An efficient and intuitive algorithm is presented for the design of vector quantizers based either on a known probabilistic model or on a long training sequence of data. The basic properties of the algorithm are discussed and demonstrated by examples. Quite general distortion measures and long blocklengths are allowed, as exemplified by the design of parameter vector quantizers of ten-dimensional vectors arising in Linear Predictive Coded (LPC) speech compression with a complicated distortion measure arising in LPC analysis that does not depend only on the error vector.
Memory and computation requirements imply fundamental limitations on the quality that can be achieved in vector quantization systems used for speech waveform coding and linear p...
Quantization, the process of approximating continuous-amplitude signals by digital (discrete-amplitude) signals, is an important aspect of data compression or coding, the field ...
Vector quantization is intrinsically superior to predictive coding, transform coding, and other suboptimal and {\em ad hoc} procedures since it achieves optimal rate distortion ...
In 1948 W. R. Bennett used a companding model for nonuniform quantization and proposed the formula <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3...
This paper describes an algorithm which allows to find out the M-output level quantizer characteristics minimizing the distortion with respect to both the Mean-Squared-Error (MS...
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