Image Compression using K-Means Clustering

This project implements an image compression technique using the K-means clustering algorithm. It reduces the number of distinct colors in an image, thereby compressing the image size while maintaining visual fidelity.

Table of Contents

• Introduction
• Features
• Technologies Used
• Installation
• Usage
• Example
• Acknowledge
• Contributing
• License

Introduction

Image compression is a common technique used to reduce the file size of images while preserving quality. In this project, we utilize the K-means clustering algorithm to group pixels in the image based on their colors, allowing us to reduce the total number of colors in the image and thereby compressing it.

Features

• Reduces image size by clustering similar colors.
• Maintains quality with adjustable number of clusters.
• Supports multiple image formats (JPEG, PNG, etc.).
• Easy-to-use command line interface or GUI (if applicable).

Technologies Used

• Python 3.x
• NumPy
• Matplotlib (for visualization, if applicable)
• scikit-learn (for K-means algorithm)

Installation

1. Clone the repository:

   git clone https://github.com/yourusername/image-compression-kmeans.git
   cd image-compression-kmeans

2. Install the required packages:

   pip install -r requirements.txt

Usage

To use the image compression tool, run the script from the command line with the following syntax:

pip install -r requirements.txt

Example

input image:

output (using 32 clusters):

output (using 16 clusters):

output (using 8 clusters):

output (using 4 clusters):

acknowledgements

• K-Means Clustering for the algorithm documentation.
• Libraries like NumPy, OpenCV, and scikit-learn for their essential functionalities.

Contributing

If you would like to contribute to this project, please follow these steps:

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

License

This project is licensed under the MIT License