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
[1]SULLIVAN G J,OHM J,HAN W J,et al.Overview of the high efficiency video coding (HEVC) standard[J].IEEE Transactions on Circuits and Systems for Video Technology,2012,22(12):1649-1668.
[2]OHM J R,SULLIVAN G J,SCHWARZ H,et al.Comparison of the coding efficiency of video coding standards-including high efficiency video coding (HEVC)[J].IEEE Transactions on circuits and systems for video technology,2012,22(12):1669-1684.
[3]DONG C,DENG Y,CHANGE LOY C,et al.Compression artifacts reduction by a deep convolutional network[C]∥Procee-dings of the IEEE International Conference on Computer Vision.2015:576-584.
[4]PARK W S,KIM M.CNN-based in-loop filtering for coding efficiency improvement[C]∥Image,Video,and Multidimensional Signal Processing Workshop (IVMSP).IEEE,2016:1-5.
[5]DONG C,LOY C C,HE K,et al.Learning a deep convolutional network for image super-resolution[C]∥European Conference on Computer Vision.Cham:Springer,2014:184-199.
[6]DAI Y,LIU D,WU F.A convolutional neural network approach for post-processing in HEVC intra coding[C]∥International Conference on Multimedia Modeling.Cham:Springer,2017:28-39.
[7]YANG R,XU M,WANG Z,et al.Enhancing Quality for HEVC Compressed Videos[J].arXiv preprint arXiv:1709.06734,2017.
[8]WANG T,CHEN M,CHAO H.A novel deep learning-based
method of improving coding efficiency from the decoder-end for hevc[C]∥Data Compression Conference (DCC),2017.IEEE,2017:410-419.
[9]SIMONYAN K,ZISSERMAN A.Very deep convolutional networks for large-scale image recognition[J].arXiv preprint arXiv:1409.1556,2014.
[10]HE K,SUN J.Convolutional neural networks at constrained
time cost[C]∥2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).IEEE,2015:5353-5360.
[11]HE K,ZHANG X,REN S,et al.Deep residual learning for ima-
ge recognition[C]∥Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition.2016:770-778.
[12]GROSS S,WILBER M.Training and investigating residual nets[EB/OL].http://torch.ch/blog/2016/02/04/resnets.html 2016.
[13]SCHWARZ H,MARPE D,WIEGAND T.Analysis of hierarchi-
cal B pictures and MCTF[C]∥2006 IEEE International Confe-rence on Multimedia and Expo.IEEE,2006:1929-1932.
[14]KINGMA D P,BA J.Adam:A method for stochastic optimization[J].arXiv preprint arXiv:1412.6980,2014.
[15]IOFFE S,SZEGEDY C.Batch normalization:Accelerating deep network training by reducing internal covariate shift[J].arXiv preprint arXiv:1502.03167,2015.
[16]ABADI M,BARHAM P,CHEN J,et al.TensorFlow:A System for Large-Scale Machine Learning[C]∥Proceedings of the 12th USENIX Conference on Operating Systems Design and Implementation.2016:265-283.
[17]BOSSEN F.Common test conditions and software reference con-
figurations[C]∥12th Joint Collaborative Team on Video Coding Meeting.2011.
[18]BJONTEGARRD G.Calculation of average PSNR differences
between RD-curves:ITU-T SG16/Q6[R].VCEG-M33,Austin,US,2001.
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