The Rate-Distortion-Perception Tradeoff: The Role of Common Randomness
This work addresses distribution-preserving compression for applications like image and data processing, but it is incremental as it extends prior models by adding common randomness.
The paper tackles the rate-distortion-perception tradeoff in lossy compression by incorporating common randomness between encoder and decoder, providing a coding theorem that recovers existing results with infinite randomness and analyzing the quadratic Gaussian case.
A rate-distortion-perception (RDP) tradeoff has recently been proposed by Blau and Michaeli and also Matsumoto. Focusing on the case of perfect realism, which coincides with the problem of distribution-preserving lossy compression studied by Li et al., a coding theorem for the RDP tradeoff that allows for a specified amount of common randomness between the encoder and decoder is provided. The existing RDP tradeoff is recovered by allowing for the amount of common randomness to be infinite. The quadratic Gaussian case is examined in detail.