Repository landing page

We are not able to resolve this OAI Identifier to the repository landing page. If you are the repository manager for this record, please head to the Dashboard and adjust the settings.

Approaching the Rate-Distortion Limit With Spatial Coupling, Belief Propagation, and Decimation

Abstract

We investigate an encoding scheme for lossy compression of a binary symmetric source based on simple spatially coupled low-density generator-matrix codes. The degree of the check nodes is regular and the one of code-bits is Poisson distributed with an average depending on the compression rate. The performance of a low complexity belief propagation guided decimation algorithm is excellent. The algorithmic rate-distortion curve approaches the optimal curve of the ensemble as the width of the coupling window grows. Moreover, as the check degree grows both curves approach the ultimate Shannon rate-distortion limit. The belief propagation guided decimation encoder is based on the posterior measure of a binary symmetric test-channel. This measure can be interpreted as a random Gibbs measure at a temperature directly related to the noise level of the test-channel. We investigate the links between the algorithmic performance of the belief propagation guided decimation encoder and the phase diagram of this Gibbs measure. The phase diagram is investigated thanks to the cavity method of spin glass theory which predicts a number of phase transition thresholds. In particular, the dynamical and condensation phase transition temperatures (equivalently test-channel noise thresholds) are computed. We observe that: 1) the dynamical temperature of the spatially coupled construction saturates toward the condensation temperature and 2) for large degrees the condensation temperature approaches the temperature (i.e., noise level) related to the information theoretic Shannon test-channel noise parameter of rate-distortion theory. This provides heuristic insight into the excellent performance of the belief propagation guided decimation algorithm. This paper contains an introduction to the cavity method

Similar works

Full text

thumbnail-image

Infoscience - École polytechnique fédérale de Lausanne

redirect
Last time updated on 09/02/2018

Having an issue?

Is data on this page outdated, violates copyrights or anything else? Report the problem now and we will take corresponding actions after reviewing your request.