Application of full-reference video quality metrics in IPTV

被引:0
|
作者
Sedano, Inigo [1 ]
Prieto, Gorka [2 ]
Brunnstrom, Kjell [3 ,4 ]
Kihl, Maria [5 ]
Montalban, Jon [2 ]
机构
[1] Tecnalia Res & Innovat, Instrumentat & Smart Syst, Derio, Spain
[2] Univ Basque Country, Fac Engn Bilbao, Dept Commun Engn, Bilbao, Spain
[3] RISE Acreo AB, Visual Media Qual, Kista, Sweden
[4] Mid Sweden Univ, Sundsvall, Sweden
[5] Lund Univ, Dept Elect & Informat Technol, Lund, Sweden
关键词
IPTV & Internet TV; Performance evaluation; Objective evaluation techniques; QoE;
D O I
暂无
中图分类号
TM [电工技术]; TN [电子技术、通信技术];
学科分类号
0808 ; 0809 ;
摘要
Executing an accurate full-reference metric such as VQM can take minutes in an average computer for just one user. Therefore, it can be unfeasible to analyze all the videos received by users in an IPTV network for example consisting of 10.000 users using a single computer running the VQM metric. One solution can be to use a lightweight no-reference metrics in addition to the full-reference metric mentioned. Lightweight no-reference metrics can be used for discarding potential situations to evaluate because they are accurate enough for that task, and then the full-reference metric VQM can be used when more accuracy is needed. The work in this paper is focused on determining the maximum number of situations/users that can be analyzed simultaneously using the VQM metric in a computer with good performance. The full-reference metric is applied on the transmitter using a method specified in the recommendation ITU BT. 1789. The best performance achieved was 112.8 seconds per process.
引用
收藏
页码:464 / 467
页数:4
相关论文
共 50 条
  • [41] A Full-Reference Quality Metric for Geometrically Distorted Images
    D'Angelo, Angela
    Zhaoping, Li
    Barni, Mauro
    IEEE TRANSACTIONS ON IMAGE PROCESSING, 2010, 19 (04) : 867 - 881
  • [42] Full-Reference Image Quality Assessment with Transformer and DISTS
    Tsai, Pei-Fen
    Peng, Huai-Nan
    Liao, Chia-Hung
    Yuan, Shyan-Ming
    MATHEMATICS, 2023, 11 (07)
  • [43] Bridge the Gap Between Full-Reference and No-Reference: A Totally Full-Reference Induced Blind Image Quality Assessment via Deep Neural Networks
    Xiaoyu Ma
    Suiyu Zhang
    Chang Liu
    Dingguo Yu
    ChinaCommunications, 2023, 20 (06) : 215 - 228
  • [44] Full-Reference 3-D Video Quality Assessment Using Scene Component Statistical Dependencies
    Appina, Balasubramanyam
    Channappayya, Sumohana S.
    IEEE SIGNAL PROCESSING LETTERS, 2018, 25 (06) : 823 - 827
  • [45] A Simple Prediction Fusion Improves Data-driven Full-Reference Video Quality Assessment Models
    Bampis, Christos G.
    Bovik, Alan C.
    Li, Zhi
    2018 PICTURE CODING SYMPOSIUM (PCS 2018), 2018, : 298 - 302
  • [46] Bridge the Gap Between Full-Reference and No-Reference: A Totally Full-Reference Induced Blind Image Quality Assessment via Deep Neural Networks
    Ma, Xiaoyu
    Zhang, Suiyu
    Liu, Chang
    Yu, Dingguo
    CHINA COMMUNICATIONS, 2023, 20 (06) : 215 - 228
  • [47] Full-Reference Quality Estimation for Images With Different Spatial Resolutions
    Demirtas, Ali Murat
    Reibman, Amy R.
    Jafarkhani, Hamid
    IEEE TRANSACTIONS ON IMAGE PROCESSING, 2014, 23 (05) : 2069 - 2080
  • [48] Combining CNN and transformers for full-reference and no-reference image quality assessment
    Zeng, Chao
    Kwong, Sam
    NEUROCOMPUTING, 2023, 549
  • [49] Full-Reference Video Quality Assessment Using Deep 3D Convolutional Neural Networks
    Dendi, Sathya Veera Reddy
    Krishnappa, Gokul
    Channappayya, Sumohana S.
    2019 25TH NATIONAL CONFERENCE ON COMMUNICATIONS (NCC), 2019,
  • [50] Full-Reference Image Quality Expression via Genetic Programming
    Bakurov, Illya
    Buzzelli, Marco
    Schettini, Raimondo
    Castelli, Mauro
    Vanneschi, Leonardo
    IEEE TRANSACTIONS ON IMAGE PROCESSING, 2023, 32 : 1458 - 1473