Tracking nonstationary probabilities in adaptive binary arithmetic coding

Adaptive arithmetic coders sometimes exhibit nonstationary symbol probabilities when coding digital halftone images with neighborhood-template models. If these nonstationary probabilities vary nonrandomly, the variations can be tracked robustly when each context derived from the coding model is expanded by conditioning on previously coded values for that model context.