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A coding theorem for the rate-distortion-perception function
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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
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RDD Function: A Tradeoff Between Rate and Distortion-in-Distortion
The RDD function extends classical rate-distortion by replacing point-wise distortion with a Gromov-Wasserstein style distance distortion, supported by a coding theorem and a heuristic alternating mirror descent algorithm.
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