JP3094810B2 - Color image processing equipment - Google Patents

Description

DETAILED DESCRIPTION OF THE INVENTION

[0001]

BACKGROUND OF THE INVENTION 1. Field of the Invention The present invention relates to a color image processing apparatus, and more particularly to a digital image processing apparatus having a function of generating a color image data of pseudo halftone by binarizing a color halftone image. The present invention relates to a color image processing apparatus in an electrophotographic copying machine, a thermal transfer type or an ink jet type printer, and the like.

[0002]

2. Description of the Related Art Conventionally, as a means for binarizing a halftone image to generate a pseudo halftone image, there is a systematic dither method for binarizing the image according to a threshold matrix (dither matrix) table. Are widely used. However, these conventional methods have a contradiction that the matrix table needs to be enlarged in order to improve the tone reproducibility, and the matrix table must be reduced in order to obtain high resolution. It was difficult to achieve both reproducibility and high resolution.

In addition to the above, there is an error diffusion method as a method for achieving both gradation reproducibility and high resolution, and a relatively good evaluation is given among various conventional methods. The error diffusion method corrects the density by distributing an error between the actual density and the binarized value, which occurs when the halftone pixel is binarized, to peripheral pixels and corrects the density after the correction. This is a process of determining with a predetermined threshold value and determining one of two values.

[0004]

However, when this error diffusion method is applied to a color halftone image, the following phenomenon occurs. For example, due to the value of the binarization error distributed from the peripheral pixels, even if different colors in the data of the target pixel have the same value, the recording positions of the colors having the same data value overlap. In some cases, they do not overlap.

More specifically, as shown in FIG. 5A, the background is C (cyan) = M (magenta) = 40 and Y (yellow)
Consider an image in which a blue area A = 0, and an area B in which the values of C, M, and Y are variously different in the center. At the head of the area A of the image (the area A1 in FIG. 5B),
The value of the binarization error between C and M generated in each pixel is equal,
Therefore, the value of the weighted error sum distributed to each pixel is also C and M
And match. Whether the target pixel is recorded is determined based on the corrected input density obtained by correcting the input density of the target pixel by the weighted error sum allocated to the target pixel. And the arrangement of the recording pixels is the same. Therefore, in the region A1, color reproduction is performed by subtractive color mixture.

In the area B at the center of the image shown in FIG. 5A, the input densities of C and M are different, so that the value of the binarization error between C and M at each pixel in the area B is different. Therefore, in the area A2 affected by the binarization error of the area B, the value of the weighted error sum distributed to each pixel is:
C and M do not match. Therefore, although the input densities of C and M are equal in the area A2, the arrangement of the recording pixels of C and the arrangement of the recording pixels of M do not match. Therefore, area A
Reference numeral 2 denotes an area where subtractive color mixture and additive color mixture are mixed.

Although the input densities of the areas A1 and A2 are equal and the ratios of the recording pixels and the non-recording pixels of each color after binarization are also equal, the color appearance is different due to the above difference. Pseudo boundaries C and D, which do not exist in the original image, occur between the area A2 and the area A2, which causes a problem that the area A2 looks as if it were two areas having different input densities.

[0008] Of course, not only the example of FIG.
The same applies to the case where binarization processing using the error diffusion method is performed on a region having another input density, and the region where color reproduction is performed only by subtractive color mixture due to the binarization error distributed from the periphery. In some cases, a pseudo boundary that does not exist in the original image is generated between a region where color reproduction by subtractive color mixture and color reproduction by additive color mixture are mixed.

SUMMARY OF THE INVENTION The present invention has been made to solve the above-mentioned problems, and a more preferable color pseudo-halftone image free from pseudo boundaries regardless of the influence of adjacent areas. An object of the present invention is to provide a color image processing apparatus that can be created.

