AI RESEARCH
SIQA: Toward Reliable Scientific Image Quality Assessment
arXiv CS.CV
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ArXi:2603.06700v1 Announce Type: new Scientific images fundamentally differ from natural and AI-generated images in that they encode structured domain knowledge rather than merely depict visual scenes. Assessing their quality therefore requires evaluating not only perceptual fidelity but also scientific correctness and logical completeness. However, existing image quality assessment (IQA) paradigms primarily focus on perceptual distortions or image-text alignment, implicitly assuming that depicted content is factually valid.