Near-lossless image compression schemes based on weighted finite automata encoding and adaptive context modelling
We study high-fidelity image compression with a given tight bound on the maximum error magnitude. We propose a weighted finite automata (WFA) recursive encoding scheme on the adaptive context modelling based quantizing prediction residue images. By incorporating the proposed recursive WFA encoding techniques into the context modelling based nearly-lossless CALIC (context based adaptive lossless image codec), we were able to increase its PSNR by 1.5 dB or more and get compression rates 15 per cent or better than the original CALIC. By combining wavelet methods and WFA encoding, we were able to obtain competitive PSNR results against the best wavelet coders in both L/sub 2/ and L/spl infin/ metrics, while obtaining much smaller maximum error magnitude than the latter.