Journal of South China University of Technology (Natural Science Edition) ›› 2017, Vol. 45 ›› Issue (3): 1-10.doi: 10.3969/j.issn.1000-565X.2017.03.001

• Electronics, Communication & Automation Technology •     Next Articles

Residual Structure Characteristics- Based Block Classifying Reconstruction Algorithm for CVS

YANG Chun-ling LI Wen-hao   

  1. School of Electronic and Information Engineering,South China University of Technology,Guangzhou 510640,Guangdong,China
  • Received:2016-05-25 Revised:2016-11-24 Online:2017-03-25 Published:2017-02-02
  • Contact: 杨春玲( 1970-) ，女，博士，教授，主要从事图像/视频压缩感知研究． E-mail:eeclyang@scut.edu.cn
  • About author:杨春玲( 1970-) ，女，博士，教授，主要从事图像/视频压缩感知研究．
  • Supported by:
    Supported by the Natural Science Foundation of Guangdong Province of China( 2016A030313455)

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Abstract:

Most existing compressed video sensing ( CVS) algorithms with best reconstruction performance adopt a “prediction-residual reconstruction”strategy,which helps obtain high reconstruction quality by taking good advantage of intra-frame and inter-frame correlation.However,all of them ignore the residual structure characteristics and simply use SPL reconstruction algorithm which is only suitable for natural image compressed sensing.In order to solve this problem,a block classifying reconstruction algorithm on the basis of residual structure characteristics is proposed,which firstly classifies residual blocks according to their average energy and then adopts suitable algorithms to reconstruct residual blocks corresponding to their structure characteristics.Simulated results show that the proposed algorithm helps achieve higher reconstruction quality than SPL algorithm for video sequences with fast movements.

Key words: compressed video sensing, residual reconstruction, average energy, residual block classification

CLC Number: