🖼️ SVD Image Compression Project

Author: Kaitlyn Kirt
Course: CMOR 220
Term: Spring 2024
Project: Singular Value Decomposition (SVD) for Image Compression
Last Modified: April 9, 2024

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📋 Overview

This project demonstrates how Singular Value Decomposition (SVD) can be used to compress grayscale images by approximating them with lower-rank matrices. It visualizes compression effects using different approximation factors and identifies the best rank to preserve a specified level of visual clarity.

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📂 Files

• Project9.ipynb: Main Jupyter notebook that loads an image, converts it to grayscale, and applies SVD for compression.
• image-2.jpg: The input image used for testing compression (ensure it is placed in the same directory).
• README.md: Project overview and usage guide.

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🔍 Techniques Used

• Grayscale Conversion: Transforming RGB to grayscale for simplicity and reduced complexity.
• SVD Decomposition: Breaking the image matrix into U, Σ, and V matrices.
• Rank Approximation: Determining optimal matrix rank to retain most image information with fewer components.
• Compression Factor Visualization: Comparing image clarity across various compression levels.

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🛠️ Requirements

pip install numpy matplotlib