L∞-constrained high-fidelity image compression via adaptive context modeling

We study high-fidelity image compression with a given tight bound on the maximum error magnitude. We propose some practical adaptive context modeling techniques to correct prediction biases caused by quantizing prediction residues, a problem common to the current DPCM-like predictive nearly-lossless image coders. By incorporating the proposed techniques into the nearly-lossless version of CALIC, we were able to increase its PSNR by 1dB or more and/or reduce its bit rate by ten per cent or more. More encouragingly, at bit rates around 1.25 bpp our method obtained competitive PSNR results against the best wavelet coders, while obtaining much smaller maximum error magnitude.