[0010]

According to the first aspect of the present invention,
In a color image processing apparatus for binarizing a color halftone image to create pseudo halftone color image data, binarization in which the density of each color of each pixel of the color halftone image is binarized by an error diffusion method. processing means, a plurality a provided weighting coefficient matrix, and selected depending on the color, and a coefficient matrix setting means for setting a weighting factor matrix for error distribution of the error diffusion processing, the upper
The coefficient matrix setting means has a characteristic that has little effect on appearance.
The constant color coefficient matrix is combined with other color coefficient matrices.
This is a color image processing apparatus characterized by setting to be common .

According to a second aspect of the present invention, there is provided the color image processing apparatus according to the first aspect , wherein the color halftone image is composed of halftone image data of three colors. The invention according to claim 3 is
The color halftone image is a color image processing apparatus according to claim 1, wherein a four-color halftone image data.

According to a fourth aspect of the present invention, there is provided the color image processing apparatus according to the first aspect , wherein the color halftone image is formed by converting halftone image data of three colors into halftone image data of four colors. . According to a fifth aspect of the present invention, the binarization processing means corrects the density of each color of each pixel of the halftone image based on a binarization error sum distributed from peripheral pixels to obtain a corrected input density. The density calculating means, the output density determining means for comparing the corrected input density obtained by the corrected input density calculating means with the threshold value to determine the output density of each color of each pixel, and the output density determining means. A binarization error calculator for calculating a binarization error generated for each color of each pixel from the output density and the correction input density calculated by the correction input density calculator; a color image processing apparatus according to claim 1-4, characterized in that and a binarization error distribution means for distributing the peripheral pixels are weighted based on.

[0013]

According to the color image processing apparatus of the first aspect, the binarization processing means binarizes the density of each color of each pixel of the color halftone image by an error diffusion method. on the other hand,
A plurality of coefficient matrix setting means are selected from a plurality of weighting coefficient matrices according to the color and set as weighting coefficient matrices for error distribution in the error diffusion processing.

Since the binarization error is distributed using a different weighting coefficient matrix for each color, even if the input density is the same for each color, the value of the binarization error distributed to the surrounding pixels does not always match the color. . Therefore, the subtractive color mixture and the additive color mixture are always mixed, and a pseudo boundary that does not exist in the original image does not occur.

Further, the weighting factor matrix, by being common in the different colors, are provided a plurality of fewer than types of colors of the color halftone image. This commonization is performed by using image data composed of three colors, such as C (cyan), M (magenta), and Y (yellow), or C (cyan), M (magenta), and Y (yellow). , K (black), Y is the same as the weighting coefficient matrix used in M or C.

The color halftone image may be halftone image data of three colors or halftone image data of four colors. As the three colors, C, M, Y or a combination of R (red), G (green), and B (blue) is usually used. As the four colors, C (cyan), M (magenta), Y (yellow), and K (black) are usually used.

The color halftone image may be composed of data obtained by converting halftone image data of three colors into halftone image data of four colors. This conversion can be made, for example, by a function known to calculate C, M, Y, K from the values of C, M, Y.

The binarization processing means corrects the input density by correcting the density of each color of each pixel of the halftone image by the correction input density calculation means, for example, using the sum of the binarization errors distributed from the peripheral pixels. The output density determining means compares the corrected input density determined by the corrected input density calculating means with a threshold to determine the output density of each color of each pixel, and the binarization error calculating means determines the output density determining means. The binary error generated for each color of each pixel is calculated from the output density determined by the above and the corrected input density calculated by the corrected input density calculating means, and the binary error distribution means calculates the binary error. It is realized by weighting based on the weighting coefficient matrix and distributing to surrounding pixels.

