Vector Quantization with Hypercolumnar Clusters.

This paper proposes a new vector quantization method which can reduce search complexity and code book memory size by reducing the number of code vectors without increasing quantization distortion. This method uses hypercolumnar clusters; and an input vector is quantized to a cluster, the center axis of which is nearest to the input vector. Thus, one vector and one scaler must be coded and transmitted. The proposed method was applied to four geometrically different distributions and LPC cepstra of speech signals. As a result, the number of code vectors was decreased comapared with that in an ordinary vector quantizer, in all of the forementioned distributions. That the reduction of memory size and search complexity were evaluated. [ABSTRACT FROM AUTHOR]