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Image Compression using DCT - Project README

Project Overview

This project demonstrates image compression using the Discrete Cosine Transform (DCT). It provides a graphical user interface (GUI) to load, compress, and visualize BMP images. Additionally, it compares the performance of manual and library-based DCT implementations across various matrix sizes.

Environment Setup

To ensure that you have all the necessary dependencies, a virtual environment should be created and activated before running the project. Follow these steps to set up your environment:

  1. Create a virtual environment:

    python -m venv env

  2. Activate the virtual environment:

    • On Windows:

      .\env\Scripts\activate

    • On macOS and Linux:

      source env/bin/activate

  3. Install the required packages:

    pip install -r requirements.txt

Requirements

The following Python packages are required for this project:

  • tkinter
  • Pillow
  • numpy
  • matplotlib
  • scipy

Ensure that these packages are installed in your environment. A requirements.txt file is provided for convenience.

Project Structure

The project is divided into several modules to organize the code effectively:

  1. GUI and Main Application (main.py):

    • This module initializes the main window using Tkinter, loads images, processes compression, and manages user interactions.

  2. DCT Algorithms (dct_algorithms.py):

    • Contains both manual and library-based implementations of 2D DCT and IDCT.

  3. Image Processing (image_processing.py):

    • Handles image conversion to/from matrices, DCT application, and filtering frequencies.

  4. Performance Comparison (compare_performance.py):

    • Measures and compares the performance of manual and library-based DCT algorithms.

Usage

Running the Application

  1. Start the application:

    bash

    Copia codice

    python dct_image_processor.py

  2. Load an Image:

    • Click on "Load .bmp image" to select a BMP file.
    • Adjust the parameters F (block size) and d (frequency cut-off) if needed.

  3. Compress the Image:

    • The application will display the original and compressed images side by side.
    • Compression details, including original size, compressed size, and compression ratio, will be shown.

  4. Compare DCT2 Algorithms:

    • Navigate to the "Comp" tab.
    • Click on "Compare DCT2 Algorithms" to start the performance comparison.
    • A progress bar will indicate the status, and a plot will display the results once completed.

Parameters

  • F (Block Size): Determines the size of the blocks for DCT transformation. Default is 10.
  • d (Frequency Cut-off): Specifies the threshold for filtering frequencies during compression. Default is 7.

Normalization

  • Check the "Normalize DCT Coefficients" option to enable normalization of DCT coefficients.

Additional Information

  • Progress and Visualization:

    • The progress of DCT algorithm comparison is shown using a progress bar.
    • The comparison results are visualized using matplotlib.

  • Error Handling:

    • Error messages will be displayed in case of invalid inputs or issues with file loading.

License

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