Tone-dependent error diffusion based on an updated blue-noise model

The conventional blue-noise model that specifies the desired noise characteristics of an ideal halftone has been updated recently, and simulation results showed that the updated model can serve as a better guideline for developing halftone algorithms. At the moment, only a feature-preserving multiscale error diffusion-based algorithm was developed based on the updated noise model. As the algorithm does not support real-time applications, a tone-dependent error diffusion (TDED) algorithm is developed based on the updated noise model. To support the proposed TDED algorithm, we optimize a diffusion filter and a quantizer threshold for each possible input gray level based on the updated noise model, such that the algorithm can adapt its diffusion filter and quantizer according to the input intensity value of a pixel to produce a halftone. Simulation results showed that the proposed TDED algorithm can successfully produce halftones bearing the desired noise characteristics as specified by the updated noise model. As a consequence, it provides better performance than conventional error diffusion-based algorithms in terms of various measures including radially averaged power spectrum density and anisotropy. When processing real images, it can eliminate directional artifacts, regular structure patterns, and unintended sharpening effects in its halftoning outputs.