Research map / Image generation / Autoregressive and latent generation
Content-Variant Reference Image Quality Assessment via Knowledge Distillation
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| add non-aligned reference images without distillation (ablation) | NR-student baseline · TID2013 and KonIQ-10K | SRCC: 0.631 -> 0.679; 0.317 -> 0.352 | Helped |
| add knowledge distillation from the FR-teacher (ablation) | NAR-student · TID2013 and KonIQ-10K | SRCC: 0.679 -> 0.691; 0.352 -> 0.416 | Helped |
| add knowledge distillation from the FR-teacher (ablation) | NAR-student · KonIQ-10K, across different NAR references | SRCC standard deviation: 0.072 -> 0.004 | Helped |
| use pixel-aligned instead of content-variant reference images (ablation) | NAR-student · LIVE, CSIQ, and TID2013 | SRCC: 0.913 -> 0.958; 0.829 -> 0.937; 0.691 -> 0.846 | Helped |
| use content-similar instead of content-variant NAR references (ablation) | NAR-student · LIVE, CSIQ, and TID2013 | SRCC: 0.913 -> 0.931; 0.829 -> 0.862; 0.691 -> 0.720 | Helped |
| use higher-quality HQ reference images (ablation) | NAR-student · TID2013 and KonIQ-10K | SRCC: 0.665 to 0.691; 0.392 to 0.416 | Helped |
| increase the number and size of input patches (ablation) | CVRKD-IQA FR-teacher and NAR-student · TID2013 | SRCC | Helped |
| use content-variant non-aligned references with FR-teacher distillation | CVRKD-IQA · LIVE, CSIQ, TID2013, and KonIQ-10K | SRCC: 0.913; 0.829; 0.691; 0.416 | Helped |
Closest papers by meaning
- Toward Generalized Image Quality Assessment: Relaxing the Perfect Reference Quality Assumption (CVPR 2025 · similarity 0.92)
- Adaptive Image Quality Assessment via Teaching Large Multimodal Model to Compare (NeurIPS 2024 · similarity 0.90)
- Distilling Spatially-Heterogeneous Distortion Perception for Blind Image Quality Assessment (CVPR 2025 · similarity 0.88)
- Revisiting MLLM Based Image Quality Assessment: Errors and Remedy (AAAI 2026 · similarity 0.88)
- Adaptive Feature Selection for No-Reference Image Quality Assessment by Mitigating Semantic Noise Sensitivity (ICML 2024 · similarity 0.87)
- Augmenting Perceptual Super-Resolution via Image Quality Predictors (CVPR 2025 · similarity 0.86)
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