In this work, we propose a no-reference video quality assessment method,
aiming to achieve high-generalization capability in cross-content, -resolution
and -frame rate quality prediction. In particular, we evaluate the quality of a
video by learning effective feature representations in spatial-temporal domain.
In the spatial domain, to tackle the resolution and content variations, we
impose the Gaussian distribution constraints on the quality features. The
unified distribution can significantly reduce the domain gap between different
video samples, resulting in a more generalized quality feature representation.
Along the temporal dimension, inspired by the mechanism of visual perception,
we propose a pyramid temporal aggregation module by involving the short-term
and long-term memory to aggregate the frame-level quality. Experiments show
that our method outperforms the state-of-the-art methods on cross-dataset
settings, and achieves comparable performance on intra-dataset configurations,
demonstrating the high-generalization capability of the proposed method.

该研究提出了一种无参考视频质量评估方法，通过学习空间 - 时间域内的有效特征表示，结合高斯分布约束和金字塔时间聚合模块等方法，达到跨内容、分辨率和帧率的高泛化能力。实验表明，该方法在跨数据集设置方面优于现有方法，在内部数据集配置方面的性能相当，展示了该方法的高泛化能力。