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Adaptive sampling for compressed sensing based image compression

Research output: Contribution to journalArticleScientificpeer-review


Original languageEnglish
Pages (from-to)94-105
Number of pages12
JournalJournal of Visual Communication and Image Representation
Publication statusPublished - 1 Jul 2015
Publication typeA1 Journal article-refereed


The compressed sensing (CS) theory has been successfully applied to image compression in the past few years as most image signals are sparse in a certain domain. In this paper, we focus on how to improve the sampling efficiency for CS-based image compression by using our proposed adaptive sampling mechanism on the block-based CS (BCS), especially the reweighted one. To achieve this goal, two solutions are developed at the sampling side and reconstruction side, respectively. The proposed sampling mechanism allocates the CS-measurements to image blocks according to the statistical information of each block so as to sample the image more efficiently. A generic allocation algorithm is developed to help assign CS-measurements and several allocation factors derived in the transform domain are used to control the overall allocation in both solutions. Experimental results demonstrate that our adaptive sampling scheme offers a very significant quality improvement as compared with traditional non-adaptive ones.


  • Adaptive sampling, Block-based compressed sensing (BCS), Image coding, Image compression, Measurement allocation, Sampling efficiency, Sparsity Compressed sensing (CS)

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