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.

非参考视频质量评估中，通过引入经过多个图像质量评估数据集预训练的增强空间感知模块和轻量级的时域融合模块，本研究实现了使用 Swin Transformer V2 作为本地级别空间特征提取器，并通过一系列 Transformer 层融合这些多阶段表示。此外，还利用时域 Transformer 在视频中进行时空特征融合。为了适应不同比特率的压缩视频，我们采用粗到精的对比策略，以增强模型区分来自不同比特率视频的特征的能力。这是一篇扩展版的一页摘要。