Lossy Compression, Realism, and Coordination

Abstract

Classical rate-distortion theory characterizes the fundamental limits of lossy compression under fidelity constraints, but minimizing distortion often yields perceptually unsatisfying reconstructions - blurry images, over-smoothed textures, and unnatural artifacts. This has motivated a growing body of work on compression with realism constraints, which require reconstructions to be statistically indistinguishable from natural signals, giving rise to the three-way rate-distortion-perception (RDP) trade-off. This paper provides an accessible overview of this emerging area and reveals deep connections to another fundamental problem: distributed coordination under rate-limited communication. Under strong distribution matching formulations, both problems lead to nearly identical information-theoretic characterizations, both require common randomness (CR) for optimal performance, and both rely on similar analytical tools such as the soft covering lemma. Beyond a unifying perspective, we survey recent developments in formalizing realism, including batched critics and algorithmic realism, and propose to transfer such paradigms to coordination - illustrating how the connection continues to generate new problems.