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Conference Papers Year : 2016

Video Quality assessment based on statistical selection approach for QoE factors dependency

Y. Ben Youssef
  • Function : Author
A Mellouk
  • Function : Author
CIR
A. Meriem
  • Function : Author
S. Tabbane
  • Function : Author

Abstract

Quality of Experience (QoE) becomes a topic of utmost eminence for service providers and the major factor in the success of multimedia services. Thus, it is challenging to investigate thoroughly the human side of QoE in order to find out the impact of factors that affect user satisfaction. In this paper, we provide a structured way to build an accurate and objective QoE model. In order to serve this purpose, Principal Component Analysis (PCA) and Analytic Hierarchy Process (AHP) approaches are combined and used to select the factors which have a significant impact on user satisfaction and essential for predicting QoE. Random Forest technique is used as a machine learning method to classify original datasets based on real environment, collected in the form of subjective scores. The results show an efficient estimation of QoE with respect to the five most influencing factors (frame rate, video size, audio rate, resolution and mean bit rate).
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Dates and versions

hal-01676585 , version 1 (05-01-2018)

Identifiers

  • HAL Id : hal-01676585 , version 1

Cite

Y. Ben Youssef, A Mellouk, A. Meriem, S. Tabbane. Video Quality assessment based on statistical selection approach for QoE factors dependency. Proc. Of the IEEE International Conference on Global Communications, GlobeCom 2016, 2016, Washington DC, United States. pp.1-6. ⟨hal-01676585⟩

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