Lossless image coding using a switching predictor with run-length encodings

In this paper, we propose a switching adaptive predictor (FSWAP) with run-length encoding for lossless image coding. The proposed FSWAP system has two operation modes; run mode and regular mode. If the members in the texture context of the coding pixel have identical grey values, the run mode is used, otherwise the regular mode is used. The run mode, using run-length coding, with an arithmetic coder, is very useful for images with flat regions. The regular mode borrows the switching predictor structure in SWAP (Lih-Jen Kau et al, IEEE Trans. Fuzzy Systems) with some modifications. Experiments show that simplified context clustering is very useful in error modeling for prediction refinement. Furthermore, the execution time of FSWAP can be accelerated with minor degradation in the bit rates associated with the modifications. Comparisons of the proposed system to existing state-of-the-art predictive coders are given to demonstrate its coding efficiency