AI RESEARCH
Breaking Annotation Barriers: Generalized Video Quality Assessment via Ranking-based Self-Supervision
arXiv CS.CV
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ArXi:2505.03631v4 Announce Type: replace Video quality assessment (VQA) is essential for quantifying perceptual quality in various video processing workflows, spanning from camera capture systems to over-the-top streaming platforms. While recent supervised VQA models have made substantial progress, the reliance on manually annotated datasets -- a process that is labor-intensive, costly, and difficult to scale up -- has hindered further optimization of their generalization to unseen video content and distortions. To bridge this gap, we.