@inproceedings{415675c077a74db184d2cbcb6955514f,
title = "Gaussian Approximation of Quantization Error for Estimation from Compressed Data",
abstract = "We consider the statistical connection between the quantized representation of a high dimensional signal X using a random spherical code and the observation of X under an additive white Gaussian noise (AWGN). We show that given X, the conditional Wasserstein distance between its bitrate-R quantized version and its observation under AWGN of signal-to-noise ratio 22R - 1 is sub-linear in the problem dimension. We then utilize this fact to connect the mean squared error (MSE) attained by an estimator based on an AWGN-corrupted version of X to the MSE attained by the same estimator when fed with its bitrate-R quantized version.",
author = "Alon Kipnis and Galen Reeves",
note = "Publisher Copyright: {\textcopyright} 2019 IEEE.; 2019 IEEE International Symposium on Information Theory, ISIT 2019 ; Conference date: 07-07-2019 Through 12-07-2019",
year = "2019",
month = jul,
day = "1",
doi = "10.1109/ISIT.2019.8849826",
language = "English",
series = "IEEE International Symposium on Information Theory - Proceedings",
publisher = "Institute of Electrical and Electronics Engineers",
pages = "2029--2033",
booktitle = "2019 IEEE International Symposium on Information Theory, ISIT 2019 - Proceedings",
address = "United States",
}