Reweighted compressive sampling for image compression

Authors:
Yi Yang;Oscar C. Au;Lu Fang;Xing Wen;Weiran Tang
Affiliations:
Department of Electronic and Computer Engineering, The Hong Kong University of Science and Technology, Kowloon, Hong Kong, China;Department of Electronic and Computer Engineering, The Hong Kong University of Science and Technology, Kowloon, Hong Kong, China;Department of Electronic and Computer Engineering, The Hong Kong University of Science and Technology, Kowloon, Hong Kong, China;Department of Electronic and Computer Engineering, The Hong Kong University of Science and Technology, Kowloon, Hong Kong, China;Department of Electronic and Computer Engineering, The Hong Kong University of Science and Technology, Kowloon, Hong Kong, China
Venue:
PCS'09 Proceedings of the 27th conference on Picture Coding Symposium
Year:
2009

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Abstract

Compressive Sampling (CS), is an emerging theory which points us a promising direction of designing novel efficient data compression techniques. However, the conventional CS adopts a non-discriminated sampling scheme which usually gives poor performance on realistic complex signals. In this paper we propose a reweighted Compressive Sampling for image compression. It introduces a weighting scheme into the conventional CS framework whose coefficients are determined in encoding side according to the statistics of image signals. Experimental results demonstrate that our proposed method notably outperforms the conventional Compressive Sampling framework in coding performance in the sense that the reconstruction quality is greatly enhanced with same number of measurements and computational complexity.