The likelihood encoder for lossy compression

Eva C. Song, Paul Cuff, H. Vincent Poor

Research output: Contribution to journalArticlepeer-review

48 Scopus citations


A likelihood encoder is studied in the context of lossy source compression. The analysis of the likelihood encoder is based on the soft-covering lemma. It is demonstrated that the use of a likelihood encoder together with the soft-covering lemma yields simple achievability proofs for classical source coding problems. The cases of the point-to-point rate-distortion function, the rate-distortion function with side information at the decoder (i.e., the Wyner-Ziv problem), and the multi-terminal source coding inner bound (i.e., the Berger-Tung problem) are examined in this paper. Furthermore, a non-asymptotic analysis is used for the point-to-point case to examine the upper bound on the excess distortion provided by this method. The likelihood encoder is also related to a recent alternative technique using the properties of random binning.

Original languageEnglish (US)
Article number7406745
Pages (from-to)1836-1849
Number of pages14
JournalIEEE Transactions on Information Theory
Issue number4
StatePublished - Apr 2016

All Science Journal Classification (ASJC) codes

  • Information Systems
  • Computer Science Applications
  • Library and Information Sciences


  • Berger-Tung
  • Wyner-Ziv
  • likelihood encoder
  • rate-distortion theory
  • soft-covering
  • source coding


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