Using compression codes in compressed sensing

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

doi:10.1109/ITW.2016.7606873

Using compression codes in compressed sensing

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

Electrical and Computer Engineering

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

Abstract

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 language	English (US)
Title of host publication	2016 IEEE Information Theory Workshop, ITW 2016
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	444-448
Number of pages	5
ISBN (Electronic)	9781509010905
DOIs	https://doi.org/10.1109/ITW.2016.7606873
State	Published - Oct 21 2016
Event	2016 IEEE Information Theory Workshop, ITW 2016 - Cambridge, United Kingdom Duration: Sep 11 2016 → Sep 14 2016

Publication series

Name	2016 IEEE Information Theory Workshop, ITW 2016

Other

Other	2016 IEEE Information Theory Workshop, ITW 2016
Country/Territory	United Kingdom
City	Cambridge
Period	9/11/16 → 9/14/16

Keywords

Compressed Sensing
Information dimension
Lossy Compression
Rate distortion dimension
Universal coding

ASJC Scopus subject areas

Computer Networks and Communications
Information Systems
Software
Signal Processing

Access to Document

10.1109/ITW.2016.7606873

Cite this

Using compression codes in compressed sensing. / Rezagah, Farideh Ebrahim; Jalali, Shirin; Erkip, Elza et al.
2016 IEEE Information Theory Workshop, ITW 2016. Institute of Electrical and Electronics Engineers Inc., 2016. p. 444-448 7606873 (2016 IEEE Information Theory Workshop, ITW 2016).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

Rezagah, FE, Jalali, S, Erkip, E & Poor, HV 2016, Using compression codes in compressed sensing. in 2016 IEEE Information Theory Workshop, ITW 2016., 7606873, 2016 IEEE Information Theory Workshop, ITW 2016, Institute of Electrical and Electronics Engineers Inc., pp. 444-448, 2016 IEEE Information Theory Workshop, ITW 2016, Cambridge, United Kingdom, 9/11/16. https://doi.org/10.1109/ITW.2016.7606873

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abstract = "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.",

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author = "Rezagah, {Farideh Ebrahim} and Shirin Jalali and Elza Erkip and Poor, {H. Vincent}",

note = "Funding Information: This research is supported by the National Science Foundation under Grant CCF-1420575. Publisher Copyright: {\textcopyright} 2016 IEEE.; 2016 IEEE Information Theory Workshop, ITW 2016 ; Conference date: 11-09-2016 Through 14-09-2016",

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AB - 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.

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Using compression codes in compressed sensing

Abstract

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Keywords

ASJC Scopus subject areas

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