Modular Blind Video Quality Assessment
Authors: Wen Wen, Mu Li, Yabin Zhang, Yiting Liao, Junlin Li, Li Zhang, Kede Ma

Blind video quality assessment (BVQA) plays a pivotal role in evaluating and improving the viewing experience of end-users across a wide range of video-based platforms and services. Contemporary deep learning-based models primarily analyze video content in its aggressively subsampled format while being blind to the impact of the actual spatial resolution and frame rate on video quality. In this paper we propose a modular BVQA model and a method of training it to improve its modularity. Our model comprises a base quality predictor a spatial rectifier and a temporal rectifier responding to the visual content and distortion spatial resolution and frame rate changes on video quality respectively. During training spatial and temporal rectifiers are dropped out with some probabilities to render the base quality predictor a standalone BVQA model which should work better with the rectifiers. Extensive experiments on both professionally-generated content and user-generated content video databases show that our quality model achieves superior or comparable performance to current methods. Additionally the modularity of our model offers an opportunity to analyze existing video quality databases in terms of their spatial and temporal complexity.