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Image Compression and Transformation

This project entails a Python-based 2D greyscale image compression code. We have implemented the methodologies proposed in two research papers, incorporating not only the prescribed approaches but also making significant modifications to the architecture. Our aim was to explore the feasibility of integrating these methods for a research-oriented study.

Link to the paper:

  1. Paper 1
  2. Paper 2

Note: Both studies are available in the repository's papers subdirectory.

Project Context

This project forms a crucial component of the university curriculum, serving as the final research project for the end term. It is part of the Master's program in Computer Science with a specialization in multimedia at the University of Alberta.

Installation

To setup the poject in your local machine follow these steps:

Navigate to the project directory:

cd [project directory]

To install the necessary dependencies for this project, follow these steps:

  1. Numpy: Numpy is a fundamental package for scientific computing with Python. If you haven't installed it yet, you can install it using pip:

    pip3 install numpy
    
  2. OpenCV: OpenCV is a library used for image processing. You can install it using pip:

    pip3 install opencv-python
    
  3. Imagecodecs: Imagecodecs is a package for reading and writing image files. You can install it using pip:

    pip3 install imagecodecs
    
  4. Matplotlib: Matplotlib is a plotting library. You can install it using pip:

    pip3 install matplotlib
    
  5. Other Dependencies: Ensure that you have other dependencies such as csv and collections installed. They are typically included in the Python standard library and do not require separate installation.

Contribution

If you'd like to contribute to the project, follow these steps:

  1. Fork the repository.
  2. Create a new branch (git checkout -b feature)
  3. Make your changes.
  4. Commit your changes (git commit -am 'Add new feature')
  5. Push to the branch (git push origin feature)
  6. Create a new Pull Request.

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  • Python 100.0%