Multiterminal compress-and-estimate source coding

Alon Kipnis, Stefano Rini, Andrea J. Goldsmith

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

5 Scopus citations


We consider a multiterminal source coding problem in which a random source signal is estimated from encoded versions of multiple noisy observations. Each encoded version, however, is compressed so as to minimize a local distortion measure, defined only with respect to the distribution of the corresponding noisy observation. The original source is then estimated from these compressed noisy observations. We denote the minimal distortion under this coding scheme as the compress-and-estimate distortion-rate function (CE-DRF). We derive a single-letter expression for the CE-DRF in the case of an i.i.d source. We evaluate this expression for the case of a Gaussian source observed through multiple parallel AWGN channels and quadratic distortion and in the case of a non-uniform binary i.i.d source observed through multiple binary symmetric channels under Hamming distortion. For the case of a Gaussian source, we compare the performance for centralized encoding versus that of distributed encoding. In the centralized encoding scenario, when the code rates are sufficiently small, there is no loss of performance compared to the indirect source coding distortion-rate function, whereas distributed encoding achieves distortion strictly larger then the optimal multiterminal source coding scheme. For the case of a binary source, we show that even with a single observation, the CE-DRF is strictly larger than that of indirect source coding.

Original languageEnglish
Title of host publicationProceedings - ISIT 2016; 2016 IEEE International Symposium on Information Theory
PublisherInstitute of Electrical and Electronics Engineers Inc.
Number of pages5
ISBN (Electronic)9781509018062
StatePublished - 10 Aug 2016
Event2016 IEEE International Symposium on Information Theory, ISIT 2016 - Barcelona, Spain
Duration: 10 Jul 201615 Jul 2016

Publication series

NameIEEE International Symposium on Information Theory - Proceedings
ISSN (Print)2157-8095


Conference2016 IEEE International Symposium on Information Theory, ISIT 2016


  • Binary source
  • Compress-and-estimate
  • Gaussian source
  • Indirect source coding
  • Remote source coding


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