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Quantization
 IEEE TRANS. INFORM. THEORY
, 1998
"... The history of the theory and practice of quantization dates to 1948, although similar ideas had appeared in the literature as long ago as 1898. The fundamental role of quantization in modulation and analogtodigital conversion was first recognized during the early development of pulsecode modula ..."
Abstract

Cited by 877 (12 self)
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The history of the theory and practice of quantization dates to 1948, although similar ideas had appeared in the literature as long ago as 1898. The fundamental role of quantization in modulation and analogtodigital conversion was first recognized during the early development of pulsecode modulation systems, especially in the 1948 paper of Oliver, Pierce, and Shannon. Also in 1948, Bennett published the first highresolution analysis of quantization and an exact analysis of quantization noise for Gaussian processes, and Shannon published the beginnings of rate distortion theory, which would provide a theory for quantization as analogtodigital conversion and as data compression. Beginning with these three papers of fifty years ago, we trace the history of quantization from its origins through this decade, and we survey the fundamentals of the theory and many of the popular and promising techniques for quantization.
Vector Quantization of Images Using the
 Proc. Int'l Conf. Image Processing
, 1995
"... This paper considers the vector quantization of signals using the L1 distortion measure. The key contribution is a result that allows one to characterize the centroid of a set of vectors for the L1 distortion measure. A method similar to the LBG algorithm for designing codebooks has been developed a ..."
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This paper considers the vector quantization of signals using the L1 distortion measure. The key contribution is a result that allows one to characterize the centroid of a set of vectors for the L1 distortion measure. A method similar to the LBG algorithm for designing codebooks has been developed and tested. The paper also discusses the design of vector quantizers employing the L1 distortion measure in an application in which the occurrences of quantization errors with larger magnitudes than a preselected threshold must be minimized. I. Introduction In many applications involving image compression with vector quantization, it is necessary to limit the maximum distortion introduced in the image during the coding process. An example involves image compression using perceptual threshold functions [1]. Perceptual threshold functions for images define the amount of distortion that can be introduced into an image without being detected by human observers. An image compression system that ...