No-reference Video Quality Assessment Based on Spatio-temporal Perception Feature Fusion

Quality assessment of real, user-generated content videos lacking reference videos is a challenging problem. For such scenarios, we propose an objective quality assessment method for no-reference video from the spatio-temporal perception characteristics of the video. First, a dual-branch network is constructed from distorted video frames and frame difference maps generated from a global perspective, considering the interaction between spatial and temporal information, incorporating a motion-guided attention module, and fusing spatio-temporal perceptual features from a multiscale perspective. Second, an InceptionTime network is introduced to further perform long-term sequence fusion to obtain the final perceptual quality score. Finally, the results were evaluated on the four user-generated content video databases of KoNViD-1k, CVD2014, LIVE_VQC and LIVE_Qualcomm, and the experimental results show that the network outperforms other partially recent no-reference VQA methods.