针对短视频质量评估难题，提出基于频域先验的端到端分析框架。
FGSVQA: Frequency-Guided Short-form Video Quality Assessment
- 利用CLIP的密集视觉编码器结合频域压缩先验生成感知权重图
- 在多个数据集上达到SRCC 0.736、PLCC 0.787的准确率
- 适合需要高效精准视频质量评估的研究与工业场景
短视频因复杂的生成流程、快速的内容变化和混合失真，给用户生成内容（UGC）的质量评估带来新挑战。为此，我们提出一种端到端视频质量评估（VQA）框架，采用基于CLIP的密集视觉编码器，并引入源自频域的压缩先验，生成兼具伪影与结构感知能力的权重图以进行特征聚合。通过显式分解伪影、结构与原始视觉特征分支，并借助学习的门控模块自适应地时序融合，该方法实现了高精度且高效的质量预测。实验结果表明，该方法在短形式视频数据集上表现优异，平均排名与线性相关性均领先，其中SRCC为0.736，PLCC为0.787，同时保持高效的推理速度。代码与附加结果见：https://github.com/xinyiW915/FGSVQA。
原文摘要 · Abstract (English)
Short-form video poses new challenges to the quality assessment of user-generated content (UGC) due to its complex generation pipeline, rapid content variation, and mixed distortions. To address this challenge, we propose an end-to-end video quality assessment (VQA) framework that employs a dense visual encoder based on CLIP, and incorporates compression priors derived from the frequency domain to generate artifact- and structure-aware weight maps for feature aggregation. By explicitly decomposing artifact, structure, and original visual feature branches and adaptively fusing them over time through a learned gating module, the proposed method achieves accurate and efficient quality prediction. Experimental results show that our method achieves strong performance on short-form video datasets in terms of average rank and linear correlation (SRCC: 0.736, PLCC: 0.787), while maintaining efficient inference runtime. The code and additional results are available at: https://github.com/xinyiW915/FGSVQA.
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