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.