Pattern Recognition Project: Image Compression with K-means Clustering

This project implements a GUI application for image compression using the K-means clustering algorithm.

Features

• User-friendly Interface: Select images and easily adjust compression settings.
• K Selection: Choose automatic K value selection or manually set the number of clusters (2-100).
• Visual Comparison: View the original and compressed images side-by-side.
• Detailed Statistics: Get insights into compression effectiveness with color reduction and file size changes.
• Performance Monitoring: Track compression time for efficiency evaluation.
• Informed K Selection (Optional): Visualize the WCSS curve to make better decisions about the K value.
• Theme Options: Switch between light and dark themes for user preference.

Installation

1. Python 3.x: Ensure you have Python 3.x installed on your system.
2. Required Libraries: Install necessary libraries using pip:
   • pip install PyQt5
   • pip install scikit-image (usually includes NumPy)
   • pip install qdarkstyle (optional, dark theme)
   • pip install matplotlib (optional, WCSS curve & enlarged image)
3. Download & Run: Download the application script and run it to start the compression tool.

Usage

1. Image Selection: Click "Select Image" and choose a PNG, JPG, JPEG, or BMP image file for compression.
2. K Value Selection: Decide between automatic K selection or manually enter a value between 2 and 100 in the K textbox.
3. Compression Initiation: Click "Compress Image" to start the compression process.
4. Results & Statistics: The compressed image and detailed statistics (color reduction, file size changes) will be displayed.
5. WCSS Curve (Optional): Click "WCSS curve" to visualize the Within-Cluster Sum of Squares curve for informed K selection (requires matplotlib).
6. Enlarged Image (Optional): Click "Enlarge image" to see the original image full-size in a separate window (requires matplotlib).
7. Theme Toggle: Switch between light and dark themes for a more comfortable user experience.
8. Exit Application: Click "Quit" to close the image compression tool.

Notes

• K-means clustering reduces the number of colors in an image, potentially affecting image quality.
• The optimal K value depends on the specific image and the desired balance between image quality and file size. Automatic selection might not always be ideal.
• The GUI is currently optimized for a screen resolution of 1920x1080. Functionality on other resolutions might be limited.

Disclaimer

This code is provided for educational purposes only and may contain bugs or limitations.

Future Enhancements

• Improve GUI responsiveness for various screen sizes.
• Integrate advanced image quality metrics for a more comprehensive evaluation of compression effectiveness.