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International Journal of Digital Multimedia Broadcasting
Volume 2010, Article ID 608138, 17 pages
http://dx.doi.org/10.1155/2010/608138
Research Article

Video Quality Prediction Models Based on Video Content Dynamics for H.264 Video over UMTS Networks

1Centre for Signal Processing and Multimedia Communication, School of Computing, Communications and Electronics, University of Plymouth, Plymouth PL4 8AA, UK
2Department of Electronics and Telecommunications, University of the Basque Country (UPV/EHU), 48013 Bilbao, Spain
3Institute of Informatics and Telecommunications, NCSR Demokritos, 15310 Athens, Greece

Received 30 October 2009; Accepted 18 February 2010

Academic Editor: Daniel Negru

Copyright © 2010 Asiya Khan et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Abstract

The aim of this paper is to present video quality prediction models for objective non-intrusive, prediction of H.264 encoded video for all content types combining parameters both in the physical and application layer over Universal Mobile Telecommunication Systems (UMTS) networks. In order to characterize the Quality of Service (QoS) level, a learning model based on Adaptive Neural Fuzzy Inference System (ANFIS) and a second model based on non-linear regression analysis is proposed to predict the video quality in terms of the Mean Opinion Score (MOS). The objective of the paper is two-fold. First, to find the impact of QoS parameters on end-to-end video quality for H.264 encoded video. Second, to develop learning models based on ANFIS and non-linear regression analysis to predict video quality over UMTS networks by considering the impact of radio link loss models. The loss models considered are 2-state Markov models. Both the models are trained with a combination of physical and application layer parameters and validated with unseen dataset. Preliminary results show that good prediction accuracy was obtained from both the models. The work should help in the development of a reference-free video prediction model and QoS control methods for video over UMTS networks.