Boosted multi-scale dictionaries for image compression

Abstract

Sparse representations over redundant dictionaries have shown to produce high quality results in various signal and image processing tasks. Recent advancements in learning of the sparsifying dictionaries have made image compression based on sparse representation a promising field. In this paper, we present a boosted dictionary learning framework to construct an ensemble of complementary specialized dictionaries for sparse image representation. Boosted dictionaries along with a competitive sparse coding can provide us with more efficient sparse representations. Based on the proposed ensemble model, we then develop a new image compression algorithm using boosted multi-scale dictionaries learned in the wavelet domain. Our algorithm is evaluated for compression of natural images. Experimental results demonstrate that the proposed algorithm has better rate-distortion performance as compared with several competing compression methods including analytic and learned dictionary schemes.