QoE Prediction Model Based on Fuzzy Logic System for Different Video Contents

A model that can predict end user satisfaction or QoE (Quality of Experience) directly from the network QoS (Quality of Service) is still illusive in the field of image processing. This motivates the derivation of a meaningful QoS to QoE mapping function to allow one to be predicted in the absence of the other. This paper presents an affine fuzzy logic based model that can estimate the visual perceptual quality for different video content types using a combination of network level and application level QoS parameters. Video contents are classified based on their spatio-temporal feature extraction. The video QoE is predicted in terms of the Mean Opinion Score (MOS). From the results it is clear that the QoE is video content dependent. Also, the network level parameters have more impact on video quality than the application level parameters. Results show that the Fuzzy logic-based model provides high prediction accuracy. The performance of the model was evaluated using a public dataset with good prediction accuracy (~ 95%). The developed model has use in control methods for streaming standard encoded video.

[1]  ITU-T Rec. P.910 (04/2008) Subjective video quality assessment methods for multimedia applications , 2009 .

[2]  Harilaos Koumaras,et al.  Video Quality Prediction Models Based on Video Content Dynamics for H.264 Video over UMTS Networks , 2010, Int. J. Digit. Multim. Broadcast..

[3]  Ana R. Cavalli,et al.  Estimation of QoE of video traffic using a fuzzy expert system , 2013, 2013 IEEE 10th Consumer Communications and Networking Conference (CCNC).

[4]  J. Mendel.  Fuzzy logic systems for engineering: a tutorial , 1995, Proc. IEEE.

[5]  J. Bih.  Paradigm shift - an introduction to fuzzy logic , 2006, IEEE Potentials.

[6]  F. Liberal,et al.  Video Quality Prediction Model for H.264 Video over UMTS Networks and Their Application in Mobile Video Streaming , 2010, 2010 IEEE International Conference on Communications.

[7]  Filippo Speranza,et al.  Quality-of-Experience perception for video streaming services: Preliminary subjective and objective results , 2012, Proceedings of The 2012 Asia Pacific Signal and Information Processing Association Annual Summit and Conference.

[8]  Li-Xin Wang,et al.  The WM method completed: a flexible fuzzy system approach to data mining , 2003, IEEE Trans. Fuzzy Syst..

[9]  Abdulmotaleb El-Saddik,et al.  A Fuzzy Logic System for Evaluating Quality of Experience of Haptic-Based Applications , 2008, EuroHaptics.

[10]  Jerry M. Mendel,et al.  Generating fuzzy rules by learning from examples , 1992, IEEE Trans. Syst. Man Cybern..

[11]  R. H. Stumpf,et al.  Graphical exploratory data analysis , 1986 .

[12]  A. Mellouk,et al.  Empirical study based on machine learning approach to assess the QoS/QoE correlation , 2012, 2012 17th European Conference on Networks and Optical Communications.

[13]  Lingfen Sun,et al.  Content-Based Video Quality Prediction for MPEG4 Video Streaming over Wireless Networks , 2009, J. Multim..

[14]  Mohammed Alreshoodi,et al.  An empirical study based on a fuzzy logic system to assess the QoS/QoE correlation for layered video streaming , 2013, 2013 IEEE International Conference on Computational Intelligence and Virtual Environments for Measurement Systems and Applications (CIVEMSA).

[15]  Hani Hagras,et al.  An Intelligent Fuzzy Agent Approach for Realising Ambient Intelligence in Intelligent Inhabited Environments , 2005 .