---
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