JPS63204375A - Half tone picture forming method - Google Patents

Abstract

PURPOSE:To form a half tone picture without a deterioration in gradation reproducibility even by using a small matrix by correcting the variable density value of a picture element at a current scanning position according to the weighted averaged value of an error between the variable density value of an input and the variable density value of an output. CONSTITUTION:The variable density value of data from a picture input device 1 or a picture memory 2 is converted to the area of the picture element by a picture processor 3. The error between the variable density value of the input and the variable density value of the output is operated for every picture element of an input picture and this error is stored in an error buffer disposed in the picture buffer processor 3. The error of the three picture elements in the vicinity of the two dimension of a scanning picture element is multiplied by a weight coefficient and summed up and it is defined to be the quantity of ER of the error to be corrected by the scanning picture. Then, the quantity ER of the error is subtracted from the variable density value of the current scanning picture element to obtain the final variable density value after the correction, this final variable density value is compared with a preset threshold matrix to obtain an output pattern. Thereby, the half tone picture in which the error as the average is corrected can be obtained.

Description

DETAILED DESCRIPTION OF THE INVENTION [Field of Industrial Application] The present invention relates to a method for comparing and binarizing a dot matrix threshold pattern and an original image gray level signal to generate a halftone image.

[Conventional technology]

For example, various methods are employed to reproduce halftone images in image output devices capable of only black and white binary output. One such method is to compare the dot matrix threshold pattern and the original image density signal and binarize the image to generate a halftone image. Known methods for generating halftone images using this dot matrix threshold pattern include the dither method and the density pattern method.

As shown in FIGS. 5 and 6, these are calculated by comparing the gray scale values of the input data (see (a) in each figure) and the dot matrix threshold pattern (see (c) in each figure). If the former is larger or equal, the output is turned on or off and black (indicated by hatching in the figure) is output; if the former is smaller, it is turned off or on and white is output, and as shown in each figure (C), the output is turned on or off to output black. This is an attempt to reproduce a tone image.

Furthermore, Figure 5 shows the dither method, and Figure 6 shows the density pattern method, and the dot matrix darkness value pattern used is 6X.
6 in size and can display 36 gradations.

However, in this method of generating halftone images using a dot matrix, when trying to reproduce good gradations without false contours, the resolution decreases, and conversely, when trying to increase the resolution, gradation reproducibility deteriorates. There was a drawback.

This is because in order to increase the number of gradations that can be displayed, the dot matrix must be made larger, but this increases the distance between the centers of the dot matrix, resulting in a decrease in resolution and gradation reproducibility. This is because there is a reciprocal relationship.

FIG. 7 is an explanatory diagram showing the drawbacks caused by the conventional halftone image generation method. Note that here, the 8-bit gray scale value having 0 to 2550 gradations (Fig. 7(a) @Shi) is set to 0.
An example will be shown in which the output pattern shown in FIG. 7(C) is obtained by binarizing using a 4×4 threshold matrix (see FIG. 7 υ) that can express 17 gradations including .

Here, if we consider the gray value actually output for the input gray value r131J, the pixels up to the eighth pixel corresponding to the threshold r120J counting from the lowest one in the threshold matrix will be on, that is, black. The output gray value was (8/16) x255 = 127.5, and a quantization error of 131-127.5 = 3.5 occurred between it and the input gray value 131. Depending on the degree of this error, false contours and the like occur. This phenomenon is particularly noticeable in the case of the so-called partial dot method, in which the threshold value MA) has a larger correspondence than the input pixel, as shown in FIG.

As can be seen on the right side of Fig. 8, when the gradation values shown in Fig. 8 (a) are binarized using the threshold values shown in Fig. 8 (m), the output pattern is shown in Fig. 8 (C). You will be able to do it. Therefore, for each input pixel ■ to ■ shown in FIG.
, 75, 43 errors will occur.

In other words, in the case of an input pixel ■ with a gray value of 1250, the gray value is 125
-(2/4)
In the case of an input pixel ■ with a 00 Å power pixel, 60− (1/4) X255 = −3, and in the case of an input pixel ■ with a gradation value of 43, 43− (0/4) X255 = 43.

As described above, the conventional halftone image generation method using a dot matrix has the drawback that as the threshold matrix used becomes smaller, the error between input and output increases, resulting in poor tone reproduction.

To solve the above problems, error diffusion method is used.

Methods such as multiple-throw division quantization have been proposed.

(Ono Text, Journal of the Institute of Image Electronics Engineers, Vol. 10, No. 5 (19
81) See P3g8-397 "Didi method").

