Wavelet SOFM-Based Image Compression Algorithm

This paper introduces a wavelet SOFM-based image compression algorithm implemented using MATLAB. Although the algorithm's source code is downloadable from MATLAB's official website, it holds substantial reference value for practical implementations. The algorithm combines wavelet transform for multi-resolution analysis with Self-Organizing Feature Maps (SOFM) neural networks for efficient feature extraction and compression. Key implementation aspects include wavelet decomposition using functions like wavedec2, SOFM network training through competitive learning algorithms, and quantization strategies for optimized compression ratios. Note that implementing this algorithm requires solid computer science knowledge and programming skills, particularly in understanding wavelet transform parameters, neural network topology design, and compression ratio optimization techniques. Therefore, careful study and comprehension of each algorithm component - from wavelet coefficient extraction to SOFM clustering and entropy coding - is essential for effective application and potential optimization of this compression methodology.