Fast vector quantization encoding based on K-d tree backtracking search algorithm

this paper we consider the K-d tree-based backtracking search algorithm and study its performance in the context of vector quantization encoding of speech waveform. We discuss the basic algorithm in detail and high-light the features of optimization as observed from theoretical analysis and from the empirical performance of the backtracking search. It is seen that, despite the 2(K) dependence of the theoretical average complexity bound for search in K-dimensional vector space, the actual average complexity of the backtracking search in vector quantization encoding of speech waveform is excellent. However, we show that the backtracking search has a very high worst-case computational overhead and that this may be unacceptable in many practical applications such as realtime vector quantization encoding, where the worst-case complexity is of considerable importance in addition to average computational complexity. In vector quantization applications where it is of interest to use large values of K for r less than or equal to 1 bit/sample, the codebook size is N less than or equal to 2(K) and the backtracking search with a performance bound of the order of 2(K) offers a poor worst-case performance. We also consider the use of principal component rotation in the context of vector quantization of speech waveform where there is a high degree of correlation across the components of a vector and show that this can improve the efficiency of optimization and reduce both the main search complexity and the overhead complexity of the backtracking search. (C) 1997 Academic Press.