By Topic

Efficient scalar quantization of exponential and Laplacian random variables

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 $13
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

1 Author(s)
Sullivan, G.J. ; PictureTel Corp., Danvers, MA, USA

This paper presents solutions to the entropy-constrained scalar quantizer (ECSQ) design problem for two sources commonly encountered in image and speech compression applications: sources having the exponential and Laplacian probability density functions. We use the memoryless property of the exponential distribution to develop a new noniterative algorithm for obtaining the optimal quantizer design. We show how to obtain the optimal ECSQ either with or without an additional constraint on the number of levels in the quantizer. In contrast to prior methods, which require a multidimensional iterative solution of a large number of nonlinear equations, the new method needs only a single sequence of solutions to one-dimensional nonlinear equations (in some Laplacian cases, one additional two-dimensional solution is needed). As a result, the new method is orders of magnitude faster than prior ones. We show that as the constraint on the number of levels in the quantizer is relaxed, the optimal ECSQ becomes a uniform threshold quantizer (UTQ) for exponential, but not for Laplacian sources. We then further examine the performance of the UTQ and optimal ECSQ, and also investigate some interesting alternatives to the UTQ, including a uniform-reconstruction quantizer (URQ) and a constant dead-zone ratio quantizer (CDZRQ)

Published in:

Information Theory, IEEE Transactions on  (Volume:42 ,  Issue: 5 )