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 the video content in its aggressively downsampled format,
while being blind to the impact of 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. Specifically, 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 so as to make 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. Furthermore, the
modularity of our model offers a great opportunity to analyze existing video
quality databases in terms of their spatial and temporal complexities. Last,
our BVQA model is cost-effective to add other quality-relevant video attributes
such as dynamic range and color gamut as additional rectifiers.

通过提出一个模块化的盲视频质量评估模型，该模型结合了基本质量预测器、空间矫正器和时间矫正器，分别对视觉内容和失真、空间分辨率和帧率变化对视频质量的影响进行评估，实验证明该模型在视频数据库上取得了优秀的性能，并能够成本效益地添加其他与质量相关的视频属性。