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 …