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Proceedings Paper

Video quality assessment using neural network based on multi-feature extraction
Author(s): Susu Yao; Weisi Lin; Zhongkang Lu; EePing Ong; Xiao Kang Yang
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Paper Abstract

In this paper, we propose a new video quality evaluation method based on multi-feature and radial basis function neural network. Multi-feature is extracted from a degraded image sequence and its reference sequence, including error energy, activity-masking and luminance-masking as well as blockiness and blurring features. Based on these factors we apply a radial basis function neural network as a classifier to give quality assessment scores. After training with the subjective mean opinion scores (MOS) data of VQEG test sequences, the neural network model can be used to evaluate video quality with good correlation performance in terms of accuracy and consistency measurements.

Paper Details

Date Published: 23 June 2003
PDF: 9 pages
Proc. SPIE 5150, Visual Communications and Image Processing 2003, (23 June 2003); doi: 10.1117/12.510050
Show Author Affiliations
Susu Yao, Institute for Infocomm Research (Singapore)
Weisi Lin, Institute for Infocomm Research (Singapore)
Zhongkang Lu, Institute for Infocomm Research (Singapore)
EePing Ong, Institute for Infocomm Research (Singapore)
Xiao Kang Yang, Institute for Infocomm Research (Singapore)

Published in SPIE Proceedings Vol. 5150:
Visual Communications and Image Processing 2003
Touradj Ebrahimi; Thomas Sikora, Editor(s)

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