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--- |
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title: TRIQA Image Quality Assessment |
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emoji: 🖼️ |
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colorFrom: blue |
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colorTo: purple |
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sdk: gradio |
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sdk_version: 4.0.0 |
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app_file: app.py |
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pinned: false |
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license: mit |
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short_description: TRIQA-IQA |
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--- |
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# TRIQA: Image Quality Assessment |
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TRIQA combines content-aware and quality-aware features from ConvNeXt models to predict image quality scores on a 1-5 scale. |
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## Features |
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- **Unified Framework**: Single interface combining content-aware and quality-aware feature extraction |
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- **ConvNeXt Architecture**: Uses state-of-the-art ConvNeXt models for feature extraction |
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- **Multi-scale Processing**: Processes images at two scales (original and half-size) for robust feature extraction |
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- **Regression-based Prediction**: Uses trained regression models for quality score prediction |
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- **Easy-to-use Interface**: Simple web interface for quality assessment |
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## How It Works |
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1. **Preprocessing**: Resize image to two scales (original + half-size) |
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2. **Feature Extraction**: Extract content and quality features using ConvNeXt models |
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3. **Prediction**: Combine features and predict quality score using regression model |
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## Model Files |
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Download the required model files from Box and place them in the appropriate directories: |
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### Required Files: |
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- `feature_models/convnext_tiny_22k_224.pth` - Content-aware model (170MB) |
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- `feature_models/triqa_quality_aware.pth` - Quality-aware model (107MB) |
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- `Regression_Models/KonIQ_scaler.save` - Feature scaler |
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- `Regression_Models/KonIQ_TRIQA.save` - Regression model (111MB) |
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### Box Links: |
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- [Download Model Files](https://utexas.box.com/s/8aw6axc2lofouja65uc726lca8b1cduf) - Place in `feature_models/` and `Regression_Models/` directories |
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## Citation |
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If you use this code in your research, please cite our paper: |
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```bibtex |
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@INPROCEEDINGS{11084443, |
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author={Sureddi, Rajesh and Zadtootaghaj, Saman and Barman, Nabajeet and Bovik, Alan C.}, |
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booktitle={2025 IEEE International Conference on Image Processing (ICIP)}, |
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title={Triqa: Image Quality Assessment by Contrastive Pretraining on Ordered Distortion Triplets}, |
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year={2025}, |
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volume={}, |
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number={}, |
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pages={1744-1749}, |
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keywords={Image quality;Training;Deep learning;Contrastive learning;Predictive models;Feature extraction;Distortion;Data models;Synthetic data;Image Quality Assessment;Contrastive Learning}, |
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doi={10.1109/ICIP55913.2025.11084443}} |
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``` |
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### Paper Links: |
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- **arXiv**: [https://arxiv.org/pdf/2507.12687](https://arxiv.org/pdf/2507.12687) |
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- **IEEE Xplore**: [https://ieeexplore.ieee.org/abstract/document/11084443](https://ieeexplore.ieee.org/abstract/document/11084443) |
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## License |
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MIT License |
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