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. Com-mon approaches for 2-D image compression like EZW (embed-ded wavelet zero tree) and SPIHT (set partitioning in hierarchi-cal trees) use a fixed significance-tree that captures well the inter-and intraband correlations of wavelet coefficients. For most 1-D signals like audio, such rigid coefficient correlations are not present. We address this problem by dynamically selecting an op-timal 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 outper-forms single-type tree coding schemes and performs comparable to the MPEG AAC coder while additionally achieving fine-grain scalability.