AI RESEARCH
HiRQA: Hierarchical Ranking and Quality Alignment for Opinion-Unaware Image Quality Assessment
arXiv CS.CV
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ArXi:2508.15130v2 Announce Type: replace Despite significant progress in no-reference image quality assessment (NR-IQA), dataset biases and reliance on subjective labels continue to hinder their generalization performance. We propose HiRQA (Hierarchical Ranking and Quality Alignment), a self-supervised, opinion-unaware framework that offers a hierarchical, quality-aware embedding through a combination of ranking and contrastive learning.