The Likelihood Encoder for Lossy Source Compression

TL;DR

This paper explores the likelihood encoder's application to lossy source compression, providing new achievability proofs and insights into classical and multi-terminal source coding problems using the soft-covering lemma.

Contribution

It introduces the likelihood encoder as a novel approach for lossy source coding, offering alternative proofs and extending to multi-terminal scenarios.

Findings

Likelihood encoder simplifies proofs of classical source coding theorems.

Achieves the rate-distortion function with side information at the decoder.

Provides a framework for multi-terminal source coding bounds.

Abstract

In this work, a likelihood encoder is studied in the context of lossy source compression. The analysis of the likelihood encoder is based on a soft-covering lemma. It is demonstrated that the use of a likelihood encoder together with the soft-covering lemma gives alternative achievability proofs for classical source coding problems. The case of the rate-distortion function with side information at the decoder (i.e. the Wyner-Ziv problem) is carefully examined and an application of the likelihood encoder to the multi-terminal source coding inner bound (i.e. the Berger-Tung region) is outlined.