1 research outputs found
Compress-and-Estimate Source Coding for a Vector Gaussian Source
We consider the remote vector source coding problem in which a vector
Gaussian source is to be estimated from noisy linear measurements. For this
problem, we derive the performance of the compress-and-estimate (CE) coding
scheme and compare it to the optimal performance. In the CE coding scheme, the
remote encoder compresses the noisy source observations so as to minimize the
local distortion measure, independent from the joint distribution between the
source and the observations. In reconstruction, the decoder estimates the
original source realization from the lossy-compressed noisy observations. For
the CE coding in the Gaussian vector case, we show that, if the code rate is
less than a threshold, then the CE coding scheme attains the same performance
as the optimal coding scheme. We also introduce lower and upper bounds for the
performance gap above this threshold. In addition, an example with two
observations and two sources is studied to illustrate the behavior of the
performance gap