K-Means Vector Quantization Implementation Code

The following code implements vector quantization using the k-means algorithm. Vector quantization is a fundamental signal processing technique for data reduction by dividing signals into smaller vector segments and representing each segment with a single codeword. The k-means clustering algorithm serves as the core mechanism, partitioning input vectors into predefined clusters and computing centroid values as representative codewords. This implementation features key algorithmic components including centroid initialization, iterative cluster assignment using Euclidean distance calculations, and centroid recalibration. The code structure includes functions for data preprocessing, k-means convergence monitoring, and quantization error evaluation. Users can customize parameters such as cluster count (k-value), convergence thresholds, and distance metrics to adapt to specific signal processing requirements. The algorithm workflow follows these steps: random centroid initialization, vector-to-cluster assignment based on minimum distance criteria, centroid recomputation, and iteration until cluster stability. This basic framework supports extensions like codebook optimization and adaptive quantization schemes for enhanced performance in applications like image compression and speech coding.