IAVQ: Interval-Arithmetic Vector Quantization for Image Compression Sandro Ridella, Stefano Rovetta, and Rodolfo Zunino IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS II: ANALOG AND DIGITAL SIGNAL PROCESSING December 2000, Volume 47, Number 12 Pages 1378-1390 Abstract- Interval Arithmetic (IA) can enhance Vector Quantization (VQ) in image- compression applications. In the 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. The 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.