An adaptive tree-based progressive audio compression scheme

A fine-grain scalable and efficient audio compression scheme based on adaptive significance-trees is presented. Common approaches for 2-D image compression like EZW (embedded wavelet zero tree) and SPIHT (set partitioning in hierarchical trees) use a fixed significance-tree that captures well the inter- and intraband correlations of wavelet coefficients. For 1-D audio signals, such rigid coefficient correlations are not present. We address this problem by dynamically selecting an optimal significance-tree for the actual audio frame from a given set of possible trees. Experimental results are given, showing that this coding scheme outperforms single-type tree coding schemes and performs comparable to the MPEG AAC coder while additionally achieving fine-grain scalability.