Abstract
In this paper, we propose a no-reference video quality assessment (VQA) method based on Convolutional Neural Network (CNN) and Multi-Regression (CNN-MR). It is universal for non-specific types of distortion. First, we innovatively introduce the 2D convolutional neural network into VQA model to learn the spatial quality features at frame level. Second, the motion information is extracted as temporal quality features at sequence level. Finally, a multi-regression model is proposed to comprehensively measure video quality and the final quality is selected out from them according to human's psychological perception. The proposed CNN-MR method is tested on the famous LIVE database with numerous kinds of distortions. Compared with other state-of-the-art no-reference VQA methods, the proposed method runs much faster while keeping the similar performance. As a no-reference VQA method, it is even comparable with most of the state-ofthe-art full-reference VQA methods.
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