TY - GEN

T1 - Compress-and-estimate source coding for a vector Gaussian source

AU - Song, Ruiyang

AU - Rini, Stefano

AU - Kipnis, Alon

AU - Goldsmith, Andrea J.

N1 - Funding Information:
The work of A. Kipnis and A. Goldsmith is partially supported by NSF Center for Science of Information (CSoI) under grant CCF-0939370 and NSF-BSF under grant 1609695. The work of S. Rini is partially supported by the Ministry Of Science and Technology (MOST) under the grant 105-2221-E-009-029.
Publisher Copyright:
© 2017 IEEE.

PY - 2018/1/31

Y1 - 2018/1/31

N2 - We consider the remote vector source coding problem in which a vector Gaussian source is 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 a local distortion measure, independent from the joint distribution between the source and the observations. In reconstruction, the decoder, having full knowledge of the joint distribution of the source and observations, estimates the original source realization from the lossy-compressed noisy observations. For the CE scheme in the vector Gaussian case, we show that, if the code rate is less than a specific threshold, then the CE coding scheme attains the same performance as the optimal coding scheme. For code rates above this threshold, we introduce lower and upper bounds on the performance gap between the CE and the optimal scheme. The case of a two-dimensional Gaussian source observed through two noisy measurements is studied to illustrate the behavior of the performance gap.

AB - We consider the remote vector source coding problem in which a vector Gaussian source is 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 a local distortion measure, independent from the joint distribution between the source and the observations. In reconstruction, the decoder, having full knowledge of the joint distribution of the source and observations, estimates the original source realization from the lossy-compressed noisy observations. For the CE scheme in the vector Gaussian case, we show that, if the code rate is less than a specific threshold, then the CE coding scheme attains the same performance as the optimal coding scheme. For code rates above this threshold, we introduce lower and upper bounds on the performance gap between the CE and the optimal scheme. The case of a two-dimensional Gaussian source observed through two noisy measurements is studied to illustrate the behavior of the performance gap.

UR - http://www.scopus.com/inward/record.url?scp=85046348363&partnerID=8YFLogxK

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U2 - 10.1109/ITW.2017.8278009

DO - 10.1109/ITW.2017.8278009

M3 - Conference contribution

AN - SCOPUS:85046348363

T3 - IEEE International Symposium on Information Theory - Proceedings

SP - 539

EP - 543

BT - 2017 IEEE Information Theory Workshop, ITW 2017

PB - Institute of Electrical and Electronics Engineers Inc.

T2 - 2017 IEEE Information Theory Workshop, ITW 2017

Y2 - 6 November 2017 through 10 November 2017

ER -