  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.
