Wasserstein Distortion with Intrinsic $\sigma$-Maps
Abstract
Wasserstein distortion is a recently proposed family of distortion measures, controlled by a width parameter $\sigma$, that lifts fidelity and realism into a common framework. In previous implementations, calculating the Wasserstein distortion between two images relied on a companion saliency map or manual tuning to specify the width parameter $\sigma$ for each location in the image. We introduce a novel scheme for automatically generating an $\sigma$-map from the image itself.
Cite
Text
Qiu et al. "Wasserstein Distortion with Intrinsic $\sigma$-Maps." NeurIPS 2024 Workshops: Compression, 2024.Markdown
[Qiu et al. "Wasserstein Distortion with Intrinsic $\sigma$-Maps." NeurIPS 2024 Workshops: Compression, 2024.](https://mlanthology.org/neuripsw/2024/qiu2024neuripsw-wasserstein/)BibTeX
@inproceedings{qiu2024neuripsw-wasserstein,
title = {{Wasserstein Distortion with Intrinsic $\sigma$-Maps}},
author = {Qiu, Yang and Lin, Ziyuan and Wagner, Aaron B.},
booktitle = {NeurIPS 2024 Workshops: Compression},
year = {2024},
url = {https://mlanthology.org/neuripsw/2024/qiu2024neuripsw-wasserstein/}
}