MATLAB Implementation of SPIHT Algorithm

This document presents a MATLAB implementation of the SPIHT algorithm, also referred to as hierarchical tree diversity compression. The current version is optimized for 8×8 matrix processing, though the complete algorithmic foundation is established for future scalability. With targeted modifications to handle larger data blocks, this implementation can be adapted for full-size image compression. We believe this resource provides valuable insights into wavelet-based compression techniques. The SPIHT algorithm employs an effective compression strategy by decomposing images into hierarchical levels and applying compression at each layer. The core implementation utilizes two key techniques: sorting passes and bit-plane coding. Through progressive sorting, the algorithm identifies significant image coefficients using magnitude thresholds, while bit-plane encoding efficiently represents these coefficients through binary significance maps. This combination enables high compression ratios while maintaining image quality through embedded coding. Though currently limited to 8×8 matrices, the algorithm's modular structure allows for straightforward scaling. The implementation includes critical functions for wavelet decomposition, significance testing using threshold comparisons, and ordered bit-plane transmission. Future enhancements could incorporate multi-level wavelet transforms and dynamic threshold adjustment for handling larger images. We anticipate SPIHT's continued relevance in image compression applications, particularly in scenarios requiring progressive transmission and rate-distortion optimization.