AI RESEARCH
FGSVQA: Frequency-Guided Short-form Video Quality Assessment
arXiv CS.CV
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ArXi:2605.20016v1 Announce Type: cross 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.