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