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Adaptive sampling for compressed sensing based image compression

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


Original languageEnglish
Title of host publication2014 IEEE International Conference on Multimedia and Expo (ICME), 14-18 July 2014, Chengdu
Publication statusPublished - 3 Sep 2014
Publication typeA4 Article in a conference publication
EventIEEE International Conference on Multimedia and Expo -
Duration: 1 Jan 1900 → …


ConferenceIEEE International Conference on Multimedia and Expo
Period1/01/00 → …


The compressed sensing (CS) theory shows that a sparse signal can be recovered at a sampling rate that is (much) lower than the required Nyquist rate. In practice, many image signals are sparse in a certain domain, and because of this, the CS theory has been successfully applied to the image compression in the past few years. The most popular CS-based image compression scheme is the block-based CS (BCS). In this paper, we focus on the design of an adaptive sampling mechanism for the BCS through a deep analysis of the statistical information of each image block. Specifically, this analysis will be carried out at the encoder side (which needs a few overhead bits) and the decoder side (which requires a feedback to the encoder side), respectively. Two corresponding solutions will be compared carefully in our work. We also present experimental results to show that our proposed adaptive method offers a remarkable quality improvement compared with the traditional BCS schemes.


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

Publication forum classification

Field of science, Statistics Finland