Lossy Compression, Realism, and Coordination

2026-08-12

AI Summary

This work addresses the perceptual degradation—such as blurriness or unnatural artifacts—that commonly arises in conventional lossy compression under fidelity constraints, a challenge also prevalent in distributed coordination under limited communication. For the first time, it establishes an information-theoretic correspondence between realism constraints in compression and distributed coordination problems, highlighting the pivotal roles of common randomness and distribution matching. By introducing novel paradigms including batch discriminators and algorithmic realism, and integrating strong distribution matching models, the soft covering lemma, and discriminator-based perceptual evaluation, the paper formulates a unified rate–distortion–perception tradeoff framework. This framework provides both theoretical foundations and new directions for enhancing perceptual reconstruction quality and enabling efficient distributed 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.

Problem

• lossy compression
• realism
• rate-distortion-perception trade-off
• distributed coordination
• distribution matching

Innovation

• rate-distortion-perception trade-off
• distributed coordination
• realism constraints
• common randomness
• soft covering lemma