Modeling of Quality of Experience in No-Reference Model
DOI:
https://doi.org/10.26636/jtit.2017.114517Keywords:
QoE, QoS, OTT, no-reference, quality assessmentAbstract
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 difficult to include in a global QoE model but are important from the operation point of view.
Downloads
References
[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).
View in Google Scholar
[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).
View in Google Scholar
[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).
View in Google Scholar
[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).
View in Google Scholar
[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).
View in Google Scholar
[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, Kraków, 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).
View in Google Scholar
[7] L. Janowski and Z. Papir, “Modeling subjective tests of quality of experience with a generalized linear model”, in Proc. Int. Worksh. on Quality of Multim. Exper. QoMEx 2009, San Diego, CA, USA, 2009, pp. 35–40 (doi: 10.1109/QOMEX.2009.5246979).
View in Google Scholar
[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).
View in Google Scholar
[9] M. Mu et al., “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).
View in Google Scholar
[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).
View in Google Scholar
[11] Video quality [Online]. Available: http://vq.kt.agh.edu.pl (2016).
View in Google Scholar
[12] C. Fenimore, J. Libert, and S. Wolf, “Perceptual effects of noise in digital video compression”, in Proc. 140th SMPTE Tech. Conf. and Exhibit, Pasadena, CA, USA, 1998 (doi: 10.5594/M00301).
View in Google Scholar
[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).
View in Google Scholar
[14] G. James, D. Witten, T. Hastie, and R. Tibshirani, An Introduction to Statistical Learning: with Applications in R. Springer, 2014.
View in Google Scholar
Downloads
Submitted
Published
Issue
Section
License
Copyright (c) 2017 Journal of Telecommunications and Information Technology
This work is licensed under a Creative Commons Attribution 4.0 International License.
