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Second-Order and Moderate Deviations Asymptotics for Successive Refinement.

Authors :
Zhou, Lin
Tan, Vincent Y. F.
Motani, Mehul
Source :
IEEE Transactions on Information Theory. May2017, Vol. 63 Issue 5, p2896-2921. 26p.
Publication Year :
2017

Abstract

We derive the optimal second-order coding region and moderate deviations constant for successive refinement source coding with a joint excess-distortion probability constraint. We consider two scenarios: 1) a discrete memoryless source (DMS) and arbitrary distortion measures at the decoders and 2) a Gaussian memoryless source (GMS) and quadratic distortion measures at the decoders. For a DMS with arbitrary distortion measures, we prove an achievable second-order coding region, using type covering lemmas by Kanlis and Narayan and by No, Ingber, and Weissman. We prove the converse using the perturbation approach by Gu and Effros. When the DMS is successively refinable, the expressions for the second-order coding region and the moderate deviations constant are simplified and easily computable. For this case, we also obtain new insights on the second-order behavior compared with the scenario where separate excess-distortion proabilities are considered. For example, we describe a DMS, for which the optimal second-order region transitions from being characterizable by a bivariate Gaussian to a univariate Gaussian, as the distortion levels are varied. We then consider a GMS with quadratic distortion measures. To prove the direct part, we make use of the sphere covering theorem by Verger–Gaugry, together with appropriately-defined Gaussian type classes. To prove the converse, we generalize Kostina and Verdú’s one-shot converse bound for point-to-point lossy source coding. We remark that this proof is applicable to general successively refinable sources. In the proofs of the moderate deviations results for both scenarios, we follow a strategy similar to that for the second-order asymptotics and use the moderate deviations principle. [ABSTRACT FROM PUBLISHER]

Details

Language :
English
ISSN :
00189448
Volume :
63
Issue :
5
Database :
Academic Search Index
Journal :
IEEE Transactions on Information Theory
Publication Type :
Academic Journal
Accession number :
122662258
Full Text :
https://doi.org/10.1109/TIT.2017.2674675