JP2002077655A - Image processing method - Google Patents

Abstract

PROBLEM TO BE SOLVED: To prevent deterioration in image quality due to an overlapping of dots of each color component of image data after an image binarization. SOLUTION: In an image processing method to create binary images by applying binarization to multivalued gradation pixels having a plurality of color components, threshold data that are mutually different corresponding to a first and a second color components are prepared, and a binary signal is outputted by comparing a threshold set by the prepared threshold data with image data to which error data are added.

Description

DETAILED DESCRIPTION OF THE INVENTION

[0001]

BACKGROUND OF THE INVENTION 1. Field of the Invention The present invention relates to an image processing method applied to a printer, a scanner, a copying machine, a facsimile, etc., for reproducing multi-valued color image information as a binary image.

[0002]

2. Description of the Related Art An error diffusion method is widely known as one of methods for converting a multi-valued image into a binary image.

FIG. 4 is a block diagram showing a circuit for performing a conventional error diffusion method.

In FIG. 4, multi-value data D of a target pixel to be subjected to a binarization process is read from an image memory 100, and an adder 101 of an error diffusion processing unit 106 adds error data E of the target pixel. F = D + E is output.

The data F of the pixel of interest to which the error data has been added is compared with a binarization threshold Th in a comparator 103. If F ≧ Th, a binary signal B = “1” is output, and F < In the case of Th, the binary signal B = "0" is output. Then, from the output result, the binarization error E ′ at the time of the binarization processing is calculated by the subtractor 105 as E ′ = FB ′.

Here, the input data has 256 gradations (0 to 2).
55), B ′ = B × 255. Therefore, for example, when the input multi-valued data is D = 230 and the threshold for binarization is Th = 128, the output data after binarization is B = 1, and the binarization error E is E = D −B × 255 =
230-1 × 255 = −25.

The binarization error E is calculated by the weighting error calculator 1
At 05, the data is stored in the error memory 102 and added to the multi-value data of the next pixel by the adder 101 in order to distribute the data of the pixels to be processed thereafter by the predetermined error matrix Mxy. Propagation takes place.

That is, in the example, the input multi-value data is D = 2
As a result of comparison with the binarization threshold value Th = 128, the output data after binarization is 1, which is 255 at 256 gradations. On the other hand, 25 errors occur. Therefore, an error E = −25 with respect to the input multi-valued data D = 230 is defined as a binarization error,
The error is distributed to the unprocessed pixel error memory 102 by the weighting error calculator 104 using the error matrix, and is reflected in the binarization process for the subsequent pixels.

FIG. 5 is an explanatory diagram showing an error matrix used in the conventional error diffusion method.

In FIG. 5, the pixel indicated by * is the current pixel of interest, and it is assumed that binarization processing is performed on this pixel.

An error generated when the target pixel is binarized is distributed to an unprocessed next pixel by the weighting coefficients (7, 1, 5, 3) shown in FIG. When performing the binarization processing of the pixel of interest indicated by *, the error memory 10
2 is read, and the next input value read from the image memory 100 is corrected using the error distribution value.

As described above, the error diffusion method distributes a binarization error generated when a certain pixel is binarized to pixel data to be binarized thereafter, and after binarization, the image data and the image data This is a method of minimizing an error with the original multi-valued image data.

[0013]

In such a conventional image processing method, when an image having a plurality of color components is subjected to a binarization process by an error diffusion method, there is a problem of color superposition. Occurs.

That is, in a micro scale of about the dot size actually formed, an area where a plurality of color components overlap and an area where they do not overlap occur, causing visual color unevenness and causing image deterioration. .

SUMMARY OF THE INVENTION It is an object of the present invention to provide an image processing method capable of preventing image quality deterioration due to overlapping of dots of each color component of image data after image binarization.

[0016]

In order to solve this problem, an image processing method of the present invention generates a binary image by performing a binarization process on a multi-valued pixel having a plurality of color components. A threshold value data different from each other corresponding to the first color component and the second color component, and the threshold value set by the threshold value data and the image data obtained by adding the error data to the threshold value data. Are compared to output a binary signal.

