Choosing the optimal number of clusters (codebook vectors) for vector quantization is a trade-off between compression and accuracy. A larger number of clusters will result in higher accuracy but lower compression, while a smaller number of clusters will lead to higher compression but lower accuracy. One common approach to determine the optimal number of clusters is the elbow method, which involves plotting the within-cluster variance (or another clustering evaluation metric) against the number of clusters and identifying the point where the curve starts to flatten, indicating diminishing returns in accuracy for additional clusters. Another approach is to use cross-validation or a hold-out validation set to evaluate the performance of different numbers of clusters and choose the one that provides the best balance between compression and accuracy.