These basically aim to realize high-fidelity reproduction in both resolution and gradation reproducibility by distributing dots so that the density of the input original and the density of the output image match as much as possible.

[Problem that the invention seeks to solve]

However, in the output images obtained by these methods, the dots may be dispersed depending on the density of the original, or may form regular patterns, resulting in images that are sometimes grainy and unsightly. This effect was especially noticeable when outputting a color image, and it was not possible to obtain a satisfactory image quality.

In view of the above problems, the present invention provides a method for generating halftone images using a dot matrix, which is capable of generating halftone images without deterioration in tone reproducibility even when using a smaller matrix than conventional methods. The purpose is to provide an image generation method.

[Means and operations for solving the problem] 'In order to achieve the above-mentioned object, the present invention binarizes the input original image density signal by comparing it with a dot matrix threshold pattern,
In the halftone image generation method that outputs an image according to the original image gradation signal, the error between the input gradation value and the output gradation value is sequentially determined corresponding to each scanning position, and the error in the vicinity of the pixel at the current scanning position is The present invention is characterized in that a weighted average value of the errors is determined, and the gray value of the pixel at the current scanning position is corrected using the weighted average value.

In the present invention, quantization errors caused by binarization for generating halftone images are averaged using a weighted window of a certain size. The gray value of the current scan pixel is corrected using the weighted average value thus determined. therefore,
The reproduced image has quantization errors corrected as a whole.

〔Example〕

DETAILED DESCRIPTION OF THE INVENTION Hereinafter, features of the present invention will be specifically described based on examples with reference to the drawings.

FIG. 2 shows a schematic block diagram of the entire image processing apparatus for implementing the halftone image generation method according to the present invention.

In the figure, 1 is an image input device for inputting images using, for example, a CCD (charge-coupled device), which inputs document information from 0 to 25,508 bits at a resolution of approximately 160 dots/cm (400 dots/inch). It is read as gradation value data. The read 8-bit document information is stored in an image memory 2 having a capacity to store one page of A4 size. The 8-bit grayscale value of the data from the image input device 1 or the image memory 2 is converted into the area of a pixel by the image processing device 3, and is also converted into a halftone image by the image output device 5, which can only output binary values of 0.1. be able to reproduce it. Signal control of these devices as a whole is performed by a control device 4. The image output device 5 records a binary image at a resolution of approximately 310 dots/cm (800 dots/inch), for example. In this embodiment, an electrophotographic laser beam printer is used as the image output device 5, but a thermal printer, an inkjet printer, etc. may also be used.

Next, the operation of the image processing device barrel 1 will be explained with reference to the flowchart of FIG.

In addition, in this example, the human image has grayscale values d(i-Lj-1) and d(Lj-ILdl) as shown in Table 1.
+1. J-1), d(l-1,j),'(LJ)+
d(to1.j). here,
It is assumed that l indicates the position of the scanning point in the main scanning direction, and j indicates the position of the scanning point in the sub-scanning direction.

Table 1 First, the position of the scanning point in the sub-scanning direction is set to the initial position (step 101). Next, an error is calculated for each pixel of the above-mentioned human image, and this error is stored in an error buffer (not shown) provided in the image processing bag [13]. Then, the error of three pixels in the two-dimensional vicinity of the scanning pixel, e (i-L
j−1)+e(i, j−1).

e(i-1,j) l: weighting coefficient m,, W, ,
The sum is multiplied by Wj and used as the error amount ER that must be corrected by the scanning pixel (step 102).

That is, ER=L-e(i-to j-1)+112-e(i, j-1
) 10W, -e(i-1,j).

Now, as shown in Table 1, each gray value is d(i-1,j
-1) = 50 d(i, j-1) = 93 d (i-1, 0 = 62, and by comparing these grayscale values with the threshold matrix shown in Fig. 4(a), we can reproduce halftones. , each error is the second
As shown in the table, e(i-1, j-1> -50-(3/16) X25
5 = 2.2e(i, j-1) =93- (6/
16) X255 = -2,6e(i-1,j) =
62-(4/16)X255=-L8.

Table 2 Table 3 Here, each weighting coefficient is as shown in Table 3, n, =
0.2. L = 0.4. If L = 0.4, ER=0.2x2.2+0.4x(-2,6)+0
．． 4X(-1,8)'=-1,3.

Note that the x mark on the right side of Tables 2 and 3 indicates the position of the scanning point.

In this embodiment, the error amount ER was determined using three pixels in the vicinity of the scanning pixel, but this was set based on a balance between processing speed and image quality, and the number of reference pixels is not limited to this. The same applies to the weighting coefficient values.

