Bit-plane compressive sensing with Bayesian decoding for lossy compression

Sz Hsien Wu*, Wen-Hsiao Peng, Tihao Chiang

*Corresponding author for this work

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

4 Scopus citations


This paper addresses the problem of reconstructing a com-pressively sampled sparse signal from its lossy and possibly insufficient measurements. The process involves estimations of sparsity pattern and sparse representation, for which we derived a vector estimator based on the Maximum a Posteriori Probability (MAP) rule. By making full use of signal prior knowledge, our scheme can use a measurement number close to sparsity to achieve perfect reconstruction. It also shows a much lower error probability of sparsity pattern than prior work, given insufficient measurements. To better recover the most significant part of the sparse representation, we further introduce the notion of bit-plane separation. When applied to image compression, the technique in combination with our MAP estimator shows promising results as compared to JPEG: the difference in compression ratio is seen to be within a factor of two, given the same decoded quality.

Original languageEnglish
Title of host publication28th Picture Coding Symposium, PCS 2010
Number of pages4
StatePublished - 1 Dec 2010
Event28th Picture Coding Symposium, PCS 2010 - Nagoya, Japan
Duration: 8 Dec 201010 Dec 2010

Publication series

Name28th Picture Coding Symposium, PCS 2010


Conference28th Picture Coding Symposium, PCS 2010


  • Bayesian estimation
  • Bit-plane separation
  • Compressive sensing


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