Perceived video quality evaluation based on interactive/repulsive relation between the QoE IFs

The user satisfaction measurement has gained high attention from Network Operators (NOs) and Service Providers (SPs) because their businesses are highly dependent on the user's satisfaction. Generally, the traditional strategies to measure the user's perception are based on Quality of Service (QoS), which is not sufficient to reflect the real user's perceived quality. Therefore, NOs and SPs start to develop new strategies based on the Quality of Experience (QoE) metric to analyze the relationship between the user's satisfaction and influence factors (QoE IFs). In this paper, a new method to build a predictive model to estimate user's satisfaction in terms of Mean Opinion Score (MOS) is proposed. The proposed method uses the dataset collected using the controlled testbed based on the YouTube video service. In the proposed model, the correlation matrix is used to develop a new heuristic method that used back-jumping technique to select the most beneficial factors to predict the optimal user's satisfaction.

[1]  Tshilidzi Marwala,et al.  Hyperspectral image classification using random forests and neural networks , 2012 .

[2]  Faruk Kazi,et al.  Support-Vector-Machine-Based Proactive Cascade Prediction in Smart Grid , 2015 .

[3]  Francisco Herrera,et al.  Learning from data using the R package "FRBS" , 2014, 2014 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE).

[4]  Jacob Benesty,et al.  On the Importance of the Pearson Correlation Coefficient in Noise Reduction , 2008, IEEE Transactions on Audio, Speech, and Language Processing.

[5]  Lamine Amour,et al.  An Open Source Platform for Perceived Video Quality Evaluation , 2015, Q2SWinet@MSWiM.

[6]  Lamine Amour,et al.  A Hierarchical Classification Model of QoE Influence Factors , 2015, WWIC.

[7]  Javier Lorca,et al.  YouTube QoE evaluation tool for Android wireless terminals , 2014, EURASIP J. Wirel. Commun. Netw..

[8]  Patrick Prosser,et al.  HYBRID ALGORITHMS FOR THE CONSTRAINT SATISFACTION PROBLEM , 1993, Comput. Intell..

[9]  Brice Augustin,et al.  Crowd-sourcing framework to assess QoE , 2014, 2014 IEEE International Conference on Communications (ICC).

[10]  Chin-Laung Lei,et al.  A crowdsourceable QoE evaluation framework for multimedia content , 2009, ACM Multimedia.

[11]  Srinivasan Seshan,et al.  Developing a predictive model of quality of experience for internet video , 2013, SIGCOMM.

[12]  Phuoc Tran-Gia,et al.  Quantification of YouTube QoE via Crowdsourcing , 2011, 2011 IEEE International Symposium on Multimedia.