Video Quality assessment based on statistical selection approach for QoE factors dependency
| Autor: | Youssef, Y. Ben, Mellouk, A, Meriem, A., Tabbane, S. |
|---|---|
| Přispěvatelé: | Amirat, Yacine, CIR, Laboratoire Images, Signaux et Systèmes Intelligents (LISSI), Université Paris-Est Créteil Val-de-Marne - Paris 12 (UPEC UP12)-Université Paris-Est Créteil Val-de-Marne - Paris 12 (UPEC UP12), Laboratoire Images, Signaux et Systèmes Intelligents ( LISSI ), Université Paris-Est Créteil Val-de-Marne - Paris 12 ( UPEC UP12 ) -Université Paris-Est Créteil Val-de-Marne - Paris 12 ( UPEC UP12 ) |
| Jazyk: | angličtina |
| Rok vydání: | 2016 |
| Předmět: |
[INFO.INFO-NI]Computer Science [cs]/Networking and Internet Architecture [cs.NI]
Quality of service Analytic hierarchy process Streaming media [INFO.INFO-NI] Computer Science [cs]/Networking and Internet Architecture [cs.NI] [ INFO.INFO-NI ] Computer Science [cs]/Networking and Internet Architecture [cs.NI] Context Principal component analysis ComputingMilieux_MISCELLANEOUS Correlation |
| Zdroj: | Proc. Of the IEEE International Conference on Global Communications, GlobeCom 2016 Proc. Of the IEEE International Conference on Global Communications, GlobeCom 2016, 2016, Washington DC, United States. pp.1-6 Proc. Of the IEEE International Conference on Global Communications, GlobeCom 2016, 2016, Washington DC, United States. pp.1-6, 2016 |
| Popis: | International audience; 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). |
| Databáze: | OpenAIRE |
| Externí odkaz: |
|