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Low-complexity distributed multi-view video coding for wireless video sensor networks based on compressive sensing theory.pdf
Neurocomputing 120 (2013) 415–421
Contents lists available at ScienceDirect
Neurocomputing
journal homepage: /locate/neucom
Low-complexity distributed multi-view video coding for wireless video sensor networks based on compressive sensing theory$
Dan Jiang a, Jichang Guo a,n, Xiaojia Wu a,b
a School of Electronic Information Engineering, Tianjin University, Tianjin 300072, China b School of Electronic Information Engineering, Taiyuan University of Science and Technology, Taiyuan 030024, China
article info
Article history: Received 21 February 2012 Received in revised form 11 July 2012 Accepted 24 July 2012 Available online 6 April 2013
Keywords: Sparsity Sparse reconstruction Multi-view video Compressive sensing Wireless video sensor networks Distributed source coding
abstract
Sparsity is an attractive feature of images. Images can be ef?ciently represented using a few signi?cant coef?cients and sparse reconstructed from a small set of random linear measurements by utilizing the sparse feature in compressive sensing theory. Storage and transmission of multi-view video sequences involve large volumes of redundant data. These data can be ef?ciently compressed with techniques which encode the signals independently and decode them jointly. By integrating the respective characteristics of compressive sensing and distributed source coding, we propose a novel multi-view video coding approach for use in resource limited devices such as wireless video sensor networks. The proposed approach can explore the sparsity of video images, allow for low complexity encoder and the exploitation of inter-camera correlation without communications among cameras. Simulation results show the proposed framework outperforms the baseline compressive sensing-based scheme of intra frame coding by 3–5 dB. Compared with conventional H.264 or DVC scheme, the proposed frameworks simple while the quality of reconstructed image and compressibility are kept.
2013 Elsevier B.V. All rights reserved.
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