AI 中文总结
本文概述带真实性约束的有损压缩领域，揭示其与速率受限分布式协调的深层关联，综述真实性形式化进展并提出将相关范式转移至协调领域以催生新问题。
AI 中文摘要
经典率失真理论刻画了保真度约束下有损压缩的基本极限，但最小化失真常产生感知上不令人满意的重构结果——模糊图像、过度平滑的纹理及不自然的伪影。这催生了带真实性约束的压缩研究，要求重构在统计上与自然信号无差异，进而形成了三方率失真-感知（RDP）权衡。本文对这一新兴领域给出了易懂概述，并揭示了其与另一基本问题（速率受限通信下的分布式协调）的深层关联。在强分布匹配公式下，两个问题导出几乎相同的信息论刻画，均需公共随机数（CR）以实现最优性能，且均依赖类似分析工具（如软覆盖引理）。除统一视角外，本文综述了真实性形式化的最新进展，包括批量评论器与算法真实性，并提出将此类范式转移至协调领域，以说明该关联如何持续催生新问题。
英文摘要
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.
CommentsPublished in the IEEE BITS Information Theory Magazine
DOI：10.1109/MBITS.2026.3712694 10.1109/MBITS.2026.3712694 10.1109/MBITS.2026.3712694
