Marginal analysis prioritization for image compression based on a hierarchical wavelet decomposition

A novel approach for jointly optimizing scalar quantization and tree-based quantization of hierarchical wavelet decompositions is presented. An image compression algorithm is developed around this approach, utilizing a pruned-tree image representation. Marginal analysis is applied to optimize jointly the pruned-tree representation and scalar quantization. Simulation results demonstrate that the proposed algorithm offer substantially improved signal-to-noise ratio at matching bit rates, compared with similarly structured compression algorithms.<>