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Modeling of Quality of Experience in No-Reference Model

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Warianty tytułu
Języki publikacji
EN
Abstrakty
EN
The key objective of no-reference (NR) visual metrics is to predict the end-user experience concerning remotely delivered video content. Rapidly increasing demand for easily accessible, high quality video material makes it crucial for service providers to test the user experience without the need for comparison with reference material. Nevertheless, the QoE measurement is not enough. The information about the source or error is very important as well. Therefore, the described system is based on calculating numerous different NR indicators, which are combined to provide the overall quality score. In this paper, more quality indicators than are used in the QoE calculation are described, since some of them detect specific errors. Such specific errors are dificult to include in a global QoE model but are important from the operation point of view.
Słowa kluczowe
Rocznik
Tom
Strony
11--17
Opis fizyczny
Bibliogr. 14 poz., rys., tab.
Twórcy
autor
  • AGH University of Science and Technology, Department of Telecommunications, Mickiewicza av. 30, 30-059 Krakow, Poland
autor
  • AGH University of Science and Technology, Department of Telecommunications, Mickiewicza av. 30, 30-059 Krakow, Poland
autor
  • AGH University of Science and Technology, Department of Telecommunications, Mickiewicza av. 30, 30-059 Krakow, Poland
Bibliografia
  • [1] M. Leszczuk, M. Hanusiak, M. C. Q. Farias, E. Wyckens, and G. Heston, “Recent developments in visual quality monitoring by key performance indicators”, Multim. Tools and Appl., vol. 75, no. 17, pp. 10745–10767, 2014 (doi: 10.1007/s11042-014-2229-2).
  • [2] M. H. Pinson, L. K. Choi, and A. C. Bovik, “Temporal video quality model accounting for variable frame delay distortions”, IEEE Trans. on Broadcast., vol. 60, no. 4, pp. 637–649, 2014 (doi: 10.1109/TBC.2014.2365260).
  • [3] M. Leszczuk et al., “Key indicators for monitoring of audiovisual quality”, in Proc. 22nd Sig. Process. and Communications Appl. Conf. SIU 2014, Trabzon, Turkey, 2014, pp. 2301–2305 (doi: 10.1109/SIU.2014.6830724).
  • [4] K. Zhu, C. Li, V. Asari, and D. Saupe, “No-reference video quality assessment based on artifact measurement and statistical analysis”, IEEE Trans. on Circ. and Sys. for Video Technol., vol. 25, no. 4, pp. 533–546, 2015 (doi: 10.1109/TCSVT.2014.2363737).
  • [5] X. Li, Q. Guo, and X. Lu, “Spatiotemporal statistics for video quality assessment”, IEEE Trans. on Image Process., vol. 25, no. 7, pp. 3329–3342, 2016 (doi: 10.1109/TIP.2016.2568752).
  • [6] M. Leszczuk, “Assessing task-based video quality – a journey from subjective psycho-physical experiments to objective quality models”, in Multimedia Communications, Services and Security. 4th International Conference, MCSS 2011, Krakow, Poland, June 2-3, 2011. Proceedings, A. Dziech and A. Czyżewski, Eds. CCIS, vol. 149, pp. 91–99. Springer, 2011 (doi: 10.1007/978-3-642-21512-4 11).
  • [7] L. Janowski and Z. Papir, “Modeling subjective tests of quality of experience with a generalized linear model”, in Proc. Quality of Int. Worksh. on Multim. Exper. QoMEx 2009, San Diego, CA, USA, 2009, pp. 35–40 (doi: 10.1109/QOMEX.2009.5246979).
  • [8] P. Romaniak, L. Janowski, M. Leszczuk, and Z. Papir, “Perceptual quality assessment for H.264/AVC compression”, in Proc. IEEE Consumer Commun. and Netw. Conf. CCNC 2012, Las Vegas, NV, USA, 2012, pp. 597–602 (doi: 10.1109/CCNC.2012.6181021).
  • [9] M. Mu, P. Romaniak, A. Mauthe, M. Leszczuk, L. Janowski, and E. Cerqueira, “Framework for the integrated video quality assessment”, Multim. Tools and Appl., vol. 61, no. 3, pp. 787–817, 2012 (doi: 10.1007/s11042-011-0946-3).
  • [10] J. Søgaard, S. Tavakoli, K. Brunnström, and N. García, “Subjective analysis and objective characterization of adaptive bitrate videos”, in Proc. IS&T Int. Symp. on Elec. Imaging 2016: Image Quality and System Performance XIII, San Francisco, CA, USA, 2016 (doi: 10.2352/ISSN.2470-1173.2016.16HVEI-105).
  • [11] Video quality [Online]. Available: http://vq.kt.agh.edu.pl (2016).
  • [12] C. Fenimore, J. Libert, and S. Wolf, “Perceptual effects of noise in digital video compression”, in in Proc. 140th SMPTE Tech. Conf. and Exhibit, Pasadena, CA, USA, 1998 (doi: 10.5594/M00301).
  • [13] L. Janowski and P. Romaniak, “QoE as a Function of Frame Rate and Resolution Changes”, in Future Multimedia Networking Third International Workshop, FMN 2010, Kraków, Poland, June 17-18, 2010. Proceedings, S. Zeadally, E. Cerqueira, M. Curado, and M. Leszczuk, Eds. LNCS, vol. 6157, pp. 34–45. Springer, 2010 (doi: 10.1007/978-3-642-13789-1 4).
  • [14] G. James, D. Witten, T. Hastie, and R. Tibshirani, An Introduction to Statistical Learning: with Applications in R. Springer, 2014.
Uwagi
Opracowanie ze środków MNiSW w ramach umowy 812/P-DUN/2016 na działalność upowszechniającą naukę (zadania 2017).
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-116c0efb-b56e-44f4-bd4e-c002c912d126
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