image processing apparatus according to claim 10, wherein the grid point conversion means converts the coordinates of grid points using an error diffusion method, and a threshold value of distributed dither. A second coordinate conversion means for performing coordinate conversion of the grid points using a matrix; and a conversion means determination means for determining which of the first coordinate conversion means and the second coordinate conversion means is to be used, The averaging means is means for performing the averaging process in a region where the density of the input color image data is low, when the conversion means deciding means decides to use the first converting means. An image processing device. 14. The image processing apparatus according to claim 10, wherein the neighboring pixels on which the averaging processing means performs the averaging processing are adjacent pixels along a direction in which the input means inputs the color image data. . 15. The image processing apparatus according to claim 14, wherein the adjacent pixel is a pixel on the front side in the input direction. 16. The image processing apparatus according to claim 14, wherein the adjacent pixel is a pixel on the rear side in the input direction. 17. The image according to claim 10, wherein the pixels in the vicinity where the averaging processing unit performs the averaging process are both pixels adjacent to each other before and after the inputting unit inputs the color image data. Processing equipment. 18. Pixels in the vicinity where the averaging processing means performs the averaging processing are adjacent pixels along a direction in which the input means inputs the color image data and pixels in a direction intersecting the direction. The image processing apparatus according to claim 10. 19. The image processing apparatus according to claim 10, further comprising density detection means for detecting the density of the pixel based on the color image data of each pixel input by the input means. In addition, the averaging processing means is an image processing device that is a means for performing averaging processing by the averaging processing means when the detected density of the pixel is less than or equal to a predetermined value. 20. The averaging means is means for performing the averaging process for the predetermined color component only in an area where the density of the predetermined color component of the input image data is a predetermined value or less. The image processing device described. 21. The image processing apparatus according to claim 10, wherein the coordinate value of the color image data is defined as a value corresponding to the density of each color component of the color image data,
The grid point conversion means is first gradation number conversion means for converting the grid point coordinates as gradation number conversion, and further, for each color component of the corrected color image data output from the color correction means. Second gradation number converting means for converting the number of gradations of the first gradation number into a gradation number suitable for the image output device, and a floor obtained by converting the coordinate value by the first gradation number converting means. An image processing apparatus, wherein the number of tones is larger than the number of tones after being converted by the second tone number conversion means. 22. The image processing apparatus according to claim 21, wherein the second gradation number conversion means performs binarization and expresses gradation by the distribution density of the binarized dots. The image processing apparatus, which is a means for performing the averaging process when the density of the binarized dots is not more than a predetermined value. 23. The image processing apparatus according to claim 10, wherein the averaging processing unit compares the color correction data read by the color correction data reading unit with the color correction data of an adjacent pixel. And a means for performing the averaging process when the difference between the compared two-color correction data is less than or equal to a predetermined value. 24. The image processing apparatus according to claim 10, wherein the averaging processing unit determines whether or not to perform the averaging processing for each color forming the color image data. An image processing apparatus which is means for determining at least one color and performing the averaging process for each color based on the determination. 25. The averaging processing unit performs the averaging processing when it is estimated that the distances of the input color image data in the color space of adjacent pixels are equal to or less than a predetermined distance. The image processing apparatus according to claim 10, which is a means for performing. 26. The averaging means is arranged such that, when adjacent pixels have at least adjacent grid points after being converted by the grid point conversion means, the distance between the adjacent pixels is equal to or less than the predetermined distance. 26. The image processing apparatus according to claim 25, which is means for estimating that 27. The averaging processing means, when the color image data of adjacent pixels are both included in a unit space formed by grid points stored by the grid point information storage means. 26. Means for estimating that the distance between adjacent pixels is less than or equal to the predetermined distance.
The image processing apparatus according to any one of the preceding claims. 28. The image processing apparatus according to claim 10, wherein the averaging processing unit has a distance in the color space of the input color image data of adjacent pixels, which is equal to or less than a predetermined distance. Means for estimating whether or not another execution condition for the averaging process is satisfied, a distance between the two adjacent pixels is determined to be a predetermined distance or less, and the other execution condition An image processing apparatus comprising means for performing the averaging process only when it is determined that 29. The image processing device according to claim 10, wherein the grid point information storage means sets the coordinates of the grid points in the predetermined low-density area of the color space to be more than those in other areas. An image processing device that divides the image into smaller pieces and stores them. 30. An image processing apparatus for color-correcting and outputting a multi-color image represented by coordinate values in a two-dimensional or more-dimensional color space, wherein the coordinate value of each pixel of the image has a predetermined floor. Input means for inputting color image data represented by a key number, and the color space is divided by the number of gradations smaller than the number of gradations expressing the coordinate value, and the division is performed for each dimension. Grid point information storage means for storing the coordinate values of the selected grid points for the color space; a color correction table for storing correction data relating to the color of the color image data corresponding to each grid point; The coordinate value of the color image data in the color space is set to the coordinate value of the grid point stored in the grid point information storage unit according to a method in which the distance from the grid point is less than or equal to a predetermined value on average. A grid point conversion means for conversion, the correction data of the lattice point corresponding to the converted coordinate values,
