Discrete Lossy Gray-Wyner Revisited: Second-Order Asymptotics, Large and Moderate Deviations

TL;DR

This paper advances the understanding of the discrete lossy Gray-Wyner problem by deriving second-order asymptotics, error exponents, and moderate deviations constants, employing novel analytical techniques and generalizations of existing information-theoretic tools.

Contribution

It introduces new methods for second-order analysis, error exponents, and moderate deviations in the Gray-Wyner problem, extending previous work with innovative lemmas and continuity arguments.

Findings

Derived the optimal second-order coding rate region.

Established the error exponent (reliability function).

Calculated the moderate deviations constant.

Abstract

In this paper, we revisit the discrete lossy Gray-Wyner problem. In particular, we derive its optimal second-order coding rate region, its error exponent (reliability function) and its moderate deviations constant under mild conditions on the source. To obtain the second-order asymptotics, we extend some ideas from Watanabe's work (2015). In particular, we leverage the properties of an appropriate generalization of the conditional distortion-tilted information density, which was first introduced by Kostina and Verd\'u (2012). The converse part uses a perturbation argument by Gu and Effros (2009) in their strong converse proof of the discrete Gray-Wyner problem. The achievability part uses two novel elements: (i) a generalization of various type covering lemmas; and (ii) the uniform continuity of the conditional rate-distortion function in both the source (joint) distribution and the distortion level. To obtain the error exponent, for the achievability part, we use the same generalized type covering lemma and for the converse, we use the strong converse together with a change-of-measure technique. Finally, to obtain the moderate deviations constant, we apply the moderate deviations theorem to probabilities defined in terms of information spectrum quantities.