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Prediction of perceptual quality for mobile video using fuzzy inference systems

Alreshoodi, M and Danish, E and Woods, J and Fernando, A and De Alwis, C (2015) 'Prediction of perceptual quality for mobile video using fuzzy inference systems.' IEEE Transactions on Consumer Electronics, 61 (4). 546 - 554. ISSN 0098-3063

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Along with the rapid growth in consumer adoption of modern portable devices, video streaming is expected to dominate a large share of the global internet traffic in the near future. In the wireless communications domain, this trend creates considerable challenges to consumers' quality of experience (QoE). From a consumer-focused vision, predicting perceptual video quality is extremely important for QoE-based service provisioning. However, available QoE measurement techniques that adopt a full reference model are impractical in real-time transmission since they require the original video sequence to be available at the receiver's end. Therefore, the primary aim of this study is to present a cross-layer no-reference prediction model for the perceptual quality of 3D video in the wireless domain. The contributions of this study are twofold: first, the impact of selected quality of service (QoS) parameters from both encoding and network levels on QoE is investigated. Also, the obtained QoS/QoE correlation is backed by thorough statistical analysis. Second, a prediction model based on fuzzy logic inference systems (FIS) is developed by mapping chosen QoS parameters to the measured QoE. This model enables a non-intrusive prediction of 3D visual quality. Conclusive results show a significantly high correlation between the predicted video quality and the objectively measured mean opinion scores (MOS). Objective MOS is also validated through methodical subjective assessments. For consumer's QoE, this study advances the development of reference-free video quality prediction models and QoE control methods for 3D video streaming.

Item Type: Article
Subjects: Q Science > QA Mathematics > QA75 Electronic computers. Computer science
Divisions: Faculty of Science and Health > Computer Science and Electronic Engineering, School of
Depositing User: Jim Jamieson
Date Deposited: 26 Jan 2016 10:44
Last Modified: 29 Mar 2021 11:15

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