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 ormation 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.