PASCAL - Pattern Analysis, Statistical Modelling and Computational Learning

Approximating rate-distortion graphs of individual data: Experiments in lossy compression and denoising
Steven de Rooij and Paul Vitányi
IEEE Transactions on Information Theory 2006.

Abstract

Classical rate-distortion theory requires knowledge of an elusive source distribution. Instead, we analyze rate-distortion properties of individual objects using the recently developed algorithmic rate-distortion theory. The latter is based on the noncomputable notion of Kolmogorov complexity. To apply the theory we approximate the Kolmogorov complexity by standard data compression techniques, and perform a number of experiments with lossy compression and denoising of objects from different domains. We also introduce a natural generalization to lossy compression with side information. To maintain full generality we need to address a difficult searching problem. While our solutions are therefore not time efficient, we do observe good denoising and compression performance.

EPrint Type:Article
Project Keyword:Project Keyword UNSPECIFIED
Subjects:Computational, Information-Theoretic Learning with Statistics
ID Code:2744
Deposited By:Steven de Rooij
Deposited On:22 November 2006