A Lightweight No-reference Video Quality Assessment Method

Recently, quality assessment for user-generated content (UGC) videos has become a challenging task due to the absence of reference videos and the presence of complex distortions. Prior methods has highlighted the effectiveness of semantic features for quality assessment. However, these models are incapable for real-time prediction and efficient computation in practical applications. In this paper, we design a lightweight no-reference video quality assessment model leveraging pretrained lightweight network for semantic understanding and utilizing a low-level CNN for distortion features. The temporal features and spatial features are extracted respectively in the semantic and low-level scales, and then they are multiplied and integrated to obtain the video-level score. Experiments on UGC video quality databases show that our proposed model achieves comparable accuracy to state-of-the-art benchmarks while providing real-time performance on GPU.