IAVQ-interval-arithmetic vector quantization for image compression

Abstract

Interval arithmetic (IA) can enhance vector quantization (VQ) in image-compression applications. In the interval arithmetic vector quantization (IAVQ) reformulation of classical VQ, prototypes assume ranges of admissible locations instead of being clamped to specific space positions. This provides the VQ-reconstruction process with some degrees of freedom, which do not affect the overall compression ratio, but help make up for coarse discretization effects. In image compression, IA attenuates artifacts (such as blockiness) brought about by the VQ schema. This paper describes the algorithms for both the training and the run-time use of IAVQ. Data-driven training endows the methodology with the adaptiveness of standard VQ methods, as confirmed by experimental results on real images.