Exponential Strong Converse for Content Identification with Lossy Recovery

TL;DR

This paper establishes an exponential strong converse theorem for high-dimensional content identification with lossy recovery, providing bounds on error and distortion exponents, and extends the results to related biometric and content identification problems.

Contribution

It introduces an exponential strong converse theorem for the content identification with lossy recovery problem, extending the information spectrum method to this setting.

Findings

Derived an upper bound on joint identification-error and excess-distortion exponents.

Established an exponential strong converse theorem for the problem.

Unified analysis applicable to biometric and content identification problems.

Abstract

We revisit the high-dimensional content identification with lossy recovery problem (Tuncel and G\"und\"uz, 2014) and establish an exponential strong converse theorem. As a corollary of the exponential strong converse theorem, we derive an upper bound on the joint identification-error and excess-distortion exponent for the problem. Our main results can be specialized to the biometrical identification problem~(Willems, 2003) and the content identification problem~(Tuncel, 2009) since these two problems are both special cases of the content identification with lossy recovery problem. We leverage the information spectrum method introduced by Oohama and adapt the strong converse techniques therein to be applicable to the problem at hand.