skip to main content
research-article

Least squares quantization in PCM

Published: 01 September 2006 Publication History

Abstract

It has long been realized that in pulse-code modulation (PCM), with a given ensemble of signals to handle, the quantum values should be spaced more closely in the voltage regions where the signal amplitude is more likely to fall. It has been shown by Panter and Dite that, in the limit as the number of quanta becomes infinite, the asymptotic fractional density of quanta per unit voltage should vary as the one-third power of the probability density per unit voltage of signal amplitudes. In this paper the corresponding result for any finite number of quanta is derived; that is, necessary conditions are found that the quanta and associated quantization intervals of an optimum finite quantization scheme must satisfy. The optimization criterion used is that the average quantization noise power be a minimum. It is shown that the result obtained here goes over into the Panter and Dite result as the number of quanta become large. The optimum quautization schemes for 2^{b} quanta, b=1,2, cdots, 7, are given numerically for Gaussian and for Laplacian distribution of signal amplitudes.

Cited By

View all
  • (2025)K*-Means: An Efficient Clustering Algorithm with Adaptive Decision BoundariesInternational Journal of Parallel Programming10.1007/s10766-024-00779-853:1Online publication date: 1-Feb-2025
  • (2024)Enhancing patient recruitment response in clinical trialsProceedings of the Fortieth Conference on Uncertainty in Artificial Intelligence10.5555/3702676.3702737(1307-1322)Online publication date: 15-Jul-2024
  • (2024)QuIP#Proceedings of the 41st International Conference on Machine Learning10.5555/3692070.3694057(48630-48656)Online publication date: 21-Jul-2024
  • Show More Cited By

Recommendations

Comments

Information & Contributors

Information

Published In

cover image IEEE Transactions on Information Theory
IEEE Transactions on Information Theory  Volume 28, Issue 2
March 1982
289 pages

Publisher

IEEE Press

Publication History

Published: 01 September 2006

Qualifiers

  • Research-article

Contributors

Other Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

  • Downloads (Last 12 months)0
  • Downloads (Last 6 weeks)0
Reflects downloads up to 04 Jan 2025

Other Metrics

Citations

Cited By

View all
  • (2025)K*-Means: An Efficient Clustering Algorithm with Adaptive Decision BoundariesInternational Journal of Parallel Programming10.1007/s10766-024-00779-853:1Online publication date: 1-Feb-2025
  • (2024)Enhancing patient recruitment response in clinical trialsProceedings of the Fortieth Conference on Uncertainty in Artificial Intelligence10.5555/3702676.3702737(1307-1322)Online publication date: 15-Jul-2024
  • (2024)QuIP#Proceedings of the 41st International Conference on Machine Learning10.5555/3692070.3694057(48630-48656)Online publication date: 21-Jul-2024
  • (2024)Beyond the federationProceedings of the 41st International Conference on Machine Learning10.5555/3692070.3693443(33794-33810)Online publication date: 21-Jul-2024
  • (2024)Winner-takes-all learners are geometry-aware conditional density estimatorsProceedings of the 41st International Conference on Machine Learning10.5555/3692070.3693157(27254-27287)Online publication date: 21-Jul-2024
  • (2024)Tabular insights, visual impactsProceedings of the 41st International Conference on Machine Learning10.5555/3692070.3692953(21988-22009)Online publication date: 21-Jul-2024
  • (2024)E2GANProceedings of the 41st International Conference on Machine Learning10.5555/3692070.3692709(15929-15950)Online publication date: 21-Jul-2024
  • (2024)Bottleneck-minimal indexing for generative document retrievalProceedings of the 41st International Conference on Machine Learning10.5555/3692070.3692542(11888-11904)Online publication date: 21-Jul-2024
  • (2024)Efficient algorithms for sum-of-minimum optimizationProceedings of the 41st International Conference on Machine Learning10.5555/3692070.3692504(10927-10959)Online publication date: 21-Jul-2024
  • (2024)Ameliorate spurious correlations in dataset condensationProceedings of the 41st International Conference on Machine Learning10.5555/3692070.3692453(9696-9721)Online publication date: 21-Jul-2024
  • Show More Cited By

View Options

View options

Media

Figures

Other

Tables

Share

Share

Share this Publication link

Share on social media