Image Compression Using Wavelet Transform with MATLAB Implementation

This program implements wavelet transform-based image compression using MATLAB, with visual comparison between original and compressed images. The code employs a straightforward structure utilizing MATLAB's Wavelet Toolbox functions: 1. wavedec2() for multi-level 2D discrete wavelet decomposition 2. wthcoef2() for coefficient thresholding to achieve compression 3. waverec2() for image reconstruction from processed coefficients Wavelet transform serves as an effective image compression technique by converting images into wavelet domain to extract frequency information, enabling data reduction while preserving critical image features. The algorithm works through: - Decomposition: Breaking down images into approximation and detail coefficients - Thresholding: Removing insignificant coefficients below a defined threshold - Reconstruction: Rebuilding compressed images from retained coefficients This implementation demonstrates how compression ratios can be controlled by adjusting threshold values, balancing between file size reduction and image quality preservation. For beginners, this program provides hands-on experience with: - Understanding wavelet families (Haar, Daubechies, etc.) and their impact on compression - Implementing multi-resolution analysis for image processing - Evaluating compression performance using metrics like PSNR and SSIM Through studying this code, learners can deepen their understanding of wavelet principles while gaining practical skills in digital image processing, establishing a solid foundation for advanced research and development in image compression algorithms.