A color correction data reading means for reading from the color correction table and a value corresponding to a main color of the read correction data of the grid points are selected, and for each main color, the grid point corresponds to the main color. Of the correction data of the adjacent grid points, in which only the coordinate values are shifted, the value corresponding to the main color is read, interpolation is performed using both values, and the correction data for the main color is replaced with the interpolation result. An image processing apparatus comprising: a color correction unit that outputs data. 31. The image processing apparatus according to claim 30, wherein the coordinate value of the color image data is defined as a value corresponding to the density of each color component of the color image data,
The grid point conversion means is first gradation number conversion means for converting the grid point coordinates as gradation number conversion, and further, for each color component of the corrected color image data output from the color correction means. Second gradation number converting means for converting the number of gradations of the first gradation number into a gradation number suitable for the image output device, and a floor obtained by converting the coordinate value by the first gradation number converting means. An image processing apparatus, wherein the number of tones is larger than the number of tones after being converted by the second tone number conversion means. 32. An averaging processing unit that performs a process of averaging the correction data output by the color correction unit based on the correction data of pixels in the vicinity of each pixel and outputs the final color image data. The image processing device according to claim 30 or claim 31, which is provided. 33. The image processing apparatus according to claim 10, further comprising a new pixel between a pixel adjacent to the pixel based on the color image data of each pixel input by the input means, Image averaging means for setting the input color image data to the new pixel is provided, and the averaging processing means is means for performing the averaging processing with the pixel assumed by the image enlarging means. Image processing device. 34. An image processing method for color-correcting and outputting a multicolor image represented by coordinate values in a two-dimensional or more-dimensional color space, wherein for each pixel of the image, the coordinate value is set to a predetermined floor. Color image data expressed using tones is input, the color space is divided by a number of gradations smaller than the number of gradations expressing the coordinate values, and the color space is divided into predetermined low-density areas. The color space is divided more finely than the area of, and the coordinate values of the grid points obtained by performing the division for each of the dimensions are stored for the color space, and the color image corresponding to each grid point is stored. A color correction table that stores correction data relating to the color of the data is prepared, and the coordinate value of the input color image data in the color space is on average less than a predetermined value from the grid point. According to the technique It was converted into the coordinate values of the stored grid points, the correction data of the lattice point corresponding to the converted coordinate values,
An image processing method for reading out from the color correction table and outputting as corrected color image data. 35. An image processing method for color-correcting and outputting a multi-color image represented by coordinate values in a two-dimensional or more-dimensional color space, wherein for each pixel of the image, the coordinate value is set to a predetermined level. Color image data expressed using tones is input, the color space is divided by a number of gradations smaller than the number of gradations expressing the coordinate values, and the color space is divided into predetermined low-density areas. The color space is divided more finely than the area of, and the coordinate values of the grid points obtained by performing the division for each of the dimensions are stored for the color space, and the color image corresponding to each grid point is stored. A color correction table that stores correction data relating to the color of the data is prepared, and the coordinate value of the input color image data in the color space is on average less than a predetermined value from the grid point. According to the technique It was converted into the coordinate values of the stored grid points, the correction data of the lattice point corresponding to the converted coordinate values,
A value corresponding to the main color is selected from the correction data of the grid points read out from the color correction table, and only the coordinate value corresponding to the main color is shifted from the grid points for each main color. Of the correction data of the adjacent grid points, the value corresponding to the main color is read, interpolation is performed using both values, and the correction data for the main color is replaced with the interpolation result to finally correct the correction data. Image processing method to output as completed color image data. 36. An image processing method for color-correcting and outputting a multicolor image represented by coordinate values in a two-dimensional or more-dimensional color space, wherein for each pixel of the image, the coordinate value is set to a predetermined level. A grid obtained by inputting color image data represented by a key number, dividing the color space by a number of gradations smaller than the number of gradations expressing the coordinate value, and performing the division for each dimension. Coordinate values of points are stored in the color space, and a color correction table storing correction data relating to the color of the color image data is prepared corresponding to each grid point, and the color of the input color image data is stored. The coordinate value in space is converted into the coordinate value of the stored grid point according to a method in which the distance from the grid point is, on average, a predetermined value or less, and the grid corresponding to the converted coordinate value. Point correction The over data,
An image processing method for performing a process of reading from the color correction table and averaging the read correction data of each pixel based on the correction data of a pixel in the vicinity of each pixel. 37. The image according to claim 36, wherein the averaging process is performed when the distance in the color space of the input color image data is estimated to be a predetermined distance or less for adjacent pixels. Processing method. 38. An image processing method for color-correcting and outputting a multicolor image represented by coordinate values in a two-dimensional or more-dimensional color space, wherein the coordinate value of each pixel of the image is a predetermined floor. A grid obtained by inputting color image data represented by a key number, dividing the color space by a number of gradations smaller than the number of gradations expressing the coordinate value, and performing the division for each dimension. Coordinate values of points are stored in the color space, and a color correction table storing correction data relating to the color of the color image data is prepared corresponding to each grid point, and the color of the input color image data is stored. The coordinate value in space is converted into the coordinate value of the stored grid point according to a method in which the distance from the grid point is, on average, a predetermined value or less, and the grid corresponding to the converted coordinate value. Point correction The over data,
A value corresponding to the main color is selected from the correction data of the grid points read out from the color correction table, and only the coordinate value corresponding to the main color is shifted from the grid points for each main color. Image processing of reading out the value corresponding to the main color from the correction data of the adjacent grid points, performing interpolation using the both values, and outputting the correction data in which the correction data for the main color is replaced with the interpolation result Method.