[0019]

DESCRIPTION OF THE PREFERRED EMBODIMENTS One embodiment of the present invention will be described below with reference to the drawings. FIG. 1 is a block diagram showing the image processing circuit 1. The image processing circuit 1 that performs the error diffusion processing and the like in the present embodiment is configured as a computer,
A required operation is performed on the input data. The image processing circuit 1 includes an original image storage device (image memory) 3 for storing halftone original image information inputted as a digital electric signal, a RAM 5 for storing each calculation result, an image processing program and a peripheral pixel. Weighting coefficient matrices Mm, Mc, My, for each color for the distribution of the binarization error of each color
ROM 7 storing Mk, threshold value T, etc., and RAM
5, an image storage device (image memory) 11 for storing data after error diffusion processing, and a bus 1 as a signal transmission path between them.
3.

The color halftone image data of the original image input from an image input means (not shown) such as a color image pickup device or an external storage device for storing already captured color halftone image data is transmitted via a bus 13. It is stored in the original image storage device 3. The input of the original image data may be sent pixel by pixel, line by line, or by one screen. The CPU 9 performs an error diffusion process on the stored color halftone image information for each color of each pixel using an image processing program in the ROM 7, the above-described weighting coefficient matrix, and data such as threshold values.

Referring to FIGS. 2 and 3, the halftone original image data of three colors (C, M, Y) stored in the original image storage device 3 is converted to four colors (C, M, Y). (M, Y, K) halftone image data, and a procedure for determining a corrected input density and a weighting coefficient matrix for each color by error diffusion processing to obtain output values for the halftone image data of the four colors. . It is assumed that the number of gradations of each color of the halftone original image data of three colors and the halftone image data of four colors is 256 gradations from 0 to 255.

FIG. 2 is a flowchart showing a processing procedure of the binarization processing in this embodiment. First, the first pixel data is read from the original image storage device 3 as a pixel of interest, and the three-color pixel data is converted into four-color image data by Equation 1 (S5).

[0023]

(Equation 1)

Since the function f of the conversion process from three colors to four colors is a general function in color image processing, the description of the content is omitted. Next, the input density Ix, y of the first target color (for example, C) of the target pixel is extracted (S10). Further, the threshold value T is extracted from the ROM 7 (S15).

Next, as shown in Equation 2, the input density I of the target color
x and y are weighted error sums E of binarization errors of the target color in the peripheral pixels distributed from the peripheral pixels to the target color of the target pixel.
To calculate the corrected input density I'x, y of the target color (S20).

[0026]

(Equation 2)

This error sum E is calculated in a step S11 to be described later in a pixel processed before the current pixel of interest.
Due to the distribution of the binarization error of the target color at 0, this is a value already accumulated in the buffer for storing the binarization error of the target color of each pixel set in the RAM 5 and already obtained.

Next, the threshold value T and the corrected input density I'x, y
Are compared (S50), the output density I of the color of interest which becomes binary
The value of e is determined (S60, S70). If I′x, y ≧ T, the output density Ie of the target color becomes “255” which is one of two values (S60), and if I′x, y <T, the output density of the target color Ie becomes "0" which is the other of the two values (S70). The output density Ie of the target color of the target pixel thus determined is stored in the output image storage device 11 (S80).

Next, a binarization error e of the target color generated at the target pixel is obtained (S90). As shown in Equation 3, the binarization error e is obtained by subtracting the output density Ie of the target color from the corrected input density I′x, y of the target color.

[0030]

(Equation 3)

Next, a weighting coefficient matrix is determined according to the target color, and the corresponding weighting coefficient matrix is
Take out from M7 (S100). Using this weighting coefficient matrix, the binarization error of the target color generated in the target pixel is distributed to the target color of the peripheral pixel (S110).

For example, in the ROM 7, the expressions 4, 5, 6, 7
, Four weighting coefficient matrices Mm, M
It is assumed that c, My, and Mk are stored.

[0033]

(Equation 4)

When the color of interest is, for example, magenta, the weighting coefficient matrix Mm of Expression 4 is selected.
That is, a coefficient value having a positional relationship as shown in FIG. 3A with respect to the target pixel represented by “*” is obtained from a ratio of each component of the weighting coefficient matrix Mm and the sum of all components,
The binarization error e of magenta of the target pixel is multiplied and weighted, and added to the buffer for storing the binarization error of magenta of the pixel at the corresponding position.

