FGSVQA: Frequency-Guided Short-form Video Quality Assessment

• URL: http://arxiv.org/abs/2605.20016v1
• Date: Tue, 19 May 2026 15:44:45 GMT
• Title: FGSVQA: Frequency-Guided Short-form Video Quality Assessment
• Abstract summary: Short-form video poses new challenges to the quality assessment of user-generated content.<n>We propose an end-to-end video quality assessment framework that employs a dense visual encoder based on CLIP.<n>We show that our method achieves strong performance on short-form video datasets in terms of average rank and linear correlation.
• Score: 9.172799792564009
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: 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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