MATLAB Implementation of SPIHT Algorithm with Encoding and Decoding

This documentation elaborates on the SPIHT algorithm's encoding and decoding procedures as referenced in the text. The SPIHT (Set Partitioning in Hierarchical Trees) algorithm is an advanced image compression technique that employs wavelet transformation followed by bit-plane encoding to achieve efficient compression. During the encoding phase, the MATLAB implementation first performs wavelet decomposition to divide the image into multiple subbands (LL, LH, HL, HH). The algorithm then prioritizes wavelet coefficients based on their significance using a spatial orientation tree structure. Key functions include initializing the significance map and implementing three distinct lists: List of Insignificant Pixels (LIP), List of Insignificant Sets (LIS), and List of Significant Pixels (LSP). The coding process involves multiple passes through bit planes from most significant to least significant bits, where coefficients are tested against decreasing thresholds. The decoding process reverses this operation by reconstructing the wavelet coefficients from the transmitted bit-stream. The MATLAB code systematically processes the encoded data to rebuild the significance map and progressively refine coefficient values. Final image reconstruction is achieved through inverse wavelet transformation. The SPIHT algorithm demonstrates particular effectiveness in medical imaging and remote sensing applications, maintaining high image quality while achieving compression ratios typically ranging from 8:1 to 20:1. The implementation includes optimization features such as dynamic threshold adjustment and efficient memory management for handling large-scale image data.