A content-oriented no-reference perceptual video quality assessment method for computer graphics animation videos

Abstract

In this paper, we propose a content-oriented no-reference (NR) perceptual video quality assessment (VQA) method for computer graphics (CG) animation videos. First, we extract features in terms of spatiotemporal information and its visual perception from the videos as inputs of our proposed artificial neural network-based VQA model. Second, to facilitate the video quality evaluation, we apply a convolutional neural network (CNN) in the VQA model to generate weight factors for the input features adaptively according to the different types of CG content in videos. Third, we build a subjective CG video quality database for validation of VQA metrics. Experiments demonstrated that our method achieved superior performance in terms of evaluating the quality of CG animation videos. Both the code and proposed database are publicly available at https://github.com/WeizhiXian/CGVQA. The corresponding newly established database is available at https:// pan.baidu.com/s/1_P2ZNrLzJwZfG6xa6tKnDQ (password: cgvq).