Symmetric Generalized Gaussian Multiterminal Source Coding

IEEE Transactions on Information Theory(2018)

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摘要
Consider a generalized multiterminal source coding system, where (ℓ : m) encoders, each observing a distinct size-m subset of ℓ(ℓ ≥ 2) zero-mean unit-variance symmetrically correlated Gaussian sources with correlation coefficient ρ, compress their observations in such a way that a joint decoder can reconstruct the sources within a prescribed mean squared error distortion based on the compressed data. The optimal rate-distortion performance of this system was previously known only for the two extreme cases m = ℓ (the centralized case) and m = 1 (the distributed case), and except when ρ = 0, the centralized system can achieve strictly lower compression rates than the distributed system under all non-trivial distortion constraints. Somewhat surprisingly, it is established in the present paper that the optimal rate-distortion performance of the afore-described generalized multiterminal source coding system with m ≥ 2 coincides with that of the centralized system for all distortions when ρ ≤ 0 and for distortions below an explicit positive threshold (depending on m) when ρ > 0.
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关键词
compressed data,optimal rate-distortion performance,centralized system,distributed system,nontrivial distortion constraints,joint decoder,mean squared error distortion,compression rates,correlation coefficient,size-m subset,symmetric generalized Gaussian multiterminal source coding system,encoders,zero-mean unit-variance symmetrically correlated Gaussian sources
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