Adaptive image compression using sparse dictionaries
Inbal Horev, Ori Bryt, Ron Rubinstein · 2012
Transform coding is a widely used image compression tech-nique, where entropy reduction can be achieved by decom-posing the image over a dictionary which provides com-paction. Existing algorithms, such as JPEG and JPEG2000, utilize fixed dictionaries which are shared by the encoder and decoder. Recently, works utilizing content-specific dictionar-ies show promising results by focusing on specific classes of images and using highly specialized dictionaries. However, such approaches lose the ability to compress arbitrary images. In this paper we propose an input-adaptive compression approach, which encodes each input image over a dictionary specifically trained for it. The scheme is based on the sparse dictionary structure, whose compact representation allows relatively low-cost transmission of the dictionary along with the compressed data. In this way, the process achieves both adaptivity and generality. Our results show that although this method involves transmitting the dictionary, it remains competitive with the JPEG and JPEG2000 algorithms. Index Terms — Image compression, sparse representa-tion, dictionary learning, Sparse K-SVD, JPEG. 1.