A sequential initialization technique for vector quantizer design

Vector quantization has been used in compressing both speech and image data. In theory, better performance can always be achieved by coding vectors instead of scalars. However, actual results depend upon the proper design of the quantizer. Vector quantizer design typically employs an algorithm such as the K-means algorithm or the Linde Buzo Gray algorithm in which the initialization affects the design cost (convergence rate) and the achievable performance (quantization error). After reviewing several current initialization techniques, a sequential initialization method called Error Function Initialization is presented. In this method, the seeds are chosen one at a time by attempting to maximize the step-wise reduction in the quantization error. Experimental results show that this technique yields faster convergence and smaller quantization errors. For real time applications, the technique could be used to design sub-optimal vector quantizers.