Lossless predictive coding with Bayesian treatment

Natural image statistics have been widely exploited for lossless predictive coding and other applications. However, traditional adaptive techniques always focus on the local consistency of training set regardless of what the predicted target looks like. We investigate the problem of introducing the model evidence of predicted target since self-similarity inherent in natural images gives some kind of prior information for the distribution of predicted result. The proposed Bayesian model integrated with both training evidence and target evidence takes full advantages of local structure as well as self-similarity. Experimental results demonstrate that the proposed context model achieves best results compared with the state-of-the-art lossless predictors.