Image Compression using K-Means Clustering

This project demonstrates an image compression technique using the K-Means clustering algorithm. The original image is compressed by reducing the number of unique colors, which can significantly decrease the image file size while maintaining visual quality.

Table of Contents

• Introduction
• Installation
• Usage
• Code Explanation
• Results
• Contributing
• License

Introduction

Image compression is a process of reducing the file size of an image while preserving its quality. This project utilizes the K-Means clustering algorithm to achieve compression by clustering similar colors and reducing the number of unique colors in the image.

Installation

1. Clone the repository:

   git clone https://github.com/CGbhakt420/Image-Compression.git
   cd Image-Compression

2. Install the required dependencies:

   pip install numpy matplotlib 

Usage

1. Place the image you want to compress in the project directory and update the image_path variable in the script.

2. Run the script:

   python compress_image.py

Code Explanation

Functions

• find_closest_centroids(X, centroids): Finds the closest centroids for each data point in X.
• compute_centroids(X, idx, K): Computes new centroids based on the current cluster assignments.
• kMeans_init_centroids(X, K): Initializes centroids by randomly selecting K data points from X.
• run_kMeans(X, initial_centroids, max_iters=10, plot_progress=False): Runs the K-Means algorithm for a given number of iterations.

Main Code

1. Load and normalize the image.
2. Reshape the image data for clustering.
3. Initialize and run the K-Means algorithm.
4. Reconstruct the compressed image from the cluster centroids.
5. Display and compare the original and compressed images.

Results

The compressed image uses fewer colors but retains a high level of visual fidelity compared to the original image. Here is an example of the results:

Contributing

Contributions are welcome! Please fork the repository and create a pull request with your changes.

1. Fork the Project
2. Create your Feature Branch (git checkout -b feature/AmazingFeature)
3. Commit your Changes (git commit -m 'Add some AmazingFeature')
4. Push to the Branch (git push origin feature/AmazingFeature)
5. Open a Pull Request

License