关键词: 空间熵, 频谱熵, 支持向量回归, 视频质量评价

Abstract: In order to solve the problem of video distortion caused by video compression, this paper proposes a no-reference Video Quality Assessment（VQA） model that utilizes many spatial and frequency features based on the analysis of perceptual feature of video’s quality. This method mainly extracts spatial and frequency perceptual features, including Gray-level Gradient Co-occurrence Matrix（GGCM）, spatial entropy, spectral entropy, correntropy and a natural index features. In the process of extracting the video features, this method calculates the variance of video frame as the whole video features, which has a better performance on distinguish between different types of distortion of the video than the traditional ways. Finally, these features are trained by Support Vector Regression（SVR） model to build the relationship with perceptual features and quality of distorted video. The proposed model is tested on LIVE VQA database and on IVP VQA database and experiment results prove that the proposed algorithm can achieve much higher consistency with the subjective evaluation than state-of-the-art published algorithm.

Key words: spatial entropy, spectral entropy, support vector regression, video quality assessment