By Topic

Convergence to the rate-distortion function for Gaussian sources

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
$33 $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

2 Author(s)

In this paper we derive an expression for the minimum-mean-square error achievable in encoding t samples of a stationary correlated Gaussian source. It is assumed that the source output is not known exactly but is corrupted by correlated Gaussian noise. The expression is obtained in terms of the covariance matrices of the source and noise sequences. It is shown that as t \rightarrow \infty , the result agrees with a known asymptotic result, which is expressed in terms of the power spectra of the source and noise. The rate of convergence to the asymptotic results as a function of coding delay is investigated for the case where the source is first-order Markov and the noise is uncorrelated. With D the asymptotic minimum-mean-square error and D_t the minimum-mean-square error achievable in transmitting t samples, we find \mid D_t - D \mid \leq O((t^{-1} \log t) ^ {1/2}) when we transmit the noisy source vectors over a noiseless channel and \mid D_t - D \mid \leq O((t^{-1} \log t)^ {1/3}) when the channel is noisy.

Published in:

IEEE Transactions on Information Theory  (Volume:17 ,  Issue: 1 )