Next, from the gray value d(i,l) of the current scanning pixel, the error amount ER
(calculate the final shading value after correction (step 1)
03). In the case of this example, D=d(i,j)-ER? 80-(-1,3)=81.3.

Next, this final shading value is compared with the preset threshold matrix shown in FIG. 4(a) to obtain the output pattern shown in FIG. 4(c) (step 1).
03). This pattern is displayed on the image output device m! If output at 5, a halftone image can be obtained.

On the other hand, at this time as well, an error e(i, j) occurs between the actually output gray value D0 and the final gray value, so this is stored in the error buffer 11 (step 105).
．． 106). In the case of this example, D, = (5/16)
Since X255 = 79.7, e(i, j) = 81.3-79.7 = 1.6.

Execute the above operations in the main scanning direction for each input pixel (steps 107 and 108), and when the processing in the main scanning direction is completed, return the main scanning position to the initial position and advance the sub scanning position by one, Processing in the main scanning direction is restarted (steps 109, 110, and 111).

In this way, by repeating the process for the entire screen, it is possible to obtain a halftone image with average errors corrected.

FIG. 3 shows an example of a circuit when the above processing is realized by hardware.

In the error amount calculation circuit 11, three pixels in the two-dimensional vicinity of the scanning pixel are
Pixel error, e(i-1, j-1), e(i, j-1
).

Weighting coefficients W,, W,, W for e(i-1,j)
, and then total the error amount ER. Then, in the subtraction circuit 13, the gray value d (i,
The error amount ER is subtracted from j) to obtain the final gradation value after correction. The comparison circuit 14 compares this final gradation value with a preset threshold matrix 15 shown in FIG. 4(a).

The output of the comparison circuit 14 is sent to the image output device 5 (see FIG. 2).
The output pattern shown in FIG. 40 is obtained. Furthermore, in the subtraction circuit 16, the error e(L, +) between the actually output gray value Do and the final gray value is determined, and this is stored in the error buffer 11, and the error amount described above is calculated. This is an error necessary for calculation in the calculation circuit 12.

In this embodiment, the case of the density pattern method in which one input pixel is expressed by one threshold value matrix pattern was explained as an example, but the partial dot method in which several input pixels are expressed by one threshold value matrix pattern is also used. , can also be applied to the dither method.

Furthermore, the halftone image generation method shown in this example can also be applied to a color image recording device, in which a threshold pattern having a different screen angle for each color is used to prevent moiré, etc. However, it is possible to generate an image without damaging the shape of the threshold pattern.

〔Effect of the invention〕

As described above, in the present invention, a correction value is obtained by averaging the quantization error caused by binarization in the vicinity of a scanning pixel using a weighted window of a certain size, and this correction value is used to calculate the gray level value of the current scanning pixel. I am trying to correct it. This reduces the average error and improves gradation reproduction. That is, in the present invention, since the correction is performed based on the pattern of the threshold matrix, noise patterns and regular patterns that are observed in error diffusion methods and the like do not occur. Therefore, even if a small-sized threshold matrix pattern with a small number of gradations that can be expressed is used, false contours will not occur, so it is possible to reproduce the original image with high resolution.

[Brief explanation of the drawing]

FIG. 1 is a flowchart showing an example of an error correction procedure in a halftone image generation method according to the present invention, FIG. 2 is a schematic block diagram showing the entire image processing device, and FIG. 3 is a halftone image generation method according to the present invention. 4 is an explanatory diagram showing the relationship between the threshold value matrix and the output pattern; FIG. 5 is an explanatory diagram showing halftone image generation using the conventional dither method; FIG. is an explanatory diagram showing halftone image generation using the conventional density pattern method, and FIG. 7 is a diagram for explaining the occurrence of errors in the density pattern method.
FIG. 8 is an explanatory diagram showing halftone image generation using the conventional partial dot method. Patent applicant: Fuji Xerox Co., Ltd. Agent
Masu Kobori (and 2 others) Figure 1 Figure 2 Figure 3 Figure 5 Figure 6 (b) (b) (c) (c) Figure 7 (a) (b) (c) @ Figure 8 (cl) (b) (c)

Claims (1)

[Claims] 1. In a halftone image generation method that compares an input original image gradation signal with a dot matrix threshold pattern, binarizes it, and outputs an image according to the original image gradation signal, the difference between the input gradation value and the output gradation value is determined. 1. Sequentially finding errors between the pixels corresponding to each scanning position, finding a weighted average value of the errors near the pixel at the current scanning position, and correcting the gray level value of the pixel at the current scanning position using the weighted average value. A halftone image generation method characterized by: