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An image compression algorithm using reordered wavelet coefficients with compressive sensing

  • King Fahd University of Petroleum and Minerals
  • Université Paris 13

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

2 Scopus citations

Abstract

In this paper, we propose a new approach for image compression based on compressive sensing (CS). We introduce a new formulation of sparse vectors for rearranging multilevel 2-D Wavelet coefficients into a structured manner using parent-child relationships. We then use a Gaussian measurement matrix normalized with the weighted average Root Mean Squared (RMS) energies of different wavelet subbands. Compressed sampling is finally performed using this normalized measurement matrix. At the decoding stage, the image is reconstructed using a simple ℓ1-minimization technique. The proposed wavelet-based CS compression results in performance increase compared to other conventional CS-based techniques. Our experimental results show that the proposed algorithm outperforms existing approaches over different natural images.

Original languageEnglish
Title of host publication5th International Conference on Image Processing, Theory, Tools and Applications 2015, IPTA 2015
EditorsRachid Jennane
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages498-503
Number of pages6
ISBN (Electronic)9781479986354
DOIs
StatePublished - 28 Dec 2015
Externally publishedYes
Event5th International Conference on Image Processing, Theory, Tools and Applications 2015, IPTA 2015 - Orleans, France
Duration: 10 Nov 201513 Nov 2015

Publication series

Name5th International Conference on Image Processing, Theory, Tools and Applications 2015, IPTA 2015

Conference

Conference5th International Conference on Image Processing, Theory, Tools and Applications 2015, IPTA 2015
Country/TerritoryFrance
CityOrleans
Period10/11/1513/11/15

Keywords

  • Adaptive sampling
  • Compressed sensing
  • Compression
  • Discrete wavelet transform
  • Image quality

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