Coding theorems for the compress and estimate source coding problem

Alon Kipnis, Stefano Rini, Andrea J. Goldsmith

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

1 Scopus citations

Abstract

We consider the remote source coding setting in which a source realization is estimated from a lossy compressed sequence of noisy observations. Unlike in the optimal remote source coding problem, however, the encoder is bound to use good codes with respect to the observation sequence, i.e., codes that are optimal for the lossy reconstruction of the observation, rather than the remote source. This encoding strategy is denoted as the compress-and-estimate (CE) scheme. For the case of an i.i.d source observed through a memoryless channel, we show that the distortion in the CE scheme is characterized by a single-letter expression, referred to as the CE distortion-rate function (CE-DRF). In particular, we show that the CE-DRF can be attained by estimating the source from the output of a remote encoder employing any sequence of good codes with respect to the observation sequence. In addition, we show that the limiting distortion in estimating any finite sub-block of the source realization from the output of a remote encoder employing good codes, averaged over all sub-blocks, is also bounded by the CE-DRF.

Original languageEnglish (US)
Title of host publication2017 IEEE International Symposium on Information Theory, ISIT 2017
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages2568-2572
Number of pages5
ISBN (Electronic)9781509040964
DOIs
StatePublished - Aug 9 2017
Externally publishedYes
Event2017 IEEE International Symposium on Information Theory, ISIT 2017 - Aachen, Germany
Duration: Jun 25 2017Jun 30 2017

Publication series

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

Other

Other2017 IEEE International Symposium on Information Theory, ISIT 2017
CountryGermany
CityAachen
Period6/25/176/30/17

All Science Journal Classification (ASJC) codes

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

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