Generalized Gaussian multiterminal source coding and probabilistic graphical models

Jun Chen; Farrokh Etezadi; Ashish Khisti

Found an issue? Give us feedback

https://doi.org/10.1...arrow_drop_down

https://doi.org/10.1109/isit.2...

Article . 2017 . Peer-reviewed

Data sources: Crossref

https://dx.doi.org/10.1109/isi...

Article

Data sources: Microsoft Academic Graph

Generalized Gaussian multiterminal source coding and probabilistic graphical models

descriptionPublicationkeyboard_double_arrow_right Article 01 Jun 2017Publisher:IEEEJournal:2017 IEEE International Symposium on Information Theory (ISIT)

Authors: Jun Chen; Farrokh Etezadi; Ashish Khisti;

doi: 10.1109/isit.2017.8006622

Generalized Gaussian multiterminal source coding and probabilistic graphical models

- Summary
- Metrics

Abstract

The sum-rate distortion function of generalized Gaussian multiterminal source coding is shown to coincide with that of joint encoding in the high-resolution regime if and only if the source-encoder bipartite graph and the undirected graphical model (also known as Gaussian Markov network or Gaussian Markov random field) of the source distribution satisfy a certain condition.

Related Organizations

McMaster University
Canada
Samsung (United States)
United States
Samsung
Korea (Republic of)
University of Toronto
Canada

Impact byBIP!

	selected citations These citations are derived from selected sources. This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).	4
	popularity This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.	Average
	influence This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).	Average
	impulse This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.	Average