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.
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