On lossy compression of binary matrices

Ronit Bustin, Ofer Shayevitz

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


We consider lossy compression of random binary matrices under distortion constraints that strive to preserve the structure of the matrix. Specifically, we assume that matrix elements are statistically independent (but not necessarily identically distributed), and that the worst case row/column average distortion is to be controlled. We discuss a natural notion of matrix types termed (R, c)-type, and provide various results concerning its probability and cardinality, as well as a 'Sanov-type' result, in the spirit of the method-of-types. We then derive bounds on the associated matrix ratedistortion function via a suitable matrix version of the covering lemma.

Original languageEnglish
Title of host publication2017 IEEE International Symposium on Information Theory, ISIT 2017
PublisherInstitute of Electrical and Electronics Engineers Inc.
Number of pages5
ISBN (Electronic)9781509040964
StatePublished - 9 Aug 2017
Event2017 IEEE International Symposium on Information Theory, ISIT 2017 - Aachen, Germany
Duration: 25 Jun 201730 Jun 2017

Publication series

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


Conference2017 IEEE International Symposium on Information Theory, ISIT 2017


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