Quantization table design revisited for image/video coding.

Abstract

Quantization table design is revisited for image/video coding where soft decision quantization (SDQ) is considered. Unlike conventional approaches, where quantization table design is bundled with a specific encoding method, we assume optimal SDQ encoding and design a quantization table for the purpose of reconstruction. Under this assumption, we model transform coefficients across different frequencies as independently distributed random sources and apply the Shannon lower bound to approximate the rate distortion function of each source. We then show that a quantization table can be optimized in a way that the resulting distortion complies with certain behavior. Guided by this new design principle, we propose an efficient statistical-model-based algorithm using the Laplacian model to design quantization tables for DCT-based image coding. When applied to standard JPEG encoding, it provides more than 1.5-dB performance gain in PSNR, with almost no extra burden on complexity. Compared with the state-of-the-art JPEG quantization table optimizer, the proposed algorithm offers an average 0.5-dB gain in PSNR with computational complexity reduced by a factor of more than 2000 when SDQ is OFF, and a 0.2-dB performance gain or more with 85% of the complexity reduced when SDQ is ON. Significant compression performance improvement is also seen when the algorithm is applied to other image coding systems proposed in the literature.