MATLAB Implementation of High-Efficiency SPIHT Algorithm for Still Image Compression

The SPIHT (Set Partitioning in Hierarchical Trees) algorithm is a wavelet-transform based compression method renowned for its high efficiency in still image compression. This MATLAB implementation provides a complete workflow without the arithmetic coding stage, consisting of the following core functions: - func_SPIHT_Main: Main controller function that coordinates the compression pipeline, handling image input, processing parameters, and output generation - func_SPIHT_Enc: Encoder function implementing the SPIHT algorithm's bit-plane processing and significance testing for efficient coefficient quantization - func_SPIHT_Dec: Decoder function that reconstructs the image from compressed data using inverse significance mapping - func_DWT: Discrete Wavelet Transform function performing multi-level decomposition using filter banks (typically 9/7 or 5/3 filters) - func_InvDWT: Inverse Discrete Wavelet Transform function for reconstructing the image from wavelet coefficients - func_ReadRaw: Utility function for reading raw grayscale image data from disk with configurable dimensions and bit-depth The implementation focuses on the core SPIHT methodology, excluding the arithmetic coding stage to demonstrate the fundamental algorithm mechanics. Each function maintains modular design for easy integration and performance analysis.