Vector Quantization Coding Based on Self-Organizing Neural Networks with MATLAB Implementation

This paper presents a MATLAB implementation of vector quantization coding based on self-organizing neural networks. Vector quantization is a data compression technique that maps high-dimensional data into a lower-dimensional space for more efficient storage and processing. Self-organizing neural networks are unsupervised learning models capable of autonomously learning the distribution patterns of input data through competitive learning mechanisms. The program implements a complete vector quantization pipeline including data preprocessing, neural network training using Kohonen's self-organizing map (SOM) algorithm, and codebook generation. Key MATLAB functions employed include `selforgmap` for network creation, `train` for unsupervised learning, and custom implementations for vector quantization encoding/decoding. The algorithm effectively compresses high-dimensional input data while preserving essential features through competitive learning and neighborhood preservation mechanisms. This implementation allows users to perform efficient data compression and feature extraction, providing a solid foundation for subsequent analysis and processing tasks. The code includes parameter configuration options for network topology, learning rate, and neighborhood size, enabling customization for different application scenarios. This work aims to assist researchers and engineers working with self-organizing neural networks in both academic studies and practical applications.