2018
Ware, Colin; Turton, Terece; Bujack, Roxana; Samsel, Francesca; Shrivastava, Piyush; Rogers, David
Measuring and Modeling the Feature Detection Threshold Functions of Colormaps Journal Article
In: IEEE Transactions on Visualization and Computer Graphics, pp. 1-1, 2018, ISSN: 1077-2626, (LA-UR-18-21476).
Abstract | Links | BibTeX | Tags: color perception, Colormapping, feature extraction, frequency measurements, Image color analysis, sea measurements, sensitivity, spatial resolution, Task analysis
@article{8413174,
title = {Measuring and Modeling the Feature Detection Threshold Functions of Colormaps},
author = {Colin Ware and Terece Turton and Roxana Bujack and Francesca Samsel and Piyush Shrivastava and David Rogers},
url = {https://ieeexplore.ieee.org/document/8413174},
doi = {10.1109/TVCG.2018.2855742},
issn = {1077-2626},
year = {2018},
date = {2018-01-01},
journal = {IEEE Transactions on Visualization and Computer Graphics},
pages = {1-1},
abstract = {Pseudocoloring is one of the most common techniques used in scientific visualization. To apply pseudocoloring to a scalar field, the field value at each point is represented using one of a sequence of colors (called a colormap). One of the principles applied in generating colormaps is uniformity and previously the main method for determining uniformity has been the application of uniform color spaces. Here we present a new method for evaluating the feature discrimination threshold function across a colormap. The method is used in crowdsourced studies for the direct evaluation of nine colormaps for three feature sizes. The results are used to test the hypothesis that a uniform color space (CIELAB) gives too much weight to chromatic differences compared to luminance differences because of the way it was constructed. The hypothesis that feature discrimination can be predicted solely on the basis of luminance is also tested. The results reject both hypotheses and we demonstrate how reduced weights on the green-red and blue-yellow terms of the CIELAB color space creates a more accurate model when the task is the detection of smaller features in colormapped data. Both the method itself and modified CIELAB can be used in colormap design and evaluation.},
note = {LA-UR-18-21476},
keywords = {color perception, Colormapping, feature extraction, frequency measurements, Image color analysis, sea measurements, sensitivity, spatial resolution, Task analysis},
pubstate = {published},
tppubtype = {article}
}
Pseudocoloring is one of the most common techniques used in scientific visualization. To apply pseudocoloring to a scalar field, the field value at each point is represented using one of a sequence of colors (called a colormap). One of the principles applied in generating colormaps is uniformity and previously the main method for determining uniformity has been the application of uniform color spaces. Here we present a new method for evaluating the feature discrimination threshold function across a colormap. The method is used in crowdsourced studies for the direct evaluation of nine colormaps for three feature sizes. The results are used to test the hypothesis that a uniform color space (CIELAB) gives too much weight to chromatic differences compared to luminance differences because of the way it was constructed. The hypothesis that feature discrimination can be predicted solely on the basis of luminance is also tested. The results reject both hypotheses and we demonstrate how reduced weights on the green-red and blue-yellow terms of the CIELAB color space creates a more accurate model when the task is the detection of smaller features in colormapped data. Both the method itself and modified CIELAB can be used in colormap design and evaluation.
: . .
1.
Ware, Colin; Turton, Terece; Bujack, Roxana; Samsel, Francesca; Shrivastava, Piyush; Rogers, David
Measuring and Modeling the Feature Detection Threