Computer Science ›› 2019, Vol. 46 ›› Issue (3): 88-91.doi: 10.11896/j.issn.1002-137X.2019.03.011

• ChinaMM2018 • Previous Articles     Next Articles

Deep Residual Network Based HEVC Compressed Videos Enhancement

HE Xiao-yi1,DUAN Ling-yu2,LIN Wei-yao1   

  1. (School of Electronic Information and Electrical Engineering,Shanghai Jiao Tong University,Shanghai 200240,China)1
    (National Engineering Laboratory for Video Technology,Peking University,Beijing 100871,China)2
  • Received:2018-07-05 Revised:2018-09-21 Online:2019-03-15 Published:2019-03-22

Abstract: This paper proposed a HEVC-compressed videos enhancement method based on deep residual network.This method utilizes several stacked residual blocks to achieve feature extraction,followed by feature enhancement and reconstruction.Compared with the existing methods which only use a few convolutional layers,the proposed method can capture the feature of input compressed frames in a more distinctive and stable way.Experimental results show that the proposed method leads to over 6.92% BD-rate saving on 20 benchmark sequences and achieves the best performance among the compared methods.

Key words: Compressed videos enhancement, Convolutional neural network, High efficiency video coding, Residual network

CLC Number: 

  • TP751
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