This makes it possible to prevent image quality deterioration due to overlapping of dots of each color component of image data after image binarization.

[0018]

DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS The invention according to claim 1 of the present invention is an image processing method for generating a binary image by performing a binarization process on a multi-valued gradation pixel having a plurality of color components. And the first
Threshold data different from each other is prepared corresponding to the color component and the second color component, and a threshold signal set by the threshold data is compared with the image data to which the error data is added, and a binary signal is output. This is an image processing method, and has an effect that it is possible to prevent image quality deterioration due to overlapping of dots of each color component of image data after image binarization.

According to a second aspect of the present invention, in the first aspect of the present invention, the threshold data corresponding to the first color component and the second color component are in an inverse relationship to each other. This is a processing method, and has an effect that it is possible to prevent image quality deterioration due to overlapping of dots of each color component of image data after image binarization.

FIG. 1 shows an embodiment of the present invention.
This will be described with reference to FIG.

FIG. 1 is a block diagram showing a circuit for executing an image processing method according to an embodiment of the present invention, and FIG. 2 is a diagram showing an example of threshold data stored in threshold data storage means of FIG. FIG. 3 and FIG. 3 are flowcharts showing a processing procedure by an image processing method according to an embodiment of the present invention.

As shown in the figure, image data G of a pixel of interest to be subjected to a binarization process is read from the image memory 1 into an error diffusion device 9 and is set based on threshold data storing means 7 storing threshold data. 8, a threshold is set. Further, the adder 2 outputs the pixel data I to which the error data H in the target pixel is added.

The pixel data I of the pixel of interest to which the error data H has been added is compared in the comparator 4 with the threshold value set by the threshold value setting means 8, and a binary signal C of ON or OFF is output. Then, based on the output result and the pixel data I, the error data H ′ at the time of the binarization processing is calculated by the subtractor 6.

The error data H ′ is weighted by a weighted error calculator 5 to calculate error data H for weighting adjacent pixels by a predetermined error matrix Mxy.
It is stored in the error memory 3. Then, in order to distribute the error data H to the pixel data to be processed thereafter, the above-described image data G and the error data H are added in the adder 2, and the error data is propagated.

Here, the threshold value setting means 8 includes the image memory 1
A threshold value for binarization is set based on the color information of the pixel data G received from, and this is output to the comparator 4.

Next, processing of each color component according to the present embodiment will be described. In the present embodiment, input multi-valued image data is composed of three color components of cyan, magenta, and yellow. However, the present invention can be applied to color components of other combinations.

First, the processing of cyan as the first color component will be described.

In FIG. 1, when cyan pixel data is obtained from the image memory 1, it is input to the threshold setting means 8 and the adder 2. In the adder 2, the error data H output from the error data storage means 7 is added to the cyan pixel data, and the added pixel data I is output to the comparator 4 and the subtractor 6.

In the comparator 4, the pixel data I is compared with the threshold value set in the threshold value setting means 8 based on the threshold value data in the threshold value data storage means 7.

Here, FIG. 2 shows an example of threshold data stored in the threshold data storage means 7. In the case of cyan, the threshold data of FIG. 2A is used as the threshold. Then, the threshold value is compared with the pixel data I output by the adder 2, and a binary signal C is output.

At the same time, the pixel data I is also output to the subtractor 6. Then, the subtractor 6 calculates error data H ′ based on the binary signal C and the pixel data I, and outputs the error data H ′ to the weighted error calculator 5. The weighting error calculator 5 calculates a weighting error for an adjacent pixel of the pixel data H ′. This calculation result is stored in the error memory 3 and transferred to the adder 2 as error data H.

Next, the processing of magenta as the second color component will be described.

In FIG. 1, when magenta pixel data is obtained from the image memory 1, it is input to the threshold setting means 8 and the adder 2. In the adder 2, the error data H output from the error data storage means 7 is added to the magenta pixel data, and the pixel data I after the addition is output to the comparator 4 and the subtractor 6.

