Adaptive reweighted compressed sensing for image compression

Shuyuan Zhu, Bing Zeng, Moncef Gabbouj

Research output: Chapter in Book/Conference Proceeding/ReportConference Paper published in a bookpeer-review

19 Citations (Scopus)

Abstract

According to the compressed sensing (CS) theory, a signal that is sparse in a certain domain can be nearly exactly recovered from a few measurements where the sampling rate is lower than the Nyquist rate. This theory has been successfully applied to the image compression in the past few years as most image signals are highly sparse. In this paper, we apply an adaptive sampling mechanism to the reweighted block-based CS (BCS). The proposed adaptive sampling allocates the measurements to each image block according to the statistical information of the block so as to sample and recover the image more efficiently. Experimental results demonstrate that our adaptive reweighted method offers a very significant quality improvement compared with the traditional BCS schemes, including the non-reweighted and reweighted ones.

Original languageEnglish
Title of host publication2014 IEEE International Symposium on Circuits and Systems, ISCAS 2014
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages1-4
Number of pages4
ISBN (Print)9781479934324
DOIs
Publication statusPublished - 2014
Externally publishedYes
Event2014 IEEE International Symposium on Circuits and Systems, ISCAS 2014 - Melbourne, VIC, Australia
Duration: 1 Jun 20145 Jun 2014

Publication series

NameProceedings - IEEE International Symposium on Circuits and Systems
ISSN (Print)0271-4310

Conference

Conference2014 IEEE International Symposium on Circuits and Systems, ISCAS 2014
Country/TerritoryAustralia
CityMelbourne, VIC
Period1/06/145/06/14

Keywords

  • adaptive CS sampling
  • compressed sensing (CS)
  • image compression

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