In this paper, we propose a perceptually-guided visualization sharpening technique. We analyze the spectral behavior of an established comprehensive perceptual model to arrive at our approximated model based on an adapted weighting of the bandpass images from a Gaussian pyramid. The main benefit of this approximated model is its controllability and predictability for sharpening color-mapped visualizations. Our method can be integrated into any visualization tool as it adopts generic image-based post-processing, and it is intuitive and easy to use as viewing distance is the only parameter. Using highly diverse datasets, we show the usefulness of our method across a wide range of typical visualizations.
%0 Conference Paper
%1 conf/apgv/0001NWJ19
%A Zhou, Liang
%A Netzel, Rudolf
%A Weiskopf, Daniel
%A Johnson, Chris R.
%B Proceedings of the ACM Symposium on Applied Perception (SAP)
%D 2019
%E Neyret, Solène
%E Kokkinara, Elena
%E González-Franco, Mar
%E Hoyet, Ludovic
%E Cunningham, Douglas W.
%E Swidrak, Justyna
%I ACM
%K 2020 B01 from:leonkokkoliadis sfbtrr161 visus visus:netzelrf visus:weiskopf
%P 18:1-18:9
%R 10.1145/3343036.3343133
%T Spectral Visualization Sharpening.
%U https://doi.org/10.1145/3343036.3343133
%X In this paper, we propose a perceptually-guided visualization sharpening technique. We analyze the spectral behavior of an established comprehensive perceptual model to arrive at our approximated model based on an adapted weighting of the bandpass images from a Gaussian pyramid. The main benefit of this approximated model is its controllability and predictability for sharpening color-mapped visualizations. Our method can be integrated into any visualization tool as it adopts generic image-based post-processing, and it is intuitive and easy to use as viewing distance is the only parameter. Using highly diverse datasets, we show the usefulness of our method across a wide range of typical visualizations.
%@ 978-1-4503-6890-2
@inproceedings{conf/apgv/0001NWJ19,
abstract = {In this paper, we propose a perceptually-guided visualization sharpening technique. We analyze the spectral behavior of an established comprehensive perceptual model to arrive at our approximated model based on an adapted weighting of the bandpass images from a Gaussian pyramid. The main benefit of this approximated model is its controllability and predictability for sharpening color-mapped visualizations. Our method can be integrated into any visualization tool as it adopts generic image-based post-processing, and it is intuitive and easy to use as viewing distance is the only parameter. Using highly diverse datasets, we show the usefulness of our method across a wide range of typical visualizations.},
added-at = {2020-10-09T12:34:19.000+0200},
author = {Zhou, Liang and Netzel, Rudolf and Weiskopf, Daniel and Johnson, Chris R.},
biburl = {https://puma.ub.uni-stuttgart.de/bibtex/2e700867e6efea9fd5ba03159c1d2edda/mueller},
booktitle = {Proceedings of the ACM Symposium on Applied Perception (SAP)},
crossref = {conf/apgv/2019},
description = {Spectral Visualization Sharpening.},
doi = {10.1145/3343036.3343133},
editor = {Neyret, Solène and Kokkinara, Elena and González-Franco, Mar and Hoyet, Ludovic and Cunningham, Douglas W. and Swidrak, Justyna},
ee = {https://doi.org/10.1145/3343036.3343133},
interhash = {f50df07fa55824833772729dad70522c},
intrahash = {e700867e6efea9fd5ba03159c1d2edda},
isbn = {978-1-4503-6890-2},
keywords = {2020 B01 from:leonkokkoliadis sfbtrr161 visus visus:netzelrf visus:weiskopf},
pages = {18:1-18:9},
publisher = {ACM},
timestamp = {2020-10-09T10:34:19.000+0200},
title = {Spectral Visualization Sharpening.},
url = {https://doi.org/10.1145/3343036.3343133},
year = 2019
}