The threshold setting means 8 obtains threshold data corresponding to the magenta pixel data stored in the threshold data storage means 7 shown in FIG. And set comparator 4
Output to That is, in the case of magenta, FIG.
The threshold data shown in FIG. 2B, which is the inverted threshold data of (a), is used. Then, in the comparator 4,
This threshold value is compared with the error data I added by the adder 2, and a binary signal C is output.

At the same time, the pixel data I is also output to the subtractor 6. Then, the subtractor 6 calculates error data H ′ based on the binary signal C and the pixel data I, and outputs the error data H ′ to the weighted error calculator 5. The weighting error calculator 5 calculates a weighting error for an adjacent pixel of the pixel data H ′. This calculation result is stored in the error memory 3 and transferred to the adder 2 as error data H.

Finally, the yellow processing will be described.

Since yellow has a characteristic that it is difficult to recognize visually, even if it overlaps with other color components, the probability that it can cause image deterioration is low. For this reason, the binarization process is performed using a threshold value that does not invert as shown in FIG. However, the binarization process can be performed on yellow using the same threshold value as that of magenta (FIG. 2B).

Next, the overall processing of this embodiment will be described with reference to the flowchart of FIG.

First, pixel data to be binarized is obtained from the image memory 1 (S200).

If the acquired pixel data is a cyan or yellow color component (S210), a weighting error is added using the above-described threshold data shown in FIG. 2A or 2C. (S220) Binarization processing is performed (S230). If the acquired pixel data is not a cyan or yellow color component (S210), that is, if it is magenta, error weighting is performed using the above-described threshold data of FIG. Is set to a value inverted from that for cyan (S250), and a binarization process is performed (S260).

If the binarization process is performed in this way, a binarization error is calculated (S270) and stored as error data (S280).

Then, such processing is repeated for all the pixels (S290).

As described above, different threshold data are prepared corresponding to the first color component and the second color component, and the threshold set by such threshold data and the error data H are calculated. Since the binary signal C is output by comparing the added image data I, it is possible to prevent image quality deterioration due to overlapping of dots of each color component of the image data after image binarization. .

[0044]

As described above, according to the present invention, mutually different threshold data are prepared corresponding to the first color component and the second color component, and are set by such threshold data. The threshold value and the image data to which the error data has been added are compared to output a binary signal. Therefore, it is possible to prevent image quality deterioration due to overlapping of dots of each color component of the image data after image binarization. An effective effect is obtained that is possible.

[Brief description of the drawings]

FIG. 1 is a block diagram showing a circuit for executing an image processing method according to an embodiment of the present invention;

FIG. 2 is an explanatory diagram showing an example of threshold data stored in a threshold data storage unit of FIG.

FIG. 3 is a flowchart illustrating a processing procedure according to an image processing method according to an embodiment of the present invention;

FIG. 4 is a block diagram showing a circuit for performing a conventional error diffusion method.

FIG. 5 is an explanatory diagram showing an error matrix used in a conventional error diffusion method.

[Explanation of symbols]

 DESCRIPTION OF SYMBOLS 1 Image memory 2 Adder 3 Error memory 4 Comparator 5 Weighting error calculator 6 Subtractor 7 Error data storage means 8 Threshold setting means 9 Error diffusion device

 ──────────────────────────────────────────────────続 き Continued on the front page F term (reference) 2C262 AA24 AA26 AA27 AB13 BB03 BB08 BB22 BB27 EA10 5B057 BA30 CA01 CA08 CA12 CA16 CB01 CB07 CB12 CB16 CC02 CE14 CE16 CH08 CH18 DA17 5C077 LL04 MP08 NN11 PP33 PQ08 LA11 LC09 NA01

Claims (2)

[Claims] 1. An image processing method for generating a binary image by performing a binarization process on a pixel of a multi-level gradation having a plurality of color components, wherein a first color component and a second color component are An image processing method comprising preparing different threshold data corresponding to each other, comparing a threshold set by the threshold data with image data to which error data has been added, and outputting a binary signal. 2. The image processing method according to claim 1, wherein the threshold data corresponding to the first color component and the second color component are in a mutually inverted relationship.