A coding theorem for f-separable distortion measures

Yanina Shkel, Sergio Verdú

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

3 Scopus citations


Rate-distortion theory is a branch of information theory that provides theoretical foundation for lossy data compression. In this setting, the decompressed data need not match original data exactly; however, it must be reconstructed with a prescribed fidelity, which is modeled by a distortion measure. An ubiquitous assumption in rate-distortion literature is that such distortion measures are separable: that is, the distortion measure can be expressed as an arithmetic average of single-letter distortions. Such set up gives nice theoretical results at the expense of a very restrictive model. Separable distortion measures are linear functions of single-letter distortions; real-world distortion measures rarely have such nice structure. In this work we relax the separability assumption and propose f-separable distortion measures, which are well suited to model non-linear penalties. We prove a rate-distortion coding theorem for stationary ergodic sources with f-separable distortion measures, and provide some illustrative examples of the resulting rate-distortion functions.

Original languageEnglish (US)
Title of host publication2016 Information Theory and Applications Workshop, ITA 2016
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)9781509025299
StatePublished - Mar 27 2017
Event2016 Information Theory and Applications Workshop, ITA 2016 - La Jolla, United States
Duration: Jan 31 2016Feb 5 2016

Publication series

Name2016 Information Theory and Applications Workshop, ITA 2016


Other2016 Information Theory and Applications Workshop, ITA 2016
Country/TerritoryUnited States
CityLa Jolla

All Science Journal Classification (ASJC) codes

  • Computer Networks and Communications
  • Computer Science Applications
  • Artificial Intelligence
  • Information Systems
  • Signal Processing


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