Mobile Video Perceptual Quality Assessment Model With ResNet-TSM and BiGRU Network; [基于 ResNet-TSM 和 BiGRU 网络的移动视频感知质量评价模型] - Details

Author：

Du, L. (Du, L..) | Yang, S. (Yang, S..) | Zhuo, L. (Zhuo, L..) | Zhang, J. (Zhang, J..) | Li, J. (Li, J..)

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Scopus

Abstract：

Considering　the　effects　of　stalling‚　quality　switching‚　content　characteristics　and　other　factors‚　which　will　be　directly　reflected　in　the　distorted　video‚　a　client-oriented　mobile　video　perceptual　quality　assessment　model　was　proposed.　The　mapping　model　between　the　distorted　video　and　the　mean　opinion　score　(MOS)　was　established　based　on　the　idea　of　“deep　feature　extraction　+　regression”　instead　of　characterizing　and　measuring　each　influencing　factor.　First‚　ResNet-TSM　network　was　constructed　to　extract　the　deep　spatial-temporal　features　of　each　distorted　video　segmentation.　Second‚　LargeVis　algorithm　was　used　to　reduce　the　dimensionality　of　the　extracted　deep　features‚　and　simultaneously　improving　the　representation　and　discriminative　capabilities　of　the　features.　Afterward‚　the　score　of　each　video　segment　was　obtained　by　modeling　the　long-term　dependence　of　the　video　by　using　the　bidirectional　gated　recurrent　unit.　The　temporal　mean　pooling　was　adopted　to　aggregate　the　scores　of　each　segment　to　obtain　the　overall　video　score.　The　experimental　results　on　the　WaterlooSQoE-Ⅲ　and　LIVE-NFLX-Ⅱ　datasets　show　that　the　proposed　model　can　achieve　a　higher　prediction　accuracy.　©　2024　Beijing　University　of　Technology.　All　rights　reserved.

Keyword：

bidirectional gated recurrent unit deep spatial-temporal features convolutional neural network mean opinion score video perception quality assessment time shift module

Author Community：

[ 1 ] [Du L.]Faculty of Information Technology, Beijing University of Technology, Beijing, 100124, China
[ 2 ] [Du L.]Beijing Key Laboratory of Computational Intelligence and Intelligent System, Beijing University of Technology, Beijing, 100124, China
[ 3 ] [Yang S.]Faculty of Information Technology, Beijing University of Technology, Beijing, 100124, China
[ 4 ] [Yang S.]Beijing Key Laboratory of Computational Intelligence and Intelligent System, Beijing University of Technology, Beijing, 100124, China
[ 5 ] [Zhuo L.]Faculty of Information Technology, Beijing University of Technology, Beijing, 100124, China
[ 6 ] [Zhuo L.]Beijing Key Laboratory of Computational Intelligence and Intelligent System, Beijing University of Technology, Beijing, 100124, China
[ 7 ] [Zhang J.]Faculty of Information Technology, Beijing University of Technology, Beijing, 100124, China
[ 8 ] [Zhang J.]Beijing Key Laboratory of Computational Intelligence and Intelligent System, Beijing University of Technology, Beijing, 100124, China
[ 9 ] [Li J.]Faculty of Information Technology, Beijing University of Technology, Beijing, 100124, China
[ 10 ] [Li J.]Beijing Key Laboratory of Computational Intelligence and Intelligent System, Beijing University of Technology, Beijing, 100124, China

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Source ：

Journal of Beijing University of Technology

ISSN： 0254-0037

Year： 2024

Issue： 1

Volume： 50

Page： 18-26

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SCOPUS Cited Count：

ESI Highly Cited Papers on the List： 0 Unfold All

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Chinese Cited Count：

30 Days PV： 5

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