Video quality assessment using neural network based on multi-feature extraction
Susu Yao, Weisi Lin, Zhongkang Lu, Ee Ping Ong, Xiao K. Yang · Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE · 2003
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.