Adaptive wavelet packet image coding using an estimation-quantization framework

In this paper, we extend the statistical model-based Estimation-Quantization (EQ) wavelet image coding algorithm introduced in [7] 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 [7] 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 in [7] 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.