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Author：

Chen, S. (Chen, S..) | Liu, P. (Liu, P..) | Zhang, Y. (Zhang, Y..) | Zhang, L. (Zhang, L..) | Wang, S. (Wang, S..) | Yuan, J. (Yuan, J..)

Indexed by：

EI Scopus SCIE

Abstract：

By　exploring　the　current　block-based　lossy　video　coding　process　and　compressed　videos,　this　paper　finds　two　unique　characteristics　namely　quality　fluctuation　and　pixel　deficiency.　And　we　use　3D　convolutional　neural　network　(3D-CNN)　to　make　full　use　of　the　limited　temporal　and　spatial　information　in　compressed　video　and　build　compressed　video　quality　enhancement　network　(CVQENet)　to　improve　the　compressed　video　quality.　The　experimental　results　show　that　compared　with　the　videos　encoded　by　High　Efficiency　Video　Coding　(HEVC/H.265),　the　mean　value　of　the　Peak　Signal-to-Noise　Ratio　(PSNR)　of　enhanced　videos　has　been　improved　by　0.4652 dB　under　Low　Delay　(LD)　configuration　with　Quantization　Parameter　(QP)　is　set　to　37.　©　2023,　The　Author(s),　under　exclusive　licence　to　Springer　Science+Business　Media,　LLC,　part　of　Springer　Nature.

Keyword：

3D-CNNs HEVC/H.265 Convolutional neural network Compressed video quality enhancement

Author Community：

  • [ 1 ] [Chen S.]College of Physics and Electronic Information Engineering, Qinghai Minzu University, Qinghai, Xining, 810000, China
  • [ 2 ] [Liu P.]The Information Department, Beijing University of Technology, Beijing, 100124, China
  • [ 3 ] [Liu P.]Advanced Information Network Beijing Laboratory, Beijing, 100124, China
  • [ 4 ] [Liu P.]Computational Intelligence and Intelligent Systems Beijing Key Laboratory, Beijing, 100124, China
  • [ 5 ] [Zhang Y.]The Information Department, Beijing University of Technology, Beijing, 100124, China
  • [ 6 ] [Zhang Y.]Advanced Information Network Beijing Laboratory, Beijing, 100124, China
  • [ 7 ] [Zhang Y.]Computational Intelligence and Intelligent Systems Beijing Key Laboratory, Beijing, 100124, China
  • [ 8 ] [Zhang L.]College of Physics and Electronic Information Engineering, Qinghai Minzu University, Qinghai, Xining, 810000, China
  • [ 9 ] [Wang S.]The Information Department, Beijing University of Technology, Beijing, 100124, China
  • [ 10 ] [Wang S.]Advanced Information Network Beijing Laboratory, Beijing, 100124, China
  • [ 11 ] [Wang S.]Computational Intelligence and Intelligent Systems Beijing Key Laboratory, Beijing, 100124, China
  • [ 12 ] [Yuan J.]The Information Department, Beijing University of Technology, Beijing, 100124, China
  • [ 13 ] [Yuan J.]Advanced Information Network Beijing Laboratory, Beijing, 100124, China
  • [ 14 ] [Yuan J.]Computational Intelligence and Intelligent Systems Beijing Key Laboratory, Beijing, 100124, China

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Source ：

Wireless Networks

ISSN： 1022-0038

Year： 2023

Issue： 6

Volume： 30

Page： 6125-6133

3 . 0 0 0

JCR@2022

ESI Discipline： COMPUTER SCIENCE;

ESI HC Threshold：19

Cited Count：

WoS CC Cited Count： 0

SCOPUS Cited Count： 1

ESI Highly Cited Papers on the List： 0 Unfold All

WanFang Cited Count：

Chinese Cited Count：

30 Days PV： 8

Affiliated Colleges：

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