Title :
L∞ constrained high-fidelity image compression via adaptive context modeling
Author :
Wu, Xiaolin ; Bao, Paul
Author_Institution :
Dept. of Comput. Sci., Univ. of Western Ontario, London, Ont., Canada
fDate :
4/1/2000 12:00:00 AM
Abstract :
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
Keywords :
adaptive signal processing; data compression; image coding; prediction theory; quantisation (signal); CALIC; DPCM predictive near-lossless image coders; L∞ bound; L∞ constrained high-fidelity image compression; L2-based wavelet coders; PSNR; adaptive context modeling; bit rate reduction; prediction bias correction; prediction residues quantization; Biomedical imaging; Bit rate; Context modeling; Entropy coding; Image coding; PSNR; Predictive coding; Predictive models; Quantization; Wavelet transforms;
Journal_Title :
Image Processing, IEEE Transactions on
