가중치 가변에 의한 코드북 성능 개선

Improving performance of the codebook by a variable weight

We provide an useful method to design codebooks with better performance than conventional methods. In the proposed method, new codevectors obtained by learning iterations are not the centroid vectors which is the representatives of partitions, but the vectors manipulated by the distance between new codevectors and old codevectors in the early stages of learning iteration. Experimental results show that the codevectors in the obtained by the proposed method converge to a better locally optimal codebook.