Approximating Rate-Distortion Graphs of Individual Data: Experiments in Lossy Compression and Denoising

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dc.creatorde Rooij, Steven
dc.creatorVitanyi, Paul
dc.date2006-09-21
dc.date.accessioned2026-07-07T08:18:02Z
dc.date.available2026-07-07T08:18:02Z
dc.descriptionClassical 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.
dc.description22 pages, submitted to IEEE transactions on information theory
dc.identifierhttps://arxiv.org/abs/cs/0609121
dc.identifierhttp://arxiv.org/abs/cs/0609121
dc.identifier.urihttp://salesiana.dossiersoluciones.com/handle/123456789/134293
dc.subjectInformation Theory
dc.subjectE.4; H.1.1
dc.titleApproximating Rate-Distortion Graphs of Individual Data: Experiments in Lossy Compression and Denoising
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