test_moissonnage_selune
type: Agent

Source coding with side information and uncertain correlation knowledge
type: Dataset
contact point: Organization
  fn: CCSD
keyword: ldpc-codes
keyword: codes-ldpc
keyword: modeles-de-markov-caches
keyword: codage-de-sources-avec-information-adjacente
keyword: evolution-de-densite
keyword: compression
keyword: hidden-markov-models
keyword: physcondcm-genphysics-physicscondensed-matter-cond-matother-cond-matother
keyword: theses
keyword: information-theory
keyword: infoeu-reposemanticsdoctoralthesis
keyword: density-evolution
keyword: theorie-de-linformation
keyword: source-coding-with-side-information
landing page: https://theses.hal.science/tel-01136663
description: In this thesis, we considered the problem of source coding with side information available at the decoder only. More in details, we considered the case where the joint distribution between the source and the side information is not perfectly known. In this context, we performed a performance analysis of the lossless source coding scheme. This performance analysis was realized from information theory tools. Then, we proposed a practical coding scheme able to deal with the uncertainty on the joint probability distribution. This coding scheme is based on non-binary LDPC codes and on an Expectation-Maximization algorithm. For this problem, a key issue is to design efficient LDPC codes. In particular, good code degree distributions have to be selected. Consequently, we proposed an optimization method for the selection of good degree distributions. To finish, we considered a lossy coding scheme. In this case, we assumed that the correlation channel between the source and the side information is described by a Hidden Markov Model with Gaussian emissions. For this model, we performed again some performance analysis and proposed a practical coding scheme. The proposed scheme is based on non-binary LDPC codes and on MMSE reconstruction using an MCMC method. In our solution, these two components are able to exploit the memory induced by the Hidden Markov model.
identifier: NNT: 2013PA112301
issued: 2026-05-06T03:54:00.634601
language: fr
modified: 2026-05-06T03:54:00.634606

Source coding with side information and uncertain correlation knowledge
type: Distribution
access URL: https://theses.hal.science/tel-01136663
format: HTML
issued: 2026-05-06T03:54:00.648689
modified: 2026-05-06T03:54:00.615067