During the trial printing of digital printing color separation, the threshold can be adjusted in real-time based on the printing effect, ultimately achieving the best threshold and printing effect
Fruit. Due to the fact that fabrics are woven from various fibers, their warp and weft properties may differ, and if not considered, unexpected stripes may appear in the printed image. In order to prevent this situation from occurring, we have introduced two parameters to characterize the difference between the meridional and latitudinal directions, called the meridional coefficient x and the latitudinal coefficient y. This way, we can adjust these two coefficients to adapt to the differences in latitude and longitude attributes. Different output devices will have different resolutions and color tone values. Less inferior devices can represent fewer colors, while better devices can represent more.
Considering that the CMYK color model is actually the CMY color model, black is only used as a supplement. Therefore, in our color separation process, we first process the three colors of cyan, magenta, and yellow, and finally extract black from the three colors. 1) Definition of color separation process parameters: n-pixel matrix dimension, used to determine the grayscale level of the output image, grayscale level=rn+1; x. The warp coefficient and weft coefficient represent the different warp and weft properties of fabrics; The threshold coefficient of f, together with the gray level ratio, determines the threshold for color separation; Space grayscale ratio, the ratio of grayscale levels before and after color separation; Error, the deviation of pixel color grayscale before and after color separation; Valve color separation threshold, used to determine the number of shading points in the output image.
Input parameters: n-pixel matrix dimensions: threshold coefficients for three colors: fc, fM, and fY (0,1); x. Y meridional coefficient and latitudinal coefficient; Output result: The number of coloring points for four colors: Cv, Mv, Yv, Kv, cyan, magenta, yellow, and black. The steps of color separation processing: conversion of color model The transformation of color model is carried out according to the following formula: C=255-R; M=255-G;
Y=255-B, without considering black temporarily, determine the number of coloring points for three colors. Taking cyan as an example, let its color grayscale value be C. To determine the number of coloring points for this color, co, solve it according to the following method.
Find the intermediate variable: Error dispersion. The error generated by a certain pixel point after the second step of processing is error=C-COpace. In order to ensure the image effect, this error needs to be processed. The specific method is to disperse and accumulate the error in a certain proportion to surrounding points, so that it can be compensated on adjacent points.
The filtering algorithm diagram represents the pixel point to be processed, and the error value generated after the second step of processing is error. The error is dispersed according to the proportional coefficient in: 8/42 of the error is added to the first image point on the right, 4/42 is added to the second image point on the right, 2/42 is added to the second image point on the right of the next row, and so on. The error is dispersed and accumulated on the adjacent 12 related points for compensation. This is the error allocation scheme of the Stucki filtering algorithm. In addition, considering the differences in the warp and weft attributes of fabrics, we have modified the error dispersion coefficient of the Stucki filtering algorithm. The specific method is to multiply the coefficients in the figure with the warp and weft coefficients x and y, and use the product as the final error dispersion coefficient. The result of this can make the color separation result suitable for fabrics with different warp and weft attributes. The image processed by this method has a better output effect due to the involvement of a relatively large number of points.
Conclusion: The software designed according to the above method can perform color separation processing on images at various grayscale levels and simulate the processing results. In theory, the larger the dimension n of the pixel matrix, the more colors it can represent and the better the simulation effect. However, in actual printing, the image size will increase and the resolution will decrease. Therefore, the value of n should not be too large. Generally, when it is set at 3 or 4, the image output effect is already very good; In addition, by adjusting the threshold coefficient for color separation, the proportion of the four colors in the output image can be changed, thereby adjusting the overall color tone of the output image. In actual color separation, it is necessary to choose appropriate parameters according to different situations to achieve the best effect.
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