Indirect and Direct Gaussian Distributed Source Coding Problems
We consider the distributed source coding system of L correlated Gaussian sources Y l , l = 1, 2, ... , L, which are noisy observations of correlated Gaussian remote sources X k , k = 1,2, ..., K. We assume that Y L = t(Y 1 , Y 2 ,...,Y L ) is an observation of the source vector X K = t(X 1 , X 2 ,...
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| Published in: | IEEE transactions on information theory Vol. 60; no. 12; pp. 7506 - 7539 |
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| Format: | Journal Article |
| Language: | English |
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New York
IEEE
01.12.2014
The Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
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| ISSN: | 0018-9448, 1557-9654 |
| Online Access: | Get full text |
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| Abstract | We consider the distributed source coding system of L correlated Gaussian sources Y l , l = 1, 2, ... , L, which are noisy observations of correlated Gaussian remote sources X k , k = 1,2, ..., K. We assume that Y L = t(Y 1 , Y 2 ,...,Y L ) is an observation of the source vector X K = t(X 1 , X 2 , . . . , X K ), having the form Y L = AX K +N L , where A is a L×K matrix and N L = t(N 1 , N 2 , ... , N L ) is a vector of L-independent Gaussian random variables also independent of X K . In this system, L correlated Gaussian observations are separately compressed by L encoders and sent to the information processing center. We study the remote source coding problem, where the decoder at the center attempts to reconstruct the remote source X K . We consider three distortion criteria based on the covariance matrix of the estimation error on X K . For each of those three criteria, we derive explicit inner and outer bounds of the rate distortion region. Next, in the case of K = L and A = I L , we study the multiterminal source coding problem, where the decoder wishes to reconstruct the observation Y L = X L + N L . To investigate this problem, we shall establish a result that provides a strong connection between the remote source coding problem and multiterminal source coding problem. Using this result, we derive several new partial solutions to the multiterminal source coding problem. |
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| AbstractList | We consider the distributed source coding system of L correlated Gaussian sources Yl, l = 1, 2, ... , L, which are noisy observations of correlated Gaussian remote sources Xk, k = 1,2, ..., K. We assume that YL = t(Y1, Y2,...,YL) is an observation of the source vector XK = t(X1, X2, . . . , X...), having the form YL = AXK+NL, where A is a LxK matrix and NL = t(N1, N2, ... , NL) is a vector of L-independent Gaussian random variables also independent of X... In this system, L correlated Gaussian observations are separately compressed by L encoders and sent to the information processing center. We study the remote source coding problem, where the decoder at the center attempts to reconstruct the remote source XK. We consider three distortion criteria based on the covariance matrix of the estimation error on XK. For each of those three criteria, we derive explicit inner and outer bounds of the rate distortion region. Next, in the case of K = L and A = IL, we study the multiterminal source coding problem, where the decoder wishes to reconstruct the observation Y... = X... + N... To investigate this problem, we shall establish a result that provides a strong connection between the remote source coding problem and multiterminal source coding problem. Using this result, we derive several new partial solutions to the multiterminal source coding problem. (ProQuest: ... denotes formulae/symbols omitted.) We consider the distributed source coding system of L correlated Gaussian sources Y l , l = 1, 2, ... , L, which are noisy observations of correlated Gaussian remote sources X k , k = 1,2, ..., K. We assume that Y L = t(Y 1 , Y 2 ,...,Y L ) is an observation of the source vector X K = t(X 1 , X 2 , . . . , X K ), having the form Y L = AX K +N L , where A is a L×K matrix and N L = t(N 1 , N 2 , ... , N L ) is a vector of L-independent Gaussian random variables also independent of X K . In this system, L correlated Gaussian observations are separately compressed by L encoders and sent to the information processing center. We study the remote source coding problem, where the decoder at the center attempts to reconstruct the remote source X K . We consider three distortion criteria based on the covariance matrix of the estimation error on X K . For each of those three criteria, we derive explicit inner and outer bounds of the rate distortion region. Next, in the case of K = L and A = I L , we study the multiterminal source coding problem, where the decoder wishes to reconstruct the observation Y L = X L + N L . To investigate this problem, we shall establish a result that provides a strong connection between the remote source coding problem and multiterminal source coding problem. Using this result, we derive several new partial solutions to the multiterminal source coding problem. |
| Author | Oohama, Yasutada |
| Author_xml | – sequence: 1 givenname: Yasutada surname: Oohama fullname: Oohama, Yasutada email: oohama@uec.ac.jp organization: Dept. of Commun. Eng. & Inf., Univ. of Electro-Commun., Chofu, Japan |
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| Cites_doi | 10.1109/TIT.1982.1056586 10.1109/ISITA.2008.4895631 10.1016/S0019-9958(78)90034-7 10.1109/TIT.1973.1055037 10.1109/TIT.1976.1055508 10.1109/18.32119 10.1109/TIT.2008.920343 10.1109/18.641555 10.1109/ISIT.2012.6284256 10.1109/TIT.1975.1055356 10.1109/TIT.2005.850110 10.1109/TIT.2010.2050960 10.1109/TIT.2012.2201347 10.1109/ITW.2012.6404653 10.1109/TIT.1987.1057384 10.1109/TIT.2011.2173706 10.1109/TIT.2008.920249 10.1109/18.669162 10.1109/TIT.2012.2184667 10.1109/TIT.2013.2245397 10.1109/ISIT.2004.1365154 10.1109/18.623151 10.1109/TIT.1979.1056105 |
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| Keywords | Gaussian CEO problem rate distortion region remote sources multiterminal source coding Distributed source coding |
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| References | ref13 ref12 ref15 oohama (ref22) 2008 ref30 berger (ref5) 1978 ref11 ref10 tung (ref6) 1978 ref2 ref1 ref17 ref16 ref19 ref18 yamamoto (ref14) 1980; 63 ref24 ref23 ref26 pandya (ref20) 2004 oohama (ref21) 2005 ref28 ref27 ref29 ref8 ref7 ref9 ref4 ref3 oohama (ref25) 2011 |
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| SubjectTerms | Codes Correlation analysis Covariance matrices Decoding Distributed source coding Estimating techniques Estimation error Gaussian CEO problem Information processing multiterminal source coding Normal distribution Random variables rate distortion region Rate-distortion remote sources Source coding Vectors |
| Title | Indirect and Direct Gaussian Distributed Source Coding Problems |
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