Without common randomness, a hybrid coding scheme can beat both separation-based and uncoded architectures for channel-aware optimal transport on binary and Gaussian channels.
Gaussian Rate-Distortion-Perception Coding and Entropy-Constrained Scalar Quantization
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abstract
This paper investigates the best known bounds on the quadratic Gaussian distortion-rate-perception function with limited common randomness for the Kullback-Leibler divergence-based perception measure, as well as their counterparts for the squared Wasserstein-2 distance-based perception measure, recently established by Xie et al. These bounds are shown to be nondegenerate in the sense that they cannot be deduced from each other via a refined version of Talagrand's transportation inequality. On the other hand, an improved lower bound is established when the perception measure is given by the squared Wasserstein-2 distance. In addition, it is revealed by exploiting the connection between rate-distortion-perception coding and entropy-constrained scalar quantization that all the aforementioned bounds are generally not tight in the weak perception constraint regime.
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Channel-Aware Optimal Transport: A Theoretical Framework for Generative Communication
Without common randomness, a hybrid coding scheme can beat both separation-based and uncoded architectures for channel-aware optimal transport on binary and Gaussian channels.