Abstract
Quantization is of ubiquitous need in information-processing systems. We typically model information-bearing signals as being analog in nature, while most of our processing of the signals is digital. As examples, consider the following. Digital communication systems encode samples of an input signal into a bit stream of finite length: finite due to bandwidth constraints. Various signal-processing techniques have their mathematical operations implemented on digital computers, finite binary representations being used for the data values. Hence an efficient method of transforming continuously valued signals into discretely valued signals is both desirable and necessary to ensure good system performance. Often such signals exist on a continuum of time. Here, however, we will assume that a sampling of the signal has already occurred and will employ the information-theoretic model of a discrete-time, continuously valued vector random process. With this assumption, the problem of interest is to in some way decide how to “round” a vector observation to one of N values.
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© 1986 Springer-Verlag New York Inc.
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Swaszek, P.F. (1986). Vector Quantization. In: Blake, I.F., Poor, H.V. (eds) Communications and Networks. Springer, New York, NY. https://doi.org/10.1007/978-1-4612-4904-7_16
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DOI: https://doi.org/10.1007/978-1-4612-4904-7_16
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