By Topic

Bound for Minkowski metric or quadratic metric applied to VQ codeword search

Sign In

Cookies must be enabled to login.After enabling cookies , please use refresh or reload or ctrl+f5 on the browser for the login options.

Formats Non-Member Member
$31 $31
Learn how you can qualify for the best price for this item!
Become an IEEE Member or Subscribe to
IEEE Xplore for exclusive pricing!
close button

puzzle piece

IEEE membership options for an individual and IEEE Xplore subscriptions for an organization offer the most affordable access to essential journal articles, conference papers, standards, eBooks, and eLearning courses.

Learn more about:

IEEE membership

IEEE Xplore subscriptions

3 Author(s)
Pan, J.-S. ; Centre for Commun. Interface Res., Edinburgh Univ., UK ; McInnes, F.R. ; Jack, M.A.

A bound for a Minkowski metric based on Lp distortion measure is proposed and evaluated as a means to reduce the computation in vector quantisation. This bound provides a better criterion than the absolute error inequality (AEI) elimination rule on the Euclidean distortion measure. For the Minkowski metric of order n, this bound contributes the elimination criterion from the L1 metric to L n metric. This bound can also be an extended quadratic metric which can be a hidden Markov model (HMM) with a Gaussian mixture probability density function (PDF). In speech recognition, the HMM with the Gaussian mixture VQ codebook PDF has been shown to be a promising method

Published in:

Vision, Image and Signal Processing, IEE Proceedings -  (Volume:143 ,  Issue: 1 )