Compressive sampling and lossy compression: Do random measurements provide an efficient method of representing sparse signals?

Vivek K. Goyal, Alyson K. Fletcher, Sundeep Rangan

Research output: Contribution to journalArticle

Abstract

A recent work investigated whether random measurements of sparse signals provide an efficient method of representing sparse signals. How the random measurements are encoded into bits are the basis for the performance of the source coding. Even very weak forms of universality are precluded using familiar forms of quantization. The recent work also showed that recovery of the sparsity pattern is asymptotically impossible and that the mean-squared error peformance is far from optimal.

Original languageEnglish (US)
Pages (from-to)48-56
Number of pages9
JournalIEEE Signal Processing Magazine
Volume25
Issue number2
DOIs
StatePublished - 2008

Fingerprint

Lossy Compression
Sampling
Source Coding
Sparsity
Mean Squared Error
Universality
Quantization
Recovery
Form

ASJC Scopus subject areas

  • Signal Processing
  • Electrical and Electronic Engineering
  • Applied Mathematics

Cite this

Compressive sampling and lossy compression : Do random measurements provide an efficient method of representing sparse signals? / Goyal, Vivek K.; Fletcher, Alyson K.; Rangan, Sundeep.

In: IEEE Signal Processing Magazine, Vol. 25, No. 2, 2008, p. 48-56.

Research output: Contribution to journalArticle

@article{74c5540a7b7a447fa022825ccbde8032,
title = "Compressive sampling and lossy compression: Do random measurements provide an efficient method of representing sparse signals?",
abstract = "A recent work investigated whether random measurements of sparse signals provide an efficient method of representing sparse signals. How the random measurements are encoded into bits are the basis for the performance of the source coding. Even very weak forms of universality are precluded using familiar forms of quantization. The recent work also showed that recovery of the sparsity pattern is asymptotically impossible and that the mean-squared error peformance is far from optimal.",
author = "Goyal, {Vivek K.} and Fletcher, {Alyson K.} and Sundeep Rangan",
year = "2008",
doi = "10.1109/MSP.2007.915001",
language = "English (US)",
volume = "25",
pages = "48--56",
journal = "IEEE Signal Processing Magazine",
issn = "1053-5888",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
number = "2",

}

TY - JOUR

T1 - Compressive sampling and lossy compression

T2 - Do random measurements provide an efficient method of representing sparse signals?

AU - Goyal, Vivek K.

AU - Fletcher, Alyson K.

AU - Rangan, Sundeep

PY - 2008

Y1 - 2008

N2 - A recent work investigated whether random measurements of sparse signals provide an efficient method of representing sparse signals. How the random measurements are encoded into bits are the basis for the performance of the source coding. Even very weak forms of universality are precluded using familiar forms of quantization. The recent work also showed that recovery of the sparsity pattern is asymptotically impossible and that the mean-squared error peformance is far from optimal.

AB - A recent work investigated whether random measurements of sparse signals provide an efficient method of representing sparse signals. How the random measurements are encoded into bits are the basis for the performance of the source coding. Even very weak forms of universality are precluded using familiar forms of quantization. The recent work also showed that recovery of the sparsity pattern is asymptotically impossible and that the mean-squared error peformance is far from optimal.

UR - http://www.scopus.com/inward/record.url?scp=85032750944&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=85032750944&partnerID=8YFLogxK

U2 - 10.1109/MSP.2007.915001

DO - 10.1109/MSP.2007.915001

M3 - Article

AN - SCOPUS:85032750944

VL - 25

SP - 48

EP - 56

JO - IEEE Signal Processing Magazine

JF - IEEE Signal Processing Magazine

SN - 1053-5888

IS - 2

ER -