@inproceedings{
 title = {Hardware implementation of compressive sensing for image compression},
 type = {inproceedings},
 year = {2017},
 keywords = {Coherence,Compression,Independent&Identically Distributed (IID),Restricted Isometric Property (RIP),Sparsity},
 volume = {2017-Decem},
 id = {128c41ce-768d-3616-883e-c30e8695dd61},
 created = {2018-09-06T11:22:40.571Z},
 file_attached = {false},
 profile_id = {11ae403c-c558-3358-87f9-dadc957bb57d},
 last_modified = {2018-09-06T11:22:40.571Z},
 read = {false},
 starred = {false},
 authored = {true},
 confirmed = {false},
 hidden = {false},
 private_publication = {false},
 abstract = {© 2017 IEEE. Compressive sensing (CS) is one of the effective data compression methods where a small number of measurements of a sparse signal is required to have an exact recovery. In Compressive sensing technique, the data is acquired and compressed at the same time. CS concept allows capturing only fewer sparse and compressible signal which contains only useful information that helps to recover a proper signal. In this paper, image compression is performed with compressive sensing concept. The different basis matrices and sensing matrices are considered which satisfy the Restricted Isometric Property (RIP) and Independent and Identically Distributed (IID). The hardware implementation of CS is covered to have real-time compression for various image-based applications. The performance is observed regarding SNR, compression ratio, and correlation. The reconstruction algorithms are implemented on MATLAB platform. The obtained results show satisfactory performance for CS based image compression.},
 bibtype = {inproceedings},
 author = {Joshi, A.M. and Sahu, C. and Ravikumar, M. and Ansari, S.},
 doi = {10.1109/TENCON.2017.8228060},
 booktitle = {IEEE Region 10 Annual International Conference, Proceedings/TENCON}
}

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