JPH07123259A - Picture processing unit - Google Patents

Abstract

PURPOSE:To suppress production of moire with respect to the picture processing unit converting a multi-level picture into s binary picture through the use of the error diffusion method. CONSTITUTION:The processing unit is provided with a selector 8 selecting one parameter group from plural parameter groups and a mean error arithmetic section 9 calculating mean error data based on error data stored in an error data memory 6 and a parameter of the parameter group selected by the selector 8. The selector 8 selects a different parameter group when the picture elements of a predetermined level are consecutive or the color of the picture of the picture element processed just before is closer to the color of the picture element currently processed.

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 for converting a multi-tone image into a binary image by using an error diffusion method, and mainly to a hard copy apparatus for a two-dimensional image such as a copying machine or a printer. It is suitable.

[0002]

2. Description of the Related Art Various binary processing methods have been proposed in the past for outputting a multi-tone image having light and shade to a binary display device such as a dot printer. Multi-tone image 2
The value is basically set by setting a predetermined intermediate level as a threshold value, a black level when it is less than the threshold value, and a white level when it is more than the threshold value.

The brightness from black to white is 2 here.
An image composed of pixels having values divided into 56 levels is called a multi-gradation image, and an image composed of pixels having only two values of black and white is called a binary image.

In the case of a multi-tone image, level 0 is generally black,
Level 255 is assigned white. Therefore, level 128 is gray and level 2
A value of 00 produces light gray (gray close to white), and a level of 80 produces dark gray (gray close to black). On the other hand, in the case of a binary image, level 0 is black, level 255 is white, and there is no other level, that is, gray.

An error diffusion method is one of the methods for binarizing a multi-tone image. This method processes an error of a pixel that has already been binarized by taking it into consideration when binarizing pixels around it, and is executed in the following procedure. Pixel a now
(Level 200), pixel b (level 150), pixel c
It is assumed that there is a multi-gradation image composed of (level 80), ..., And binarization processing is performed in order of pixel a, pixel b, pixel c, ... With level 128 as a threshold value.

First, when the pixel a is binarized by using the level 128 as a threshold value, the level becomes 255 (white) after binarization, so that the following error value a appears. Input value a (200) -Output value a (255) = Error value a (-55)

Next, the processing for pixel b is started. In this case, since the binarization processing is performed in consideration of the error of the pixel a processed immediately before, the input value b considering the error is as follows. Input value b (150) + error value a (-55) = input value b (95) considering error If the input value b considering this error is binarized, level 0
(Black). Therefore, the following error value b appears in the pixel b. Input value b (150) -Output value b (0) = Error value b (150)

Next, the processing for pixel c is started. Also in this case, since the binarization processing is performed in consideration of the error of the pixel b processed immediately before, the input value c considering the error is as follows. Input value c (80) + error value b (150) = input value c (230) considering error If the input value c considering this error is binarized, it becomes level 2
It becomes 55 (white). Therefore, the following error value c appears in the pixel c. Input value c (80) -output value c (255) = error value c (-175)
Perform value conversion processing.

That is, since the pixel a has a level of 200 (darker than white), the output has a level of 255 (white).
When the next pixel b is binarized to fill the error, even if the pixel b is a bright color, it is corrected so that it becomes black as much as possible after binarization, and the binarization is performed so as to minimize the density error with the original image. The error diffusion method is used.

In the actual error diffusion method, the error value of each pixel is weighted by taking into consideration the error of a plurality of pixels that have been previously processed, and the error value of each pixel is weighted in order to place more importance on the error of the pixel closer to the periphery. Is averaged to obtain an error value.

For example, as shown in FIG. 6, [5],
Processing for binarizing the pixels a, b, c ... Using the parameter groups [7] and [*] will be described. However, the parameter indicated by * represents the position of the pixel currently processed.

