想搞懂压缩里的感知极限吗？这篇教程把率失真感知理论的计算方法和多种散度度量讲透了，比泛泛的综述实在得多。
传统率失真理论使用均方误差等失真度量，但难以捕捉感知质量。率失真感知理论引入感知作为第三轴，通过源信号与重构信号之间的分布相似性量化。本教程综述了感知感知压缩的编码原则，以及不同随机性假设下的可达性结果。它提供了计算Blau和Michaeli定义的率失真感知函数的统一优化视角，涵盖离散和连续源下的f-散度、α-散度和Wasserstein度量。还介绍了交替最小化、牛顿法和凸优化等计算工具，以及高斯源和完美现实主义等可解析情形。
Rate-Distortion-Perception Theory: Redefining the Fundamental Limits of Information Representation
Classical rate-distortion (RD) theory has long established the fundamental limits of lossy compression by quantifying the minimum number of bits required to represent a source under a prescribed distortion constraint. However, widely used distortion measures such as mean-squared error often fail to capture perceptual quality or semantic validity, which are increasingly central in modern learning-driven applications. Rate-distortion-perception (RDP) theory extends the RD framework by introducing perception as a third fundamental axis, quantified via distributional similarity between the source and reconstructed signals, leading to the rate-distortion-perception function (RDPF). This tutorial provides a structured overview of the coding principles underlying perception-aware lossy compression and surveys recent achievability results under different randomness assumptions. It then presents a unifying optimization viewpoint for computing the RDPF as defined by Blau and Michaeli, for both discrete and continuous sources under broad families of perceptual constraints, including f-divergences, alpha-divergences, and Wasserstein-based metrics. Special attention is given to computational tools such as alternating minimization schemes, Newton-based methods, and convex optimization formulations, as well as to analytically tractable cases such as Gaussian sources and the perfect-realism regime. Unlike recent broad surveys that emphasize generative architectures and AI-empowered communication systems, this tutorial focuses on the coding-theoretic and computational machinery needed to characterize, compute, and interpret the RDP limits. Finally, the tutorial outlines promising research directions at the intersection of information theory, neural compression, robust source coding, and perception-aware networked control systems.