Vector quantization is a method that compresses data by representing it with a smaller set of representative vectors, called codebook vectors. The process involves the following steps: 1. Determine the number of codebook vectors (clusters) needed for the desired level of compression. 2. Apply a clustering algorithm, such as k-Means, to partition the data into clusters based on similarity. 3. Calculate the centroids of the clusters, which will serve as the codebook vectors. 4. Encode each data point as the index of the closest codebook vector. 5. To reconstruct the original data, replace the index with the corresponding codebook vector. This method reduces storage and computational requirements while maintaining a reasonable level of accuracy.