@inproceedings{
freirich2023the,
title={The Distortion-Perception Tradeoff in Finite Channels with Arbitrary Distortion Measures},
author={Dror Freirich and Nir Weinberger and Ron Meir},
booktitle={NeurIPS 2023 workshop: Information-Theoretic Principles in Cognitive Systems},
year={2023},
url={https://openreview.net/forum?id=euzT4OcP2m}
}
Whenever inspected by humans, reconstructed signals should not be distinguished from real ones. Typically, such a high perceptual quality comes at the price of high reconstruction error.
We study this distortion-perception (DP) tradeoff over finite-alphabet channels, for the Wasserstein-$1$ distance as the perception index, and an arbitrary distortion matrix. We show that computing the DP function and the optimal
reconstructions is equivalent to solving a set of linear programming problems. We prove that the DP curve is a piecewise linear function of the perception index, and derive a closed-form expression for the case of binary sources.
The Distortion-Perception Tradeoff in Finite Channels with Arbitrary Distortion Measures
Dror Freirich Nir Weinberger Ron Meir