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User-centric QoE model of visual perception for mobile videos

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Abstract

It is crucial for service providers to improve user’s quality of visual perception for mobile users. Quality of experience (QoE) is an important perceptual visual metric. In this paper, we propose a user-centric QoE assessment model by joint considering technological-aware and psychology-aware parameters in the QoE communication ecosystem. For technological parameters, video encoding features are extracted from the video stream, and video content feature is estimated by video analysis. Moreover, user interests are also quantitatively collected as psychology parameters. Then, QoE model is developed by using support vector machine (SVM). Subjective tests have been performed. The collected data from subjective tests are used for training and validation of the proposed model. The experiment results show that the proposed user-centric QoE assessment model performs better in terms of high Pearson correlation coefficient (PCC) and low root-mean-square error (RMSE) compared with the conventional models.

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References

  1. Hamblen, M.: AT & moves closer to usage-based fees for data, Computer world, 2011. [Online]. http://www.computerword.com/s/article/9142012/AT_T_moves+closer_to_usage_based_fees_for_data/

  2. Hu, S.M., Chen, T., Xu, K., Cheng, M.M., Martin, R.R.: Internet visual media processing: a survey with graphics and vision applications. Vis. Comput. 29, 393–405 (2013)

    Article  Google Scholar 

  3. Wang, Z., Bovik, A.C., Sheikh, H.R., Simoncelli, E.P.: Image quality assessment: from error visibility to structural similarity. IEEE Trans. Image Process. 13(4), 600–612 (2004)

    Article  Google Scholar 

  4. Seshadrinathan, K., Bovik, A.C.: Motion tuned spatio-temporal quality assessment of natural videos. IEEE Trans. Image Process. 19(2), 335–350 (2010)

    Article  MathSciNet  MATH  Google Scholar 

  5. Wang, Z., Lu, L., Bovik, A.: Foveation scalable video coding with automatic fixation selection. IEEE Trans. Image Process. 12(2), 243–254 (2003)

    Article  Google Scholar 

  6. Kilkki, K.: Quality of experience in communications ecosystem. J. Univers. Comput. Sci. 14(5), 615–624 (2008)

    Google Scholar 

  7. Kim, H. J.: The QoE evaluation method through the QoS-QoE correlation model. In: Proceedings of the IEEE International Conference on Networked Computing and Advanced Information Management, pp. 719–725 (2008)

  8. Chen, Z., Liao, N., Gu, X., Wu, F., Shi, G.: Hybrid distortion ranking tuned bitstream-layer video quality assessment. IEEE Trans. Circuits Syst. Video Technol. 26(6), 1029–1043 (2016)

    Article  Google Scholar 

  9. Kim, H.J., Choi, S.G.: A study on a QoS/QoE correlation model for QoE evaluation on IPTV service. In: Proceedings of the IEEE International Conference on Advanced Communication Technology, pp. 1377–1382 (2010)

  10. Moorthy, A.K., Choi, L.K., Bovik, A.C., de Veciana, G.: Video quality assessment on mobile devices: subjective, behavioral and objective studies. IEEE J. Sel. Top. Signal Process. 6(6), 652–671 (2012)

    Article  Google Scholar 

  11. Khan, A., Sun, L., Ifeachor, E., Fajardo, J., Liberal, F.: Video quality prediction models based on video content dynamics for H.264 video over UMTS networks, Int. J. Digit. Multimedia Broadcast., Special Issue on IP and Broadcasting Systems Convergence (IPBSC), 2010, pp. 170–179 (2010)

  12. Wang, Z., Lu, L., Bovik, A.: Foveation scalable video coding with automatic fixation selection. IEEE Trans. Image Process. 12(2), 243–254 (2003)

    Article  Google Scholar 

  13. Molnar, A., Muntean, C.H.: Consumer’ risk attitude based personalisation for content delivery. In: Proceedings of the IEEE Consumer Communications and Networking Conference, pp. 265–269 (2012)

  14. Li, F., Fu, S., Liu, Z.Y., Qian, X.M.: A cost-constrained video quality satisfaction study on mobile devices. IEEE Trans. Multimed. 20(5), 1154–1168 (2018)

    Article  Google Scholar 

  15. Callet, P.L., Möller, S., Perkis, A.: Qualinet white paper on definitions of quality of experience. In: European Network on Quality of Experience in Multimedia Systems and Services (Cost Action IC 1003), Lausanne, Switzerland, Version 1.1, Jun. 2012

  16. Ries, M., Froehlich, P., Schatz, R.: QoE evaluation of high-definition IPTV services. In: Proceedings of the IEEE International Conference Radio Elektronika, pp. 1–5 (2011)

  17. Laghari, K.U.R., Connelly, K.: Toward total quality of experience: a QoE model in a communication ecosystem. IEEE Commun. Mag. 50(4), 58–65 (2012)

    Article  Google Scholar 

  18. Methods for the Subjective Assessment of Video Quality, Audio Quality and Audiovisual Quality of Internet Video and Distribution Quality Television in Any Environment, Rec. P. 913 ITU-T, Geneva, Switzerland, 2014

  19. Standard Video Sequences. [Online]. http://trace.eas.asu.edu/yuv/

  20. x264 software, VideoLAN. [Online]. http://www.videolan.org/developers/x264.html/

  21. BT-500-11: Methodology for the Subjective Assessment of the Quality of Television Pictures, Int. Telecommuncation Union Std

  22. Hulusic, V., Debattista, K., Chalmers, A.: Smooth perception. Vis. Comput. 29, 1159–1172 (2013)

    Article  Google Scholar 

  23. Seshadrinathan, K., Bovik, A.C.: Motion-based perceptual quality assessment of video, SPIE processing on Human Vision and Electronic Imaging, Feb. 2009

  24. Garcia, M.N., Raake, A., List. P.: Towards content-related features for parametric video quality prediction of IPTV services. In: Proceedings of the International Conference on Acoustics, Speech, and Signal Processing, pp. 757–760 (2008)

  25. Zargari, F., Mehrabi, M., Moin, M.S.: Compressed domain texture retrieval based on I-frame coding in H.264. In: IEEE International Conference on Multimedia Expo, pp. 831–834 (2007)

  26. Zargari, F., Mehrabi, M., and Ghanbari, M.: A robust compressed domain feature vector for texture based image retrieval, IEEE Int. Content-Based Multimedia Indexing Workshop, pp. 489–495 (2008)

  27. Yang, R.L., Wu, C.P.: Neural networks for exact solution of constrained optimal control problems. IEEE Am. Control Conf. 2, 1379–1383 (1994)

    Google Scholar 

  28. Schölkopf, B., Smola, A., Williamson, R., Bartlett, P.: New support vector algorithms. Neural Comput. 12(5), 1207–1245 (2000)

    Article  Google Scholar 

  29. Vapnik, V.: The Nature of Statistical Learning Theory. Springer, New York (2000)

    Book  MATH  Google Scholar 

  30. ITU-T Rec. G. 1070, “Opinion model for video-telephony applications,” Apr. 2007

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Authors and Affiliations

  1. Xi’an Medical University, Xi’an, 710021, Shaanxi, People’s Republic of China

    Zhihong Xin

  2. Xi’an Jiaotong University, Xi’an, 710049, Shaanxi, People’s Republic of China

    Shuang Fu

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  1. Zhihong Xin
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  2. Shuang Fu
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Correspondence to Zhihong Xin.

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Xin, Z., Fu, S. User-centric QoE model of visual perception for mobile videos. Vis Comput 35, 1245–1254 (2019). https://doi.org/10.1007/s00371-018-1590-y

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