First, the processing of pixel a is executed (FIG. A).
However, since the pixel a is the first pixel, the output value a is 255 and the error value a is −55, which is the same as the previous processing result. Then, the processing of the pixel b is executed (FIG. B). This time, the error of the pixel a is taken into consideration, the error value a is multiplied by the parameter 7, and it is divided by 7 to calculate the input value b in consideration of the error. Input value b (150) + error value a (-55) x7 / 7 = input value b (95) considering error If the input value b considering this error is binarized, the output value b
Becomes level 0. Therefore, the following error value b is obtained. Input value b (150) -Output value b (0) = Error value b (150)

Next, the processing for pixel c is started (FIG. C). This time, considering the error between the pixel a and the pixel b, the error value a is multiplied by the parameter 5, the error value b is multiplied by the parameter 7, and the result is divided by the sum 12 (= 5 + 7) of both parameters to calculate the error. Calculate the considered input value c. Input value c (80) + {error value a (-55) x 5 + error value b (150)
× 7} / 12 = input value c (145) considering error

When the input value c in consideration of this error is binarized, the output value c becomes the level 255, and the following error value c is obtained. Input value c (145) -output value c (255) = error value c (-175) In this way, the error value of each pixel is weighted, and binarization processing is performed while giving more importance to the error of pixels closer to the periphery, Minimize the density error from the original image.

[0015]

By the way, in the error diffusion method, generally only one parameter group is used as a parameter group for weighting the error value of each pixel. However, in this case, a unique striped pattern may appear during binarization, which causes moire. Therefore,
An object of the present invention is to suppress the occurrence of moire when converting a multi-tone image into a binary image by the error diffusion method.

[0016]

An image processing apparatus according to the present invention comprises a parameter memory for storing a plurality of parameter groups, a selector for selecting one parameter group from the plurality of parameter groups, and a parameter group selected by the selector. An average error calculation unit that calculates average error data based on error data stored in a parameter and error data memory, an addition unit that adds multi-tone input pixel data and the average error data, and an addition unit The addition output data is compared with a predetermined threshold level, converted into a binary level and output as binary pixel data, and 2 based on the multi-gradation pixel data and the binary pixel data from the threshold comparison unit. An error calculation unit that calculates an error caused by the binarization process as error data and stores the error data in the error data memory. .

In this case, the selector monitors the level of the input pixel data and selects a different parameter group when pixels of a predetermined level are continuous. Alternatively, the selector monitors the input pixel data and selects a different parameter group when the pixel processed immediately before and the pixel of interest currently processed are of a similar color.

[0018]

According to the present invention, when pixels of a certain level are consecutive,
Alternatively, as in the case where the pixel processed immediately before and the pixel of interest currently being processed are close in color, a pixel in which moire occurs is obtained in advance, and a parameter group that differs only in the processing of that pixel is selected, and this selected parameter is selected. Average error calculation is performed for each group, and binarization processing of a multi-tone image is performed.

[0019]

1 is a block diagram showing an embodiment of an image processing apparatus according to the present invention. In the present embodiment, the input image memory 1 is a memory that stores the levels of a plurality of pixels forming a two-dimensional multi-tone image as digital pixel data.

The addition unit 2 reads pixel data A of each pixel read from the input image memory 1 and average error data F described later.
Is a circuit for adding and the addition result is the addition pixel data B
Is input to the threshold comparison unit 3.

The threshold comparing section 3 is a circuit for outputting the pixel data C of the white level when the level of the added pixel data B is equal to or higher than the predetermined threshold level and the black level when the level of the added pixel data B is less than the threshold level. It is stored in the output image memory 4.

The error calculation unit 5 subtracts the pixel data A read from the input image memory 1 and the output pixel data C from the threshold value comparison unit 3 and is generated as the multi-gradation pixel data A is binarized. The error is calculated and stored in the error data memory 6 as the error data E.

The parameter memory 7 is a memory for storing a plurality of parameter groups. In this embodiment, the parameter memory 7 stores the five parameter groups Pa, Pb, Pc, Pd and Pe shown in FIG. Each of the parameter groups Pa to Pe is composed of a 5 × 3 parameter matrix, the halftone dot portion indicated by * is the position of the target pixel currently being processed, and the shaded portion is the pixel position which can be ignored in the processing.

The selector 8 selects one parameter group from the five parameter groups Pa to Pe stored in the parameter memory 7 and outputs the selected parameter group to the average error calculator 9, which is a method for determining the parameter group. Monitors the level of pixel data A from the input image memory 1 and selects a different parameter group only when pixels of a certain fixed level are continuous.

The average error calculator 9 is provided in the error data memory 6
The error data E stored in is multiplied by the parameter of the parameter group selected by the selector 8, the accumulated value is divided by the total value of each parameter to obtain the average error data F, which is added to the addition unit 2 as the added data. It is what is output.

