Data compression based on compressed sensing and wavelet transform

The recovery can be achieved from the undersampling signal in compressed sensing theory relying on the sparsity and incoherent characteristics of the signal. A data compression algorithm is advanced in this article, based on compressed sensing and wavelet transform. Firstly the framework of the comp...

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Bibliographic Details
Published in2010 3rd International Conference on Computer Science and Information Technology Vol. 8; pp. 537 - 542
Main Authors Lou Hao, Luo Weibing, Wang Jiachen
Format Conference Proceeding
LanguageEnglish
Published IEEE 01.07.2010
Subjects
1th Norm Minimization
Compressed Sensing
Data Compression
Sparse Representation
Wavelet Transform
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Summary:The recovery can be achieved from the undersampling signal in compressed sensing theory relying on the sparsity and incoherent characteristics of the signal. A data compression algorithm is advanced in this article, based on compressed sensing and wavelet transform. Firstly the framework of the compressed sensing theory is introduced, and then a one-dimension and a two-dimension wavelet transform matrixes are constructed respectively, which leads two compressed algorithms based on modulus and original data separately. At last, the compression characteristics are simulated and compared using one-dimension signal and two-dimension images separately, at the same time, the validity is proved by those results.
ISBN:9781424455379
1424455375
DOI:10.1109/ICCSIT.2010.5564748