Augmenting vector quantization with interval arithmetics for image-coding applications

Interval Arithmetic (IA) augments the basic Vector-Quantization (VQ) paradigm for image compression. The reformulated VQ scheme allows prototypes to assume ranges of admissible locations rather than be clamped to specific space positions. The image-reconstruction process exploits the resulting degrees of freedom to make up for the excessive discretization (such as blockiness) that often affects VQ-based coding. The paper describes the algorithms for both the training and the run-time use of IAVQ codebooks; the possibility of data-driven training endows the proposed methodology with the flexibility and adaptiveness of standard VQ methods, as confirmed by experimental results on real images.