AI RESEARCH
MetaSR: Content-Adaptive Metadata Orchestration for Generative Super-Resolution
arXiv CS.AI
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ArXi:2604.26244v1 Announce Type: cross We study generative super-resolution (SR) in real-world scenarios where content and degradations vary across domains, genres, and segments. For example, images and videos may alternate between text overlays, fast motion, smooth cartoons, and low-light faces, each benefiting from different forms of side information. Existing metadata-guided SR methods typically use a fixed conditioning design, which is suboptimal when useful cues are content dependent and transmission budgets are limited.