File size: 2,618 Bytes
2ed357d 4d706d8 2ed357d 4d706d8 2ed357d 76e1e27 2ed357d 4d706d8 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 |
---
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
|