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Journal ArticleDOI

A fast encoding method for lattice codes and quantizers

John H. Conway, +1 more
- 01 Nov 1983 - 
- Vol. 29, Iss: 6, pp 820-824
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TLDR
A solution to the inverse problem for the same lattices, namely, given an integer k, to find the kth code vector, and to the closely related problem of finding the index k of a given code vector.
Abstract
In an earlier paper the authors described a very fast method which, for the root lattices A_{n}, D_{n}, E_{n} , their duals and certain other lattices, finds the closest lattice point to an arbitrary point of the underlying space. If the lattices are used as codes for a Gaussian channel, the algorithm provides a fast decoding procedure, or if they are used as vector quantizers the algorithm performs the analog-to-digital conversion efficiently. The present paper offers a solution to the inverse problem for the same lattices (the encoding problem for channel codes or the digital-to-analog part of quantizing), namely, given an integer k , to find the kth code vector, and to the closely related problem of finding the index k of a given code vector.

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Citations
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Introduction to data compression

TL;DR: The author explains the development of the Huffman Coding Algorithm and some of the techniques used in its implementation, as well as some of its applications, including Image Compression, which is based on the JBIG standard.
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Closest point search in lattices

TL;DR: An efficient closest point search algorithm, based on the Schnorr-Euchner (1995) variation of the Pohst (1981) method, is implemented and is shown to be substantially faster than other known methods.
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Quantization

TL;DR: The key to a successful quantization is the selection of an error criterion – such as entropy and signal-to-noise ratio – and the development of optimal quantizers for this criterion.
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Efficient Modulation for Band-Limited Channels

TL;DR: This paper attempts to present a comprehensive tutorial survey of the development of efficient modulation techniques for bandlimited channels, such as telephone channels, with principal emphasis on coded modulation techniques, in which there is an explosion of current interest.
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Entropy-constrained vector quantization

TL;DR: An iterative descent algorithm based on a Lagrangian formulation for designing vector quantizers having minimum distortion subject to an entropy constraint is discussed and it is shown that for clustering problems involving classes with widely different priors, the ECVQ outperforms the k-means algorithm in both likelihood and probability of error.
References
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Linear Algebra

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Linear Algebra

Journal ArticleDOI

Fast quantizing and decoding and algorithms for lattice quantizers and codes

TL;DR: A very fast algorithm is given for finding the closest lattice point to an arbitrary point if these lattices are used for vector quantizing of uniformly distributed data.
Journal ArticleDOI

Optimization of Two-Dimensional Signal Constellations in the Presence of Gaussian Noise

TL;DR: In this paper an asymptotic (large signal-to-noise ratio) expression is derived for the error rate and it is rigorously proved in the Appendix that the optimum constellations tend toward an equilateral structure, and become uniformly distributed in a circle.
Journal ArticleDOI

Voronoi regions of lattices, second moments of polytopes, and quantization

TL;DR: The answers to the squared distance questions and a description of the Voronoi (or nearest neighbor) regions of these lattices have applications to quantization and to the design of signals for the Gaussian channel.
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