Using compression codes in compressed sensing

Farideh Ebrahim Rezagah, Shirin Jalali, Elza Erkip, H. Vincent Poor

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


Data compression and compressed sensing algorithms exploit the structure present in a signal for its efficient representation and measurement, respectively. While most state-of-the-art data compression codes take advantage of complex patterns present in signals of interest, this is not the case in compressed sensing. This paper explores usage of efficient data compression codes in building compressed sensing recovery methods for stochastic processes. It is proved that for an i.i.d. process, compression-based compressed sensing achieves the fundamental limits in terms of the number of measurements. It is also proved that compressed sensing recovery methods built based on a family of universal compression codes yield a family of universal compressed sensing schemes.

Original languageEnglish (US)
Title of host publication2016 IEEE Information Theory Workshop, ITW 2016
PublisherInstitute of Electrical and Electronics Engineers Inc.
Number of pages5
ISBN (Electronic)9781509010905
StatePublished - Oct 21 2016
Event2016 IEEE Information Theory Workshop, ITW 2016 - Cambridge, United Kingdom
Duration: Sep 11 2016Sep 14 2016

Publication series

Name2016 IEEE Information Theory Workshop, ITW 2016


Other2016 IEEE Information Theory Workshop, ITW 2016
Country/TerritoryUnited Kingdom


  • Compressed Sensing
  • Information dimension
  • Lossy Compression
  • Rate distortion dimension
  • Universal coding

ASJC Scopus subject areas

  • Computer Networks and Communications
  • Information Systems
  • Software
  • Signal Processing


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