Rate-distortion-perception theory: Redefining the fundamental limits of information representation

Classical rate–distortion theory asks how many bits are needed to represent a source at a given level of reconstruction error. Yet a reconstruction with low numerical error can still look unnatural, while one with higher error may better preserve the source’s perceptually important features. Rate–distortion–perception (RDP) theory adds a third dimension to this fundamental tradeoff: the statistical similarity between the source and its reconstruction. In this talk, I will introduce the rate–distortion–perception function and explain the coding principles behind it, including how shared randomness affects achievable rates. I will then discuss methods for characterizing and computing RDP tradeoffs for discrete and continuous sources under perceptual constraints based on statistical divergences and Wasserstein distance. Gaussian sources and the perfect-realism setting will provide concrete examples of the theory. I will conclude with open questions connecting RDP theory to neural compression, robust source coding, and communication for networked control.