MATLAB Simulation of Vector Quantizer Using LBG Algorithm

This article provides a comprehensive guide to implementing a vector quantizer simulation using the LBG algorithm in MATLAB. We begin with a thorough explanation of the LBG algorithm, including its advantages in codebook design and limitations regarding computational complexity and local optimum convergence. The implementation section details MATLAB programming techniques for vector quantization, covering key functions like k-means clustering for codebook generation, distortion calculation using Euclidean distance metrics, and iterative codebook refinement through the LBG training process. Practical recommendations include optimal initialization methods for training vectors, convergence threshold settings, and handling high-dimensional data. Finally, we demonstrate complete MATLAB implementation with sample code showcasing algorithm initialization, iterative codebook optimization, and quantization error analysis. The article includes executable code examples with explanations of resulting codebook structures and rate-distortion performance metrics, making it valuable for researchers and engineers working on signal compression and pattern recognition applications.