No-reference video quality measurement with support vector regression.
A novel approach for no-reference video quality measurement is proposed in this paper. Firstly, various feature extraction methods are used to quantify the quality of videos. Then, a support vector regression model is trained and adopted to predict unseen samples. Six different regression models are compared with the support vector regression model. The experimental results indicate that the combination of different video quality features with a support vector regression model can outperform other methods for no-reference video quality measurement significantly.
Lian
Huicheng
H
School of Computer Engineering and Science, Shanghai University, P. Box 147, No. 149, Yanchang Road, Shanghai, 200072, China. lianhc@shu.edu.cn
D000465:Algorithms; D001185:Artificial Intelligence; D003198:Computer Simulation; D007091:Image Processing, Computer-Assisted / Q000379:methods*; D016014:Linear Models; D055641:Mathematical Concepts; D016571:Neural Networks, Computer; D010363:Pattern Recognition, Automated; D012044:Regression Analysis; D013663:Teaching; D014741:Video Recording / Q000379:methods*