trojblue/HiRQA

Note on This Upload

The weights included here (HiRQA.pth, HiRQA-S.pth) are not modified—they are simply re-uploaded from the original authors for easier access and integration within the Hugging Face ecosystem (e.g., hf_hub_download, XetFS, etc.). All credit for the models and methodology goes to the original authors.

Refer to original github repo for details: https://github.com/uf-robopi/HiRQA.

Model Variants

• HiRQA (ResNet-50 backbone) — higher accuracy, suitable for offline evaluation.
• HiRQA-S (ResNet-18 backbone) — optimized for real-time applications.

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HiRQA: Hierarchical Ranking and Quality Alignment for Opinion-Unaware Image Quality Assessment

HiRQA is an opinion-unaware no-reference image quality assessment (NR-IQA) framework that learns a hierarchical, quality-aware embedding space without requiring human opinion scores during training. It introduces three key components:

• Pair-of-Pairs Ranking Loss — enforces consistent hierarchical relationships between distortions.
• Embedding Distance Consistency Loss — stabilizes relative quality ordering.
• Contrastive Image–Text Alignment — improves generalization to real-world distortions via CLIP-based semantic cues.

HiRQA requires only a single distorted image at inference, and its lightweight variant HiRQA-S provides real-time performance.