A switching predictor for lossless image coding

In this paper, we propose a switching adaptive predictor (SWAP) with automatic context modeling for lossless image coding. In the SWAP system, two predictors are used. For areas with edges, estimates of coding pixels are obtained using texture context matching (TCM). For all other areas, an adaptive neural predictor (ANP) is used. The SWAP encoder switches between the two predictors ANP and TCM depending on the neighborhood of the coding pixel. The switching predictor allows statistical redundancy to be removed effectively. On the other hand, it is known that prediction can be further refined using error compensation. For this, we propose the use of a modified fuzzy clustering, which leads to a modeling of errors that adapts itself to the input statistics. Experiments show that the proposed context clustering is very useful in modeling error for prediction refinement. Comparisons of the proposed system to existing state-of-the-art predictive coders will be given to demonstrate its coding efficiency.