抄録
Vector quantization is the process of encoding vector data as an index to a dictionary or codebook of representative vectors. One of the most serious problems for vector quantization is the high computational complexity involved in searching for the closest codeword through the codebook. In this paper, we describe a new method allowing significant acceleration of codebook design and encoding processes for vector quantization. This method has feature of using a suitable hyperplane to partition codebook and image data. Experimental results are presented on image block data. These results show that our method performs better than the previously known methods.
