Design and analysis of vector color error diffusion halftoning systems

Traditional error diffusion halftoning is a high quality method for produci ng binary images from digital grayscale images. Error diffusion shapes the quantization noise power into the high frequency regions where the human ey e is the least sensitive. Error diffusion may be extended to color images b y using error filters with matrix-valued coefficients to take into account the correlation among color planes. For vector color error diffusion, we pr opose three contributions. First, we analyze vector color error diffusion b ased on a new matrix gain model for the quantizer, which linearizes vector error diffusion. The model predicts key characteristics of color error diff usion, esp. image sharpening and noise shaping. The proposed model includes linear gain models for the quantizer by Ardalan and Paulos and by Kite et al.as special cases. Second, based on our model, we optimize the noise shap ing behavior of color error diffusion by designing error filters that are o ptimum with respect to any given linear spatially-invariant model of the hu man visual system. Our approach allows the error filter to have matrix-valu ed coefficients and diffuse quantization error across color channels in an opponent color representation. Thus, the noise is shaped into frequency reg ions of reduced human color sensitivity. To obtain the optimal filter, we d erive a matrix version of the Yule-Walker equations which we solve by using a gradient descent algorithm. Finally, we show that the vector error filte r has a parallel implementation as a polyphase filterbank.