  The objective of non-reference video quality assessment is to evaluate the
quality of distorted video without access to reference high-definition
references. In this study, we introduce an enhanced spatial perception module,
pre-trained on multiple image quality assessment datasets, and a lightweight
temporal fusion module to address the no-reference visual quality assessment
(NR-VQA) task. This model implements Swin Transformer V2 as a local-level
spatial feature extractor and fuses these multi-stage representations through a
series of transformer layers. Furthermore, a temporal transformer is utilized
for spatiotemporal feature fusion across the video. To accommodate compressed
videos of varying bitrates, we incorporate a coarse-to-fine contrastive
strategy to enrich the model's capability to discriminate features from videos
of different bitrates. This is an expanded version of the one-page abstract.
