L-infinity constrained high-fidelity image compression via adaptive context modeling
In this paper, vee study high-fidelity image compression with a given tight L-infinity bound. We propose some practical adaptive context modeling techniques to correct prediction biases caused by quantizing prediction residues, a problem common to the existing DPCM-type predictive near-lossless image coders. By incorporating the proposed techniques into the near-lossless version of CALIC that is considered by many as state-of-the-art algorithm, me mere able to increase its PSNR by 1 dB or more and/or reduce its bit rate by 10% or more. More encouragingly, at bit rates around 1.25 bpp or higher, our method obtained competitive PSNR results against the best L-2-based wavelet coders, while obtaining much smaller L-infinity bound.
context modeling of images entropy coding near-lossless image compression predictive coding trellis quantization