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Gaussian Rate-Distortion-Perception Coding and Entropy-Constrained Scalar Quantization

Information Theory 2024-09-05 v1 Machine Learning math.IT

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.

Keywords

Cite

@article{arxiv.2409.02388,
  title  = {Gaussian Rate-Distortion-Perception Coding and Entropy-Constrained Scalar Quantization},
  author = {Li Xie and Liangyan Li and Jun Chen and Lei Yu and Zhongshan Zhang},
  journal= {arXiv preprint arXiv:2409.02388},
  year   = {2024}
}
R2 v1 2026-06-28T18:33:28.084Z