Vector Quantized Codebook Optimization using K-Means

In this paper we are proposing K-means algorithm for optimization of codebook. In general K-means is an optimization algorithm but this algorithm takes very long time to converge. We are using existing codebook so that the convergence time for K-means is reduced considerably. For demonstration we have used codebooks obtained from Linde Buzo and Gray (LBG) and Kekre s Fast Codebook Generation (KFCG) algorithms. It is observed that the optimal error obtained from both LBG and KFCG is almost same indicating that there is a unique minima. From the results it is obvious that KFCG codebook takes less number of iterations as compared to LBG codebook. This indicates that KFCG codebook is close to the optimum. This is also indicated by less Mean Squared Error (MSE) for it.