Next, the operation of the image processing apparatus shown in FIG.
The operation will be described with reference to the operation explanatory diagrams shown in FIGS.
In this embodiment, the input image memory 1 stores 6 × 4 pixel pixel data A _ij (i = 1 to 6, 1 to 4), and the upper left pixel data is A ₁₁ , the lower right pixel data is shown in FIG. The pixel data of A is represented by A _64, and binarization processing is performed for each pixel in the horizontal direction from the upper left pixel, and when the row is completed, the binarization processing is performed in the vertical direction, and the binarization processing is performed again from the leftmost pixel.

Further, in the explanatory views shown in FIGS. 3 to 5,
Each window frame W schematically shows the positional relationship between the currently processed pixel and the already processed pixel, has a size of 5 × 3 similarly to the parameter group, and the position of the pixel being processed is The positions of the processed pixels are indicated by hatching with halftone dots. Further, in this embodiment, it is assumed that the parameter group Pa shown in FIG. 2 is selected as the parameter group.

First, the binarization process of the first pixel data A ₁₁ (level 200) will be described with reference to the explanatory view shown in FIG. Since it is the first pixel, the average error data F in the addition unit 2 is zero, and the threshold comparison unit 3 receives the input pixel data A
₁₁ is input as it is as the addition pixel data B ₁₁ .

In the threshold comparing section 3, since the level 200 of the added pixel data B ₁₁ is higher than the threshold level 128, the data of level 255 is output as the output pixel data C ₁₁ and stored in the output image memory 4.

At the same time, the error between the input pixel data A ₁₁ and the output pixel data C ₁₁ is calculated by the error calculator 5 and stored in the error data memory 6 as error data E ₁₁ (= A ₁₁ −C ₁₁ ). In this case, the level of the error data E ₁₁ is −55.
Becomes Thus, the binarization processing of the first pixel data A ₁₁ is completed.

Next, the binarization process of the second pixel data A ₂₁ (level 150) will be described with reference to the explanatory view shown in FIG. Since it is the second pixel, processing is performed in consideration of the error data E ₁₁ stored in the error data memory 6.
That is, the error data E ₁₁ is read from the error data memory 6, the following average error calculation is performed using the parameter Pa selected by the selector 8, the average error data F ₂₁ is calculated, and the average error data F ₂₁ is output to the addition unit 2 as addition data. . F _ij = Σ (E _{im j} × p _−m ) / Σ (p _−m ).

Here, p represents a parameter of the parameter group Pa, and -m represents a position m pixels ahead of the pixel of interest. In this case, i = 2 and j = 1, and the processed pixel is only the first pixel processed immediately before. Therefore, m
= 1 and E ₁₁ = -55 and p _-1 = 7, so the average error data F ₂₁ is as follows. F ₂₁ = E ₁₁ × p ₋₁ / p ₋₁ = −55 × 7/7 = −55

The adder unit 2 adds the input pixel data A ₂₁ and the average error data F ₂₁ and inputs the result as the added pixel data B ₂₁ to the threshold value comparison unit 3. Since the level 95 (= 150−55) of the added pixel data B ₂₁ is smaller than the threshold level 128, the threshold comparison unit 3 outputs level 0 as the output pixel data C ₂₁ and stores it in the output image memory 4.

At the same time, the error calculator 5 calculates the difference between the input pixel data A ₂₁ and the output pixel data C _21, and stores it in the error data memory 6 as error data E ₂₁ (= A ₂₁ −C ₂₁ ). In this case, the level of the error data E ₂₁ is 150. In this way, the binarization process of the second pixel data A ₂₁ is completed.

Next, the binarization process of the third pixel data A ₃₁ (level 80) will be described with reference to the explanatory view shown in FIG. In this case as well, processing is performed in consideration of the error data stored in the error data memory 6. That is, the binarized pixel data A ₁₁ and A from the error data memory 6 have already been binarized.
It reads the error data E ₁₁ and E ₂₁ of _21, the selector 8
The above-described average error calculation is performed using the parameter Pa selected in step 1, the average error data F ₃₁ is calculated, and the average error data F ₃₁ is output to the adder 2 as addition data.