For example, for the pixel on the right of the pixel of interest, the weighting coefficient is “4/16 (the component on the right of“ * ”in the matrix Mm of Equation 4: 4 / the sum of all components: 1”
6), and as a result, the magenta 2 of the pixel on the right
Regarding the buffer for storing the binarization error, “e × 4/1
6 "is added.

When the color of interest is cyan, the weighting coefficient matrix Mc of Expression 5 is selected. That is, a coefficient value having a positional relationship as shown in FIG. 3B with respect to the target pixel represented by “*” is obtained from the ratio of each component of the weighting coefficient matrix Mc to the sum of all components, The binarization error e of cyan of the target pixel is multiplied and weighted, and added to the buffer for storing the binarization error of cyan of the pixel at the corresponding position. When the color of interest is yellow, the weighting coefficient matrix My of Expression 6 is selected, and the binarization error e of yellow of the pixel of interest is distributed by the distribution shown in FIG.
, And weighted, and added to the buffer for storing the binary error of yellow at the pixel at the corresponding position. When the color of interest is black, the weighting coefficient matrix Mk of Expression 7 is selected, and is weighted by multiplying the binarization error e of black of the pixel of interest by distribution as shown in FIG. It is added to the buffer for storing the black binarization error of the pixel at the corresponding position.

Thus, the processing of the target color of the pixel at the position of the coordinates (x, y) is completed, and it is determined whether or not the next pixel has an unprocessed color (S112). Is affirmatively determined, the next unprocessed color of the same pixel is set as the target color (S114), and step S10 is performed.
, The above-described processing is repeated.

When the processing of all colors for the same pixel is completed, a negative determination is made in step S112, and it is determined whether or not the next unprocessed pixel exists (S12).
0) If there is an unprocessed pixel, the unprocessed pixel at the next coordinate is set as the target pixel (S130), and step S5 is performed.
The processing of each color described above is repeated.

The processing order of pixels in this embodiment is as follows.
The processing is performed rightward in the horizontal direction from the left pixel of the uppermost line, and the same processing is sequentially shifted down by one line, so that the lower right pixel is finally processed. If the above-described processing has been performed on all the pixels, a negative determination is made in step S120, and the binarization processing ends.

In this embodiment, as described above, in the error diffusion method, a weighting coefficient matrix for binarizing error distribution is calculated from a weighting coefficient matrix prepared for each color.
It is determined according to the color processed by the error diffusion method. Since the binarization error is distributed using a different weighting coefficient matrix for each color, even if the input density is the same for each color, the value of the binarization error distributed to the surrounding pixels does not always match the color. Therefore, the subtractive color mixture and the additive color mixture are always mixed, and a pseudo boundary that does not exist in the original image does not occur.

In the above embodiment, step S20 corresponds to the processing as the correction input density calculating means.
Steps S50, S60, and S70 correspond to processing as output density determination means, step S90 corresponds to processing as binarization error calculation means, step S110 corresponds to processing as binarization error distribution means, S100 corresponds to processing as coefficient matrix setting means.

[Others] In the above embodiment, in order to record original image data composed of image data of three colors in four colors, the original image data is converted into image data of four colors in step S5, and then error diffusion is performed on the four colors. Although the image data is binarized by the method, it is needless to say that if the image data is four-color image data from the beginning, the conversion processing in step S5 may be omitted. Also, if recording in three colors, leave three colors
A weighting coefficient matrix may be prepared for each of the three colors, and a weighting coefficient matrix may be selected according to the color at the time of error diffusion processing to perform a binarized error distribution processing. The same applies to the case of processing an image composed of two or more colors.

In particular, for a color which does not significantly affect the appearance of boundaries, the same one as the weighting coefficient matrix of another color may be used. For example, in the above-described embodiment, when yellow is used, the influence on the boundary occurrence is smaller when printing and printing on blank paper than in the other colors (M, C, K). Instead of the coefficient matrix, another color, for example, the weighting coefficient matrix of magenta equation 4 or cyan equation 5 may be used as it is, that is, the weighting coefficient matrix may be partially shared and used for binarization error distribution. .

The weighting coefficient matrix was a combination of matrices having different matrix shapes or different component values for each color.
That is, in the case of Equation 4, 2 rows and 5 columns, in the case of Equation 5, 2 rows and 5 columns in which the component values are different from those of Equation 4, 2 rows and 3 columns in the case of Equation 6, and 3 rows and 3 columns in the case of Equation 7 However, other shapes or component values may be combined. Also, a combination of weighting coefficient matrices having the same shape but different values of each component may be used.

Further, in addition to or instead of the configuration for selecting the weighting coefficient matrix according to the color in the above-described embodiment, the threshold value T is calculated and set for each color, or the threshold value T provided for each color is set. The selection may be made according to the color to prevent the occurrence of the boundary.

[Brief description of the drawings]

FIG. 1 is a block diagram illustrating an image processing circuit according to one embodiment.

FIG. 2 is a flowchart of the binarization process.

3A and 3B are explanatory diagrams illustrating a relationship between a target pixel and peripheral pixels for each color in an error distribution process, wherein FIG. 3A is an explanatory diagram related to magenta, and FIG. 3B is an explanatory diagram related to cyan.

FIGS. 4A and 4B are explanatory diagrams showing a relationship between a target pixel and peripheral pixels for each color in the error distribution processing, wherein FIG. 4A is an explanatory diagram relating to yellow, and FIG. 4B is an explanatory diagram relating to black.

FIG. 5 is an explanatory diagram of pseudo boundary generation in conventional binarization processing.

[Explanation of symbols]

DESCRIPTION OF SYMBOLS 1 ... Image processing circuit 3 ... Original image storage device 5 ...
RAM 7 ROM 9 CPU 11 Output image storage device 13 Bus

Continuation of the front page (58) Fields surveyed (Int.Cl. ⁷ , DB name) H04N 1/40-1/409 H04N 1/46 H04N 1/60

Claims (5)

(57) [Claims] 1. A color image processing apparatus for binarizing a color halftone image to generate pseudo halftone color image data, wherein a density of each color of each pixel of the color halftone image is converted into a binary value by an error diffusion method. And a coefficient matrix setting means for selecting from a plurality of weighting coefficient matrices according to the color and setting as a weighting coefficient matrix for error distribution in the error diffusion processing. , The coefficient matrix setting means has little effect on appearance
The coefficient matrix of a specific color is replaced with the coefficient matrix of another color.
A color image processing apparatus characterized in that it is set so as to be common with the image processing apparatus. Wherein said color halftone image, three-color image processing apparatus according to claim 1, wherein comprising a halftone image data. Wherein the color halftone image, four-color image processing apparatus according to claim 1, wherein comprising a halftone image data. 4. The color image processing apparatus according to claim 1 , wherein said color halftone image is data obtained by converting halftone image data of three colors into halftone image data of four colors. 5. A correction input density calculation means for correcting a density of each color of each pixel of the halftone image by a binarization error sum distributed from peripheral pixels to obtain a correction input density. Output density determining means for comparing the corrected input density calculated by the corrected input density calculating means with a threshold to determine the output density of each color of each pixel; and the output density determined by the output density determining means. A binarization error calculating means for calculating a binarization error generated in each color of each pixel from the correction input density calculated by the correction input density calculation means; and a binarization error based on the weighting coefficient matrix. The color image processing apparatus according to any one of claims 1 to 4 , further comprising: binarization error distribution means for weighting and distributing to peripheral pixels.