Vector quantization with hyper-columnar clusters

A new vector quantization method is proposed which can reduce search complexity and code book memory size by reducing the number of code vectors without increasing quantization distortion. This method uses hyper-columnar clusters, and an input vector is quantized to a cluster, center axis of which is nearest to the input vector. Thus, one vector and one scalar must be coded and transmitted. The proposed method was applied to four geometrically different distributions and LPC cepstrums of speech signals. As a result, the number of code vectors was decreased compared with that in an ordinary vector quantizer, in all of the above distributions. Then the reduction of memory size and search complexity were evaluated