Hypergraph-based Source Codes for Function Computation Under Maximal Distortion

TL;DR

This paper introduces a hypergraph-based source coding scheme for function computation under maximal distortion, providing explicit procedures and achieving optimal rate-distortion bounds in various settings.

Contribution

It proposes a constructive hypergraph-based coding scheme that attains optimal or near-optimal rate-distortion functions for function computation problems.

Findings

Achieves the optimal rate-distortion function with side information.

Attains the Berger-Tung sum-rate inner bound in distributed coding.

Optimal for successive refinement and cascade multiple description problems.

Abstract

This work investigates functional source coding problems with maximal distortion, motivated by approximate function computation in many modern applications. The maximal distortion treats imprecise reconstruction of a function value as good as perfect computation if it deviates less than a tolerance level, while treating reconstruction that differs by more than that level as a failure. Using a geometric understanding of the maximal distortion, we propose a hypergraph-based source coding scheme for function computation that is constructive in the sense that it gives an explicit procedure for finding optimal or good auxiliary random variables. Moreover, we find that the hypergraph-based coding scheme achieves the optimal rate-distortion function in the setting of coding for computing with side information and achieves the Berger-Tung sum-rate inner bound in the setting of distributed source coding for computing. It also achieves the El Gamal-Cover inner bound for multiple description coding for computing and is optimal for successive refinement and cascade multiple description problems for computing. Lastly, the benefit of complexity reduction of finding a forward test channel is shown for a class of Markov sources.