Compress-and-estimate source coding for a vector Gaussian source

Ruiyang Song, Stefano Rini, Alon Kipnis, Andrea J. Goldsmith

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

1 Scopus citations


We consider the remote vector source coding problem in which a vector Gaussian source is estimated from noisy linear measurements. For this problem, we derive the performance of the compress-and-estimate (CE) coding scheme and compare it to the optimal performance. In the CE coding scheme, the remote encoder compresses the noisy source observations so as to minimize a local distortion measure, independent from the joint distribution between the source and the observations. In reconstruction, the decoder, having full knowledge of the joint distribution of the source and observations, estimates the original source realization from the lossy-compressed noisy observations. For the CE scheme in the vector Gaussian case, we show that, if the code rate is less than a specific threshold, then the CE coding scheme attains the same performance as the optimal coding scheme. For code rates above this threshold, we introduce lower and upper bounds on the performance gap between the CE and the optimal scheme. The case of a two-dimensional Gaussian source observed through two noisy measurements is studied to illustrate the behavior of the performance gap.

Original languageEnglish
Title of host publication2017 IEEE Information Theory Workshop, ITW 2017
PublisherInstitute of Electrical and Electronics Engineers Inc.
Number of pages5
ISBN (Electronic)9781509030972
StatePublished - 31 Jan 2018
Event2017 IEEE Information Theory Workshop, ITW 2017 - Kaohsiung, Taiwan
Duration: 6 Nov 201710 Nov 2017

Publication series

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


Conference2017 IEEE Information Theory Workshop, ITW 2017


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