A Markovian framework for digital halftoning

Abstract

A mathematical framework for digital halfloning is proposed. Two models for digital halftoning are provided, one based on maximum-entropy Gibbs measures and one based on reversible Markov chains. The models are seen to be equivalent. This equivalence induces an equivalence between two associated halftoning algorithms, one based on neural networks and one based on simulated annealing. These algorithms are seen to provide halftone images that are preferable to those obtained by standard techniques.