Rate-distortion based sparse coding for image set compression
In this paper, we propose a novel image set compression approach based on sparse coding with an ordered dictionary learned from perceptually informative signals. For a group of similar images, one representative image is first selected and transformed into wavelet domain, and then its AC components are utilized as samples to train an over-complete dictionary. In order to improve compression efficiency, the dictionary atoms are reordered according to their frequency used in sparse approximation of the representative image. In addition, a rate-distortion based sparse coding method is proposed to distribute atoms among different image patches adaptively. Experimental results show that the proposed method outperforms JPEG and JPEG2000 up to 6+ dB and 2+ dB, respectively.