Adaptive wavelet packet image coding using an estimation-quantization framework

We extend the statistical model-based estimation-quantization (EQ) wavelet image coding algorithm introduced by LoPresto, Ramchandran and Orchard (see Proceedings of the Data Compression Conference, Snowbird, UT, 1997) to include an adaptive transform component. For this, we resort to the rich, space-frequency diverse, and easy-to-search library of transforms provided by the family of wavelet packet (WP) bases and their adaptive extensions. We use rate-distortion criteria to find the best basis jointly with the statistical model-based best adaptive quantization and entropy coding strategy of LoPresto et al. based on an efficient and fast tree pruning algorithm. A key underlying attribute of our paradigm is that the spatially-varying generalized Gaussian mixture model for wavelet coefficients introduced by LoPresto et al. is also applicable to the more arbitrary framework of (adaptive) wavelet packet transform coefficients as well. Our WP-EQ framework produces excellent results on standard test images. The most attractive property of our paradigm is its “universality” and robustness: based on an overall performance criterion that considers diverse classes of input test images that have varying space-frequency characteristics, it is more powerful than most of the existing image coding algorithms, using reasonable complexity, and a generic, integrated, non-training based framework