On the Heegard-Berger problem with common reconstruction constraints

Behzad Ahmadi, Ravi Tandon, Osvaldo Simeone, H. Vincent Poor

Research output: Chapter in Book/Report/Conference proceedingConference contribution

1 Scopus citations

Abstract

In lossy source coding with side information at the decoder (i.e., the Wyner-Ziv problem), the estimate of the source obtained at the decoder cannot be generally reproduced at the encoder, due to its dependence on the side information. In some applications this may be undesirable, and a Common Reconstruction (CR) requirement, whereby one imposes that encoder and decoder be able to agree on the decoder's estimate, may be instead in order. The rate-distortion function under the CR constraint has been recently derived for the point-to-point (Wyner-Ziv) problem. In this paper, this result is extended to the Heegard-Berger (HB) problem and to its variant with cooperating decoders. Specifically, for the HB problem, the ratedistortion function is derived under the assumption that the side information sequences at the two decoders are stochastically degraded. The rate-distortion function is also calculated explicitly for the special case of binary source and erased side information with Hamming distortion metric. The rate-distortion function is then characterized also for the HB problem with cooperating decoders and physically degraded side information.

Original languageEnglish (US)
Title of host publication2012 IEEE International Symposium on Information Theory Proceedings, ISIT 2012
Pages179-183
Number of pages5
DOIs
StatePublished - 2012
Event2012 IEEE International Symposium on Information Theory, ISIT 2012 - Cambridge, MA, United States
Duration: Jul 1 2012Jul 6 2012

Publication series

NameIEEE International Symposium on Information Theory - Proceedings

Other

Other2012 IEEE International Symposium on Information Theory, ISIT 2012
Country/TerritoryUnited States
CityCambridge, MA
Period7/1/127/6/12

All Science Journal Classification (ASJC) codes

  • Theoretical Computer Science
  • Information Systems
  • Modeling and Simulation
  • Applied Mathematics

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