An Efficient Fine-grain Scalable Compression Scheme for Sparse Data

A fine-grain scalable and efficient compression scheme for sparse data 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 interand intraband correlations of wavelet coefficients. For most 1D signals like audio, such rigid coefficient correlations are not present. We address this problem by dynamically selecting an optimal significance-tree for the actual data frame from a given set of possible trees. Experimental results on sparse representations of audio signals 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.