Compressive-Sensing-Based Video Codec by Autoregressive Prediction and Adaptive Residual Recovery
This paper presents a compressive-sensing- (CS-) based video codec which is suitable for wireless video system requiring simple encoders but tolerant, more complex decoders. At the encoder side, each video frame is independently measured by block-based random matrix, and the resulting measurements a...
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| Published in | International journal of distributed sensor networks Vol. 2015; no. 8; p. 562840 |
|---|---|
| Main Authors | , , , |
| Format | Journal Article |
| Language | English |
| Published |
London, England
Hindawi Publishing Corporation
01.01.2015
SAGE Publications Sage Publications Ltd. (UK) John Wiley & Sons, Inc Wiley |
| Subjects | |
| Online Access | Get full text |
| ISSN | 1550-1329 1550-1477 1550-1477 |
| DOI | 10.1155/2015/562840 |
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| Abstract | This paper presents a compressive-sensing- (CS-) based video codec which is suitable for wireless video system requiring simple encoders but tolerant, more complex decoders. At the encoder side, each video frame is independently measured by block-based random matrix, and the resulting measurements are encoded into compressed bitstream by entropy coding. Specifically, to reduce the quantization errors of measurements, a nonuniform quantization is integrated into the DPCM-based quantizer. At the decoder side, a novel joint reconstruction algorithm is proposed to improve the quality of reconstructed video frames. Firstly, the proposed algorithm uses the temporal autoregressive (AR) model to generate the Side Information (SI) of video frame, and next it recovers the residual between the original frame and the corresponding SI. To exploit the sparse property of residual with locally varying statistics, the Principle Component Analysis (PCA) is used to learn online the transform matrix adapting to residual structures. Extensive experiments validate that the joint reconstruction algorithm in the proposed codec achieves much better results than many existing methods with consideration of the reconstructed quality and the computational complexity. The rate-distortion performance of the proposed codec is superior to the state-of-the-art CS-based video codec, although there is still a considerable gap between it and traditional video codec. |
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| AbstractList | This paper presents a compressive-sensing- (CS-) based video codec which is suitable for wireless video system requiring simple encoders but tolerant, more complex decoders. At the encoder side, each video frame is independently measured by block-based random matrix, and the resulting measurements are encoded into compressed bitstream by entropy coding. Specifically, to reduce the quantization errors of measurements, a nonuniform quantization is integrated into the DPCM-based quantizer. At the decoder side, a novel joint reconstruction algorithm is proposed to improve the quality of reconstructed video frames. Firstly, the proposed algorithm uses the temporal autoregressive (AR) model to generate the Side Information (SI) of video frame, and next it recovers the residual between the original frame and the corresponding SI. To exploit the sparse property of residual with locally varying statistics, the Principle Component Analysis (PCA) is used to learn online the transform matrix adapting to residual structures. Extensive experiments validate that the joint reconstruction algorithm in the proposed codec achieves much better results than many existing methods with consideration of the reconstructed quality and the computational complexity. The rate-distortion performance of the proposed codec is superior to the state-of-the-art CS-based video codec, although there is still a considerable gap between it and traditional video codec. |
| Audience | Academic |
| Author | Liu, Hongbing Li, Ran Li, Yanling Xue, Rui |
| Author_xml | – sequence: 1 givenname: Ran surname: Li fullname: Li, Ran organization: School of Computer and Information Technology Xinyang Normal University Xinyang 464000 China xytc.edu.cn – sequence: 2 givenname: Hongbing orcidid: 0000-0002-4839-6054 surname: Liu fullname: Liu, Hongbing organization: School of Computer and Information Technology Xinyang Normal University Xinyang 464000 China xytc.edu.cn – sequence: 3 givenname: Rui surname: Xue fullname: Xue, Rui organization: School of Computer and Information Technology Xinyang Normal University Xinyang 464000 China xytc.edu.cn – sequence: 4 givenname: Yanling surname: Li fullname: Li, Yanling organization: School of Computer and Information Technology Xinyang Normal University Xinyang 464000 China xytc.edu.cn |
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| Cites_doi | 10.1109/ICIP.2009.5414429 10.1109/icassp.2009.4959797 10.1109/TIT.2006.871582 10.1002/cpa.3160410705 10.1109/msp.2007.914728 10.1007/978-1-4419-7011-4 10.1109/dcc.2011.25 10.1109/ciss.2010.5464825 10.1109/MSP.2006.1657820 10.1002/wics.101 10.1364/oe.21.010526 10.1109/icip.2009.5414631 10.1109/76.246088 10.1109/TIP.2011.2163520 10.1109/pcs.2009.5167431 10.1109/TIP.2012.2222893 10.1109/MSP.2007.4286571 10.1109/icip.2012.6467004 10.1109/tit.2005.862083 10.1109/tit.2006.885507 10.1109/tsp.2011.2170977 10.1109/18.382009 10.1109/icdsp.2007.4288604 10.1109/TCSVT.2012.2207269 10.1109/tip.2012.2188810 10.1109/dcc.2011.26 10.1109/TCSVT.2010.2046057 10.1109/jstsp.2007.910281 10.1016/j.jvcir.2011.10.001 10.1090/conm/026/737400 10.1109/TCSVT.2009.2022798 10.1109/tip.2003.819861 10.1109/JPROC.2004.839619 10.1109/TCSVT.2012.2221191 10.1109/iccv.2011.6126273 |
| ContentType | Journal Article |
| Copyright | Copyright © 2015 Ran Li et al. 2015 Ran Li et al. COPYRIGHT 2015 Sage Publications Ltd. (UK) Copyright © 2015 Ran Li et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. |
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| SubjectTerms | Algorithms Codec Coders Coding theory Decoders Digital video Encoders Engineering research Entropy Frames Quantization Reconstruction |
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| Title | Compressive-Sensing-Based Video Codec by Autoregressive Prediction and Adaptive Residual Recovery |
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