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Three-dimensional interconnection scheme for color error diffusion

机译:彩色误差扩散的三维互连方案

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This is a generalization, to color images, of earlier results on two-dimensional monochromatic halftoning with error diffusion neural networks (EDNs). Previously, we have shown that EDNs find local minima of frequency-weighted error between a binary halftone output and corresponding smoothly varying input, which is an ideal framework for solving halftone problems. We cast color halftoning as four related subproblems: the first three are to compute good binary halftones for each primary color and the fourth is to simultaneously minimize frequency-weighted error in the luminosity of the composite result. We show that an EDN with a three-dimensional (3D) interconnection scheme can solve all four problems in parallel. The 3D EDN algorithm not only shapes the error to frequencies to which the human visual system (HVS) is least sensitive but also shapes the error in colors to which the HVS is least sensitive-namely it satisfies the minimum brightness variation criterion. The correlation among the color planes by luminosity reduces the formation of high contrast pixels, such as black and white pixels that often constitute color noise, resulting in a smoother and more homogeneous appearance in a halftone image and a closer resemblance to the continuous tone image.
机译:对于彩色图像,这是对带有误差扩散神经网络(EDN)的二维单色半色调的早期结果的概括。以前,我们已经证明EDN在二进制半色调输出和相应的平滑变化输入之间找到频率加权误差的局部最小值,这是解决半色调问题的理想框架。我们将颜色半色调转换为四个相关的子问题:前三个是为每种原色计算好的二进制半色调,而第四个是同时最小化合成结果的光度中的频率加权误差。我们表明,具有三维(3D)互连方案的EDN可以并行解决所有四个问题。 3D EDN算法不仅将误差整形为人类视觉系统(HVS)最不敏感的频率,而且还整形了HVS最不敏感的颜色误差-即它满足最小亮度变化标准。通过亮度在颜色平面之间的相关性减少了高对比度像素(例如经常构成颜色噪声的黑白像素)的形成,从而导致半色调图像中的外观更平滑,更均匀,并且与连续色调图像更相似。

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