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End-to-end deep multi-score model for No-reference stereoscopic image quality assessment

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End-to-end Deep Multi-Score Model for No-reference Stereoscopic Image Quality Assessment

Overview

This repository contains the implementation of an end-to-end deep multi-score model for no-reference stereoscopic image quality assessment (SIQA). The model predicts multiple quality scores, including global quality, left view quality, right view quality, and stereo quality, without requiring a reference image.

Features

  • Multi-Score Prediction: Predicts global, left, right, and stereo quality scores.
  • No-reference Assessment: Evaluates image quality without the need for a reference image.
  • Browser-based Application: Run the model directly in your browser using the online web application.
  • Support for Various Image Formats: Compatible with BMP, PNG, JPG, GIF, and more.

Paper

This implementation is based on our paper published at the 2022 IEEE International Conference on Image Processing (ICIP 2022): Read the Paper on arXiv

Citation

@proceeding{messai2022-end-to-end,
    title={End-to-end deep multi-score model for No-reference stereoscopic image quality assessment},
    author={Messai, Oussama and Chetouani, Aladine},
    conference={ICIP2022},
    year={2022},
    publisher={IEEE}
}

Online Web Application

Experience the model in action through our online web application:

https://oussama-messai.com/iqa-stereo

Features:

  • Edge Mode Execution: The model runs directly in the browser, leveraging your machine's computational power.
  • Flexible Resolution Support: Works with any image resolution greater than 32x32 pixels.
  • Data Privacy: The application does not record or save any user data or images.
  • Feedback Welcome: While we do not store data, your feedback is greatly appreciated to improve the application.

Online Web Application

Requirements

Ensure you have the following dependencies installed:

You can install all the required packages using the provided requirements.txt file.

Installation

  1. Clone the Repository

    git clone https://github.com/o-messai/multi-score-SIQA.git
    cd multi-score-SIQA
  2. Set Up a Virtual Environment (Optional but Recommended)

    python3 -m venv venv
    source venv/bin/activate
  3. Install Dependencies

    pip install -r requirements.txt
  4. Prepare the Dataset

    • Place your datasets in the ./data/Waterloo_1/ and ./data/Waterloo_2/ directories.
    • Ensure the following files are present in each dataset directory:
      • im_names_S.txt
      • refnames_S.txt
      • ref_ids_S.txt
      • MOS_S.txt
      • MOS_L.txt
      • MOS_R.txt

Usage

Training the Model

To train the model, use the train.py script with the desired configuration:

python train.py --batch_size 128 --epochs 100 --lr 0.0001 --dataset Waterloo_1 --weight_decay 0.001

Parameters:

- `--batch_size`: Number of samples per batch (default: 128)
- `--epochs`: Number of training epochs (default: 100)
- `--lr`: Learning rate (default: 0.0001)
- `--dataset`: Dataset to use (`Waterloo_1` or `Waterloo_2`)
- `--weight_decay`: Weight decay (default: 0.001)

After training, the model will automatically evaluate on the test set and save the performance metrics in results/total_result.txt.

(Ensure that the evaluation flag and corresponding logic are implemented in train.py if not already present.)

Testing the Model

To test the model, use the test.py script with the desired configuration:

python test.py --dataset Waterloo_1 --model_path path/to/your/model.pth

Using the Online Web Application

Access the online web application to assess image quality without any setup:

https://oussama-messai.com/iqa-stereo

License

This project is licensed under the MIT License. See the LICENSE file for details.

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