Marginal analysis prioritization for image compression based on a hierarchical wavelet decomposition
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Pyramidal and hierarchical image decompositions have been widely used for image compression in conjunction with a variety of quantization schemes. Recently, a number of promising tree-structured quantization strategies have been proposed which exploit correlation between coefficients at different levels of the decomposition. This paper addresses optimal bitrate allocation between scalar and tree-structured quantization of hierarchical image representations. We propose an image compression algorithm based on a pruned-tree image representation with scalar quantization of coefficients associated with the tree nodes. Marginal analysis is applied to jointly optimize the pruned-tree representation and scalar quantizers.