Research map / Image generation / Autoregressive and latent generation
Deep Learning of Human Visual Sensitivity in Image Quality Assessment Framework
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| add the objective error map as a model input (ablation) | DeepQA · TID2013 | SRCC: 0.766 -> 0.939 | Helped |
| add the objective error map as a model input (ablation) | DeepQA · TID2013 | PLCC: 0.818 -> 0.947 | Helped |
| add the objective error map as a model input (ablation) | DeepQA · LIVE IQA | SRCC: 0.977 -> 0.981 | Helped |
| add the objective error map as a model input (ablation) | DeepQA · CSIQ | SRCC: 0.957 -> 0.961 | Helped |
| vary the TV regularization weight (ablation) | DeepQA sensitivity-map prediction · LIVE IQA | SRCC | No clear effect |
| use grayscale images and discard low-frequency components during normalization | DeepQA · TID2008 Block distortion | SRCC: 0.517 | Hurt |
| discard low-frequency components during normalization | DeepQA · TID2008 MS distortion | SRCC: 0.652 | Hurt |
Closest papers by meaning
- Toward Generalized Image Quality Assessment: Relaxing the Perfect Reference Quality Assumption (CVPR 2025 · similarity 0.87)
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- Image Quality Assessment: From Human to Machine Preference (CVPR 2025 · similarity 0.87)
- Image Quality Assessment: Investigating Causal Perceptual Effects with Abductive Counterfactual Inference (CVPR 2025 · similarity 0.87)
- VisualQuality-R1: Reasoning-Induced Image Quality Assessment via Reinforcement Learning to Rank (NeurIPS 2025 · similarity 0.86)
- Boosting Image Quality Assessment through Efficient Transformer Adaptation with Local Feature Enhancement (CVPR 2024 · similarity 0.86)
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