Near-lossless image compression by relaxation-labelled prediction
Signal Processing - Image and Video Coding beyond Standards
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Proceedings of the 2007 ACM symposium on Applied computing
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IEEE Transactions on Information Theory
A near-lossless image compression algorithm using vector quantization
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We study high-fidelity image compression with a given tight L∞ 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 the state-of-the-art algorithm, we were 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 L2-based wavelet coders, while obtaining much smaller L∞ bound