Delta-MSE dissimilarity in GLA based vector quantization

The generalized Lloyd algorithm is one of popular partition-based algorithms to construct the codebook in vector quantization. We propose the delta-MSE dissimilarity measurement between training vectors and code vectors, based on the MSE distortion function. The delta-MSE function is heuristically derived by calculating the difference of MSE distortion before and after moving a training vector from one cluster to another. We show that the delta-MSE dissimilarity applies also to minimizing the F-ratio validity index of the vector quantizer. We incorporate the underlying dissimilarity into the generalized Lloyd algorithm in vector quantization with the initial codebook derived from the PCA-based k-d tree algorithm. Experimental results show that the proposed dissimilarity generally achieves better performance than the L2 distance in constructing the codebook of vector quantization.