L?-Constrained near-lossless image compression using weighted finite automata encoding
In this paper we study high-fidelity image compression with a given tight bound on the maximum error magnitude. We propose an L?-constrained weighted finite automata recursive encoding scheme on the adaptive context modelling based quantizing prediction residue images. By incorporating the proposed L?-constrained WFA encoding techniques into the context modelling based nearly-lossless CALIC, we were able to increase its PSNR by 2 dB or more and obtain bit rates 29% lower than the original CALIC. When applying the wavelet transform for the residue images and then applying the WFA encoding on the wavelet coefficients, we were able to obtain competitive PSNR results against the best wavelet coders in L? metrics, while obtaining much smaller maximum error magnitude than the latter. In particular, when the error bound is <3, the L?-constrained wavelet WFA coder were able to obtain 1.3416.75 dB higher PSNR on the standard ISO test benchmarks than the SPIHT, one of the best wavelet coder.
near-lossless compression weighted finite automata wavelet context-based