Video quality assessment using neural network based on multi-feature extraction

Susu Yao; Weisi Lin; Zhongkang Lu; EePing Ong; Xiao Kang Yang

doi:10.1117/12.510050

23 June 2003 Video quality assessment using neural network based on multi-feature extraction

Susu Yao, Weisi Lin, Zhongkang Lu, EePing Ong, Xiao Kang Yang

Proceedings Volume 5150, Visual Communications and Image Processing 2003; (2003) https://doi.org/10.1117/12.510050
Event: Visual Communications and Image Processing 2003, 2003, Lugano, Switzerland

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.

Citation Download Citation

Susu Yao, Weisi Lin, Zhongkang Lu, EePing Ong, and Xiao Kang Yang "Video quality assessment using neural network based on multi-feature extraction", Proc. SPIE 5150, Visual Communications and Image Processing 2003, (23 June 2003); https://doi.org/10.1117/12.510050

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