SPIHT Image Compression Using Wavelet and Bandelet Transforms
Implementation of SPIHT image compression algorithm based on wavelet and bandelet transforms with enhanced code-level explanations
MATLAB Implementation of SPIHT Algorithm (Excluding Arithmetic Coding Stage)
This MATLAB implementation focuses on the core Set Partitioning in Hierarchical Trees (SPIHT) algorithm without the arithmetic coding module. The implementation demonstrates wavelet-based image compression through three main components: wavelet decomposition using functions like wavedec2, significant coefficient identification through tree structure traversal, and bit-plane coding for progressive transmission. Performance evaluation compares this implementation against standard SPIHT using the lena512.raw test image, showing PSNR results at various bit rates (0.1-0.9 bpp).
MATLAB Implementation of High-Efficiency SPIHT Algorithm for Still Image Compression
MATLAB implementation of SPIHT (Set Partitioning in Hierarchical Trees) - a highly efficient wavelet-based compression algorithm for still images. This implementation excludes arithmetic coding and demonstrates the core SPIHT methodology through modular MATLAB functions including wavelet decomposition, encoding, and decoding components.
EZW and Wavelet-Based Embedded Image Coding Algorithms: SPIHT, SPECK, and CREW
This paper provides a comparative analysis and discussion of embedded image coding algorithms including EZW (Embedded Zerotree Wavelet), SPIHT (Set Partitioning in Hierarchical Trees), SPECK (Set Partitioning Embedded Block Coding), and CREW (Compression with Reversible Embedded Wavelets). The study evaluates these algorithms from fundamental principles to performance metrics, elaborating on research directions in embedded image coding.
Explanation of the SPIHT Algorithm Principle with Implementation
This code demonstrates the core principles of the SPIHT (Set Partitioning in Hierarchical Trees) algorithm for image compression. The implementation excludes Arithmetic Coding for simplicity and currently supports only 128x128 pixel images—generalization for arbitrary image sizes requires additional modifications.
MATLAB Implementation of SPIHT Algorithm with Encoding and Decoding
SPIHT MATLAB implementation featuring complete encoding and decoding processes for image compression, including wavelet transformation and bit-plane coding techniques
Wavelet Transform-Based SPIHT Algorithm for High-Efficiency Still Image Compression Coding
An efficient still image compression coding algorithm SPIHT based on wavelet transformation, implemented in MATLAB with detailed code explanations and demonstrations
MATLAB Implementation of SPIHT Algorithm
This MATLAB-coded SPIHT (Set Partitioning in Hierarchical Trees) algorithm, also known as hierarchical tree diversity compression, currently processes 8×8 matrices. While limited to small-scale operations due to time constraints, the core algorithm framework is fully implemented. With minor modifications, it can be extended to handle larger images. The implementation demonstrates key compression techniques including sorting passes and bit-plane coding.