A coding theorem for the rate-distortion-perception function
This provides a theoretical foundation for the RDPF, addressing a key unknown in lossy compression for applications requiring realistic reconstructions.
The paper tackled the problem of whether encoders and decoders can achieve the rate suggested by the rate-distortion-perception function (RDPF) for lossy compression, and proved that the RDPF can be achieved using stochastic, variable-length codes and lower-bounds the achievable rate.
The rate-distortion-perception function (RDPF; Blau and Michaeli, 2019) has emerged as a useful tool for thinking about realism and distortion of reconstructions in lossy compression. Unlike the rate-distortion function, however, it is unknown whether encoders and decoders exist that achieve the rate suggested by the RDPF. Building on results by Li and El Gamal (2018), we show that the RDPF can indeed be achieved using stochastic, variable-length codes. For this class of codes, we also prove that the RDPF lower-bounds the achievable rate