In the present case, E ₁₁ = -55, E ₂₁ = 150,
Since p ₋₂ = 5 and p ₋₁ = 7, the average error data F ₃₁ is as follows. F ₃₁ = (E ₁₁ × p ₋₂ + E ₂₁ × p ₋₁ ) / (p ₋₂ + p ₋₁ ) = (− 55 × 5 + 150 × 7) / (5 + 7) = 65

The adder unit 2 adds the input pixel data A ₃₁ and the average error data F ₃₁ and inputs the result as the added pixel data B ₃₁ to the threshold value comparing unit 3. In the threshold comparison unit 3, since the level 145 (= 80 + 65) of the added pixel data B ₃₁ is higher than the threshold level 128, the level 255 is output as the output pixel data C ₃₁ and stored in the output image memory 4.

At the same time, the error calculator 5 calculates the difference between the input pixel data A ₃₁ and the output pixel data C _31, and stores it in the error data memory 6 as error data E ₃₁ (= A ₃₁ −C ₃₁ ). In this case, the level of error data E ₃₁ is -175
(= 80-255). Thus, the binarization processing of the third pixel data A ₃₁ is completed.

In the same manner, the fourth and subsequent pixel data are processed in the same manner. The above algorithm is summarized as follows. Multiply the parameter element of the position corresponding to the element of the error value and divide by the total number of the multiplied parameter elements. It is called the average error value. However, the error value of the first pixel is zero. The input image data and the average error value obtained from are added. The value obtained in step 1 is compared with the threshold level. If the value obtained in step 1 is smaller, the white level is set, and if it is larger, the black level is set, and the value is called an output value. The output value is subtracted from the value obtained in step 3, and this is taken as the error value. Move to the next pixel (on the right) and move to. When the pixel is moved to the rightmost pixel, the pixel is vertically lowered by one and returned to the leftmost. When the processes of to are performed for all the pixels, the process ends.

In this way, the binarization processing of each pixel stored in the input image memory 1 is executed. In this process,
As described above, when the selector 8 detects that certain specific pixel data are continuous, the selector 8 causes the five parameter groups Pa to stored in the parameter memory 7 to be stored.
Another parameter different from the currently used parameter Pa, for example, the parameter Pc, is selected from Pe, and the average error calculator 9 calculates the average error using the parameter Pc only for the current pixel of interest.

In the above-described embodiment, the continuation of certain specific pixel data is detected and another parameter group is selected. However, the present invention is not limited to this, and each time one pixel is processed. You may make it choose randomly from five parameter groups. Alternatively, when the input image is a color image, the parameter group may be changed only when the pixel processed immediately before and the pixel of interest currently processed are close in color.

[0042]

According to the present invention, a regular pattern is less likely to appear in a binary image, and moire is reduced. In addition,
By limiting to the case of processing the pixels in which moire is generated, only the portion in which moire is likely to occur can be processed, and the binarization processing in which the accuracy of the usual error diffusion method is not impaired and moire is less likely to occur can be realized.

[Brief description of drawings]

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

FIG. 2 is a matrix diagram showing an example of a parameter group.

FIG. 3 is an explanatory diagram for explaining the operation of the present invention.

FIG. 4 is an explanatory diagram for explaining the operation of the present invention.

FIG. 5 is an explanatory diagram for explaining the operation of the present invention.

FIG. 6 is an explanatory diagram for explaining an operation by a conventional error diffusion method.

[Explanation of symbols]

 1 Input Image Memory 2 Adder 3 Threshold Comparison 4 Output Image Memory 5 Error Calculator 6 Error Data Memory 7 Parameter Memory 8 Selector 9 Average Error Calculator Pa-Pe Parameter Group W Window Frame

Claims (3)

[Claims] 1. A parameter memory for storing a plurality of parameter groups, a selector for selecting one parameter group from the plurality of parameter groups, and a parameter and error data memory for the parameter group selected by the selector. An average error calculator that calculates average error data based on the error data, an adder that adds multi-tone input pixel data and the average error data, and addition output data from the adder with a predetermined threshold level. A threshold comparing unit that compares and converts to binary level data and outputs as binary pixel data; and 2 from the multi-gradation pixel data and the threshold comparing unit.
An image processing apparatus comprising: an error calculation unit that calculates an error caused by binarization processing based on the value pixel data as error data and stores the error data in the error data memory. 2. The image processing apparatus according to claim 1, wherein the selector monitors the level of the input pixel data and selects different parameter groups when pixels of a predetermined level are continuous. . 3. The selector monitors the input pixel data and selects a different parameter group when the pixel processed immediately before and the pixel of interest currently processed are of a similar color. The image processing apparatus according to item 1.