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L constrained high-fidelity image compression via adaptive context modeling

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2 Author(s)
Xiaolin Wu ; Dept. of Comput. Sci., Univ. of Western Ontario, London, Ont., Canada ; Bao, P.

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

Published in:

Image Processing, IEEE Transactions on  (Volume:9 ,  Issue: 4 )