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

Results

Lena Images

  1. Original Image

Lena image

  1. Compressed Image (Non-Optimized)

Lena image

  1. Compressed Image (Optimized)

Lena image

Peppers Images

  1. Original Image

Peppers image

  1. Compressed Image (Non-Optimized)

Peppers image

  1. Compressed Image (Optimized)

Peppers image

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.

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

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

  3. 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.