In this article, we first review the vector quantiser and discuss its well-known advantages over the scalar quantiser, namely the space-filling advantage, the shape advantage, and the memory advantage. It is important to understand why vector quantisers always perform better than any other quantisation scheme for a given dimension, as this will provide the basis for our investigation on improvi...

In this paper, we examine a coding scheme for quantising feature vectors in a distributed speech recognition environment that is more robust to noise. It consists of a vector quantiser that operates on the logarithmic filterbank energies (LFBEs). Through the use of a perceptually-weighted Euclidean distance measure, which emphasises the LFBEs that represent the spectral peaks, the vector quanti...

Learning Vector Quantisation (LVQ) is a method of applying the Vector Quantisation (VQ) to generate references for Nearest Neighbour (NN) classification. Though successful in many occasions, LVQ suffers from several shortcomings, especially the reference vectors are prone to diverge. In this paper, we propose a Classified Vector Quantisation (CVQ) to establish VQ for classification. By CVQ, eac...

The paper presents a novel approach, based on the wavelet decomposition and the learning vector quantisation algorithm, to automatic classification of signals with linear frequency modulation, generated by radar emitters. The goal of radar transmitter classification is to determine the particular transmitter, from which a signal originated, using only the just received waveform. To categorise a...

{ Based on the assumption that a pattern is constructed out of features which are either fully present or absent, we propose a vector quantisation method which constructs patterns using binary combinations of features. For this model there exists an eecient EM-like learning algorithm which learns a set of representative codebook vectors. In terms of a generative model, the collection of allowed...

A hybrid 3D compression scheme which combines fractal coding with neighbourhood vector quantisation for video and volume data is reported. While fractal coding exploits the redundancy present in different scales, neighbourhood vector quantisation, as a generalisation of translational motion compensation, is a useful method for removing both intraand interframe coherences. The hybrid coder outpe...

The study prcsents a formulation of a Hadaniard framework for analysing the vector quantisation aver channels with memory. In seeking faster response, classes of index assignments are defined in terms of the Hadamard transform of channel transition probabilities, An index assignment algorithm is developed that achieves high robustness against channel errors, and its performance i n vector quant...

Vector quantization provides better ratedistortion performance over scalar quantization even for a random vector with independent dimensions. However, the design and implementation complexity of vector quantizers is much higher than that of scalar quantizers. To reduce the complexity while achieving performance close to optimal vector quantization and better than scalar quantization, we propose...