Multi-Resolution-Image-Compression-Using-Wavelets

📌 Project Overview

This project implements multi-level discrete wavelet transform (DWT) based image compression using Python and PyWavelets. The goal is to reduce image size while preserving key visual details using quantization and thresholding techniques.

The process involves:

• Wavelet decomposition: Breaking down an image into different frequency components using pywt.wavedec2
• Quantization: Reducing precision of wavelet coefficients to improve compression
• Thresholding: Eliminating small, insignificant coefficients to reduce storage needs
• Wavelet reconstruction: Rebuilding the image using pywt.waverec2 after compression
• Performance evaluation: Measuring the quality of reconstructed images using PSNR (Peak Signal-to-Noise Ratio) and SSIM (Structural Similarity Index).

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🚀 Features

✅ Multi-Level Wavelet Compression using pywt.wavedec2
✅ Quantization-Based Compression with adjustable quant_step
✅ Thresholding for Higher Compression
✅ Reconstruction using pywt.waverec2
✅ Performance Metrics (PSNR & SSIM)
✅ Data visualization for compressed & reconstructed images

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🔧 Technologies Used

• Python (Main programming language)
• PyWavelets (pywt) for Discrete Wavelet Transform (DWT & IDWT)
• NumPy (numpy) for array manipulations
• Matplotlib (matplotlib) for image visualization
• scikit-image (skimage.metrics) for SSIM calculations

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