K-means Image Compression

Introduction

This project demonstrates how K-means clustering can be used to compress images. It applies both non-optimized and optimized versions of the algorithm to iconic test images ("Lena" and "Peppers"). The results highlight how compression reduces file size while preserving visual quality.

Prerequisites

• Python 3.8 or higher

• Libraries required:

• NumPy

• Matplotlib

• PIL (Python Imaging Library)

How to Run

1. Clone the repository or download the project files.

2. Install the required dependencies:

• pip install numpy matplotlib pillow

3. Open and run the Jupyter Notebook or Python script to process the images.

---

Results

Lena Images

1. Original Image

2. Compressed Image (Non-Optimized)

3. Compressed Image (Optimized)

Peppers Images

1. Original Image

2. Compressed Image (Non-Optimized)

3. Compressed Image (Optimized)

---

Key Features

• Non-optimized K-means: Implements a basic clustering algorithm for image compression.

• Optimized K-means: Uses improved techniques for better speed and efficiency.

Code Workflow

1. Image Loading: Load images and convert them into arrays using PIL.

2. Clustering:

• Groups similar pixel intensities using K-means.

• Iteratively updates cluster centers to minimize variance.

3. Compression: Replaces pixel values with their nearest cluster center.

4. Optimization: Enhances the clustering process for better performance.

5. Visualization: Generates comparisons of the original, non-optimized compressed, and optimized compressed images.

References

• K-means Clustering Algorithm

• Python documentation for NumPy, Matplotlib, and PIL.