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Abstract : |
We introduce a new image compression paradigm 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 image wavelet coefficients as being drawn from an independent Generalized Gaussian distribution field, of fixed unknown shape for each subband, having zero mean and unknown slowly spatiallyvarying variances. Based on this model, we develop a powerful "on the fly" 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 pardigm is the dynamic switching between forward and backward adaptation, |
