MATLAB Implementation of EZW Encoding with Haar Wavelet Transform

For beginners, EZW encoding and Haar wavelet transform provide a straightforward approach to understanding image processing fundamentals. EZW (Embedded Zerotree Wavelet) encoding is an efficient image compression algorithm that progressively encodes and decodes image data using wavelet coefficients. The implementation typically involves scanning wavelet coefficients in a specific order (usually from low to high frequencies) and applying zerotree quantization to efficiently represent significant coefficients while eliminating insignificant ones. Haar wavelet transform is a fundamental image processing technique that decomposes images into different frequency subbands (approximation and detail coefficients) through simple averaging and differencing operations. The MATLAB implementation usually uses functions like `wavedec2` for 2D wavelet decomposition and `waverec2` for reconstruction. The combined approach allows beginners to understand how wavelet-based compression works by first transforming the image using Haar wavelets, then applying EZW encoding to the resulting coefficients. Key implementation steps include: performing multi-level wavelet decomposition, thresholding coefficients, zerotree scanning (using parent-child relationships in wavelet trees), and progressive bitplane encoding. This method effectively extracts image features while enabling scalable compression, making it an excellent starting point for image processing education.