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We introduce a new image compression framework that combines compression efficiency with speed, and is based on an independent infinite mixture model which accurately captures the space-frequency characterization of the wavelet image representation. Specifically, we model individual image wavelet coefficients as being drawn from an independent generalized Gaussian distribution (GGD) field of zero mean and unknown spatially-varying variances. Based on this model, we develop a powerful estimation-quantization (EQ) framework that consists of: (i) first finding the maximum- likelihood estimate of the individual spatially-varying coefficient field variances based on causal and quantized spatial neighborhood contexts; and (ii) then applying an off-line rate-distortion (R-D) optimized quantization/entropy coding strategy, implemented as a fast lookup table, that is optimally matched to the derived variance estimates. A distinctive feature of our framework is the dynamic partitioning of wavelet data into subsets representing coefficients that are 'predictable' and 'unpredictable' respectively from their quantized causal contexts. The statistical parameters of the 'unpredictable' set in each subband, obtained through a fast, R-D based, simple thresholding first-pass operation, represent the negligible parametric side-information for use in the forward adaptation mode. The combination of the powerful infinite mixture model, the dynamic switching between forward and backward adaptation modes, and the theoretical soundness and speed of the EQ framework lead to a novel, high-performing, and fast image coder that is extremely competitive with the best published coders in the literature across all classes of images and target bit rates of interest, in both compression efficiency and processing speed. For example, our coder exceeds the objective performance of the best zerotree-based wavelet coder at all bit rates for all tested images at a fraction of its complexity. At mow to medium bit rates our preliminary results appear to exceed all reported results in the wavelet image coding literature to the best of our knowledge.
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