A switching predictor for lossless image coding

I n this paper, we propose a switching adaptive predictor (SWYP) with automatic context modeling for lossless image coding. In the SWAP system, two predictors are used. FOT areas with edges, estim.ates of coding pixels are obtained using texture wntext matching (TCM). For all other arras, an adaptive neural predictor (ANP) is used. The SWAP encoder switches between the two predictors ANP and TGM depending on the neighborhood of the coding pixel. The switching predictor allows statistic01 redundancy to be removed effectzuely. On llke otlrer. hand, it is known that prediction can be further refined using error compensation. For this, we propose the use of a modijied 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.