JP3094809B2 - 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. There are times when they do not overlap.

More specifically, as shown in FIG. 4A, 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. In the top portion of the area A of the image (area of FIG. 4 (b) A1),
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.

[0006] In the central portion of the region B of the image of FIG. 4 (a), since the input density of C and M are different, the value of the binarization error of the C and M generated in each pixel in the region B 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. 4 described above,
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, in response to the color, the threshold value for binarization employed in the binarization processing unit, and a threshold value setting means for selecting and setting a plurality equipped threshold value, the threshold setting Means print and record
The threshold value of a color that has little effect on boundary
A color image processing apparatus characterized in that the value is set so as to be shared with a value .

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. Density calculating means, 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. Binarization error calculating means for calculating a binarization error generated for each color of each pixel from the output density obtained and the correction input density obtained by the correction input density calculation means, and a weighting coefficient matrix for the binary error. 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,
Threshold setting means sets a threshold for binarization used in the binarization processing means according to the color.

Since different threshold values are set for each color,
Even if the input density is the same for each color, distribute to surrounding pixels 2
The value of the binarization error does not always match depending on 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.

The threshold value setting means selects one of a plurality of threshold values according to the color and sets it as a threshold value for binarization used in the binarization processing means . What
A threshold value for binarization used in the binarization processing means may be calculated according to the color and the density of the color.

[0016] the threshold value, 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 M (magenta), C (cyan), and Y (yellow), or M (magenta), C (cyan), and Y (yellow). ,
If the image data is composed of four colors of K (black), Y
By using the same thresholds 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, a combination of the above M, C, Y or R (red), G (green), B (blue) is usually used. As the four colors, M (magenta), C (cyan), Y (yellow), and K (black) are usually used.

The color halftone image may be formed 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 M, C, Y, K from the values of M, C, Y.

The binarization processing means, for example, corrects the density of each color of each pixel of the halftone image by the correction input density calculation means based on the sum of the binarization errors distributed from the peripheral pixels, thereby obtaining the corrected input density. The output density determining means compares the corrected input density determined by the corrected input density calculating means with the threshold to determine the output density of each color of each pixel, and the binarization error calculating means determines the output density. The binarization error generated for each color of each pixel is calculated from the output density determined by the means and the corrected input density obtained by the correction input density calculation means, and the binarization error distribution means calculates the binarization error. Is weighted based on the weighting coefficient matrix and distributed to peripheral pixels.

[0020]

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. A ROM 7 in which threshold values Tm, Tc, Ty, Tk and a weighting coefficient matrix M for each color in the binarization of each color are stored, a CPU 9 for performing various image processing using the RAM 5, and data after error diffusion processing An output image storage device (image memory) 11 for storing;
A bus 13 is a signal transmission path between them.

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 the image processing program in the ROM 7 and the data such as the threshold value and the weighting coefficient matrix.

Referring to FIGS. 2 and 3, the halftone original image data of three colors (M, C, Y) stored in the original image storage device 3 is converted to four colors (M, C, Y). The procedure for obtaining the output values by determining the corrected input density and the threshold value for each color by the error diffusion processing for the halftone image data of the four colors will be described as the halftone image data of (C, Y, K). 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).

[0024]

(Equation 1)

The function f of the conversion process from three colors to four colors is a general function in color image processing, and the description of the function f will be omitted. Next, the input density Ix, y of the first target color (for example, M) of the target pixel is extracted (S10).

Next, from the threshold values Tm, Tc, Ty, and Tk for each color provided in the ROM 7, a threshold value corresponding to the target color to be processed this time is extracted and set as the threshold value T (S1).
5). For example, “12” is set as the threshold value Tm for magenta.
0, a threshold Tc for cyan is "104", a threshold Ty for yellow is "136", and a threshold Tk for black
Is set to "152", and if the color of interest is magenta, "120" is used as the threshold value T for binarization.

Next, as shown in Expression 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).

[0028]

(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.

[0032]

(Equation 3)

Next, the weighting coefficient matrix is extracted from the ROM 7 (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, it is assumed that a weighting coefficient matrix M as shown in Expression 4 is stored in the ROM 7.

[0034]

(Equation 4)

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

For example, for the pixel on the right of the pixel of interest, the weighting coefficient is “7/48 (the component on the right of“ * ”in the matrix M of Equation 4: 7 / the sum of all components: 48)”.
As a result, “e × 7/48” is added to the buffer for storing the same color binarization error of the pixel on the right.

Thus, the processing of the target color of the pixel at the coordinates (x, y) is completed, and it is determined whether or not an unprocessed color exists in the pixel (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, the threshold value T for binarization is processed by the error diffusion method from the threshold values Tm, Tc, Ty, and Tk prepared for each color. The selection is made according to the color. As described above, since different threshold values are used for the respective colors, even if they have the same input density, the values of the binarization errors distributed to the surrounding pixels do not always match the colors. 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, S15 corresponds to the processing as threshold setting means.

[Others] In the above embodiment, the determination of the threshold value according to the color in step S15 is made simply by selecting from the threshold values Tm, Tc, Ty, and Tk for each color provided in the ROM 7. However, in consideration of the density of each color, in step S15, the threshold value may be set for each color by arithmetic processing as follows. For example, the threshold value T may be determined by calculation as shown in Expression 5 for magenta, Expression 6 for cyan, Expression 7 for yellow, and Expression 8 for black.

[0043]

(Equation 5)

In order to record the original image data consisting of the three color image data in four colors, the image data is converted into four color image data in step S5, and then the four colors are binarized by the error diffusion method. However, of course, if the image data is four-color image data from the beginning, the conversion processing in step S5 may be omitted and the processing may be performed. If recording is performed in three colors, it is only necessary to prepare a threshold value for each of the three colors and perform the processing. The same applies to the case of processing an image composed of two or more colors.

In particular, for a color that does not significantly affect the appearance of a boundary, the same threshold value as that of another color may be used. For example, in the above-described embodiment, in the case of yellow, compared to the other colors (M, C, K), when printing and printing on a blank sheet, the influence on the occurrence of the border is small, so that the threshold Ty is used instead. Alternatively, the threshold values Tm and Tc of other colors, for example, magenta or cyan, may be used as they are, that is, the threshold values may be partially shared and used for the binarization process.

[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.

FIG. 3 is an explanatory diagram illustrating a relationship between a target pixel and peripheral pixels in an error distribution process.

FIG. 4 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. Binarization processing means for converting the image data into two, which are used in the binarization processing means according to the color.
Select a threshold for value conversion from multiple thresholds
And a threshold setting means for setting said threshold value setting means is less color threshold affect the appearance
Is set so as to be shared with threshold values of other colors . 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 determining the output density of each color of each pixel by comparing the corrected input density obtained by the corrected input density calculating means with the threshold; and the output density determined by the output density determining means. A binarization error calculator for calculating a binarization error generated for each color of each pixel from the correction input density calculated by the correction input density calculator, and a binarization error based on a 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.