Minimax Rate-Distortion - Mahmood, Adeel; Wagner, Aaron B. (Proceedings / IEEE International Symposium on Information Theory, 2022)
We show the existence of universal, variable-rate rate-distortion codes that meet the distortion constraint almost surely and approach the rate-distortion function uniformly with respect to an unknown source distribution and a distortion measure that is only revealed to the encoder and only at runtime. If the convergence only needs to be uniform with respect to the source distribution and not the distortion measure, then we provide an explicit bound on the minimax rate of convergence. Our construction combines conventional random coding with a zero-rate uncoded transmission scheme. The proof uses exact asymptotics from large deviations, acceptance-rejection sampling, the VC dimension of distortion measures, and the identification of an explicit, code-independent, finite-blocklength quantity, which converges to the rate-distortion function, that controls the performance of the best codes.