HiRQA is a self-supervised NR-IQA framework trained on synthetic distortions, using a higher-order ranking loss, embedding distance loss, and text-guided contrastive alignment, claimed to generalize to authentic distortions.
Boosting clip adaptation for image quality assessment via meta-prompt learning and gradient regularization, 2024
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HiRQA: Hierarchical Ranking and Quality Alignment for Opinion-Unaware Image Quality Assessment
HiRQA is a self-supervised NR-IQA framework trained on synthetic distortions, using a higher-order ranking loss, embedding distance loss, and text-guided contrastive alignment, claimed to generalize to authentic distortions.