JOURNAL OF MULTIMEDIA (JMM)
ISSN : 1796-2048
Volume : 4    Issue : 4    Date : August 2009

Content-Based Video Quality Prediction for MPEG4 Video Streaming over Wireless Networks
Asiya Khan, Lingfen Sun, and Emmanuel Ifeachor
Page(s): 228-239
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Abstract
There are many parameters that affect video quality but their combined effect is not well identified
and understood when video is transmitted over mobile/ wireless networks. In addition, video content
has an impact on video quality under same network conditions. The main aim of this paper is the
prediction of video quality combining the application and network level parameters for all content
types. Firstly, video sequences are classified into groups representing different content types using
cluster analysis. The classification of contents is based on the temporal (movement) and spatial
(edges, brightness) feature extraction. Second, to study and analyze the behaviour of video quality
for wide range variations of a set of selected parameters. Finally, to develop two learning models
based on – (1) ANFIS to estimate the visual perceptual quality in terms of the Mean Opinion Score
(MOS) and decodable frame rate (Q value) and (2) regression modeling to estimate the visual
perceptual quality in terms of the MOS. We trained three ANFIS-based ANNs and regression based-
models for the three distinct content types using a combination of network and application level
parameters and tested the two models using unseen dataset. We confirmed that the video quality is
more sensitive to network level compared to application level parameters. Preliminary results show
that a good prediction accuracy was obtained from both models. However, the regression based
model performed better in terms of the correlation coefficient and the root mean squared error. The
work should help in the development of a reference-free video prediction model and Quality of
Service (QoS) control methods for video over wireless/mobile networks.

Index Terms
ANFIS, neural networks, Content clustering, MOS, MPEG4, video quality evaluation.