  In learning vision-language representations from web-scale data, the
contrastive language-image pre-training (CLIP) mechanism has demonstrated a
remarkable performance in many vision tasks. However, its application to the
widely studied video quality assessment (VQA) task is still an open issue. In
this paper, we propose an efficient and effective CLIP-based Transformer method
for the VQA problem (CLIPVQA). Specifically, we first design an effective video
frame perception paradigm with the goal of extracting the rich spatiotemporal
quality and content information among video frames. Then, the spatiotemporal
quality features are adequately integrated together using a self-attention
mechanism to yield video-level quality representation. To utilize the quality
language descriptions of videos for supervision, we develop a CLIP-based
encoder for language embedding, which is then fully aggregated with the
generated content information via a cross-attention module for producing
video-language representation. Finally, the video-level quality and
video-language representations are fused together for final video quality
prediction, where a vectorized regression loss is employed for efficient
end-to-end optimization. Comprehensive experiments are conducted on eight
in-the-wild video datasets with diverse resolutions to evaluate the performance
of CLIPVQA. The experimental results show that the proposed CLIPVQA achieves
new state-of-the-art VQA performance and up to 37% better generalizability than
existing benchmark VQA methods. A series of ablation studies are also performed
to validate the effectiveness of each module in CLIPVQA.
