Vector Quantization Encoding Using the LBG Algorithm

This project implements vector quantization encoding using the LBG algorithm through MATLAB programming. The algorithm serves as a fundamental technique for data compression and signal processing, functioning by grouping input data into discrete vectors and encoding these vectors using clustering methods. The LBG algorithm represents a classical vector quantization approach that compresses high-dimensional data into lower-dimensional vector representations while preserving essential information from the original dataset. Key implementation aspects include: - Initial codebook generation using splitting methodology - Iterative clustering through k-means optimization - Distortion measurement and convergence criteria - Vector assignment and codebook updating mechanisms The MATLAB implementation involves several critical functions: - Codebook initialization with binary splitting technique - Lloyd iteration for centroid calculation and vector reassignment - Mean squared error computation for convergence testing - Batch processing of vector data for efficient encoding Implementing vector quantization with the LBG algorithm significantly reduces data storage and transmission costs while enhancing processing efficiency. The code structure allows for customizable parameters including codebook size, distortion thresholds, and maximum iteration counts, making it adaptable for various signal processing applications such as image compression, speech coding, and pattern recognition systems.