Transform image coding based on joint adaptation of filter banks and tree structures
Recent work on filter banks and related expansions has revealed an interesting insight: different filter bank trees can be regarded as different ways of constructing orthonormal bases for linear signal expansion. In particular, fast algorithms for finding best bases in an operational rate-distortion sense have been successfully used in image coding. Independently of this work, recent research has also explored the design of filter banks that optimize energy compaction for a single signal or a class of signals. In this paper we integrate these two different but complementary approaches to best-basis design and propose an image coder in which subband filter banks, tree structure and quantizers are chosen so as to optimize rate-distortion performance. These optimal filter banks, tree structure and quantizers represent side information. They are selected from a codebook designed from training data, using a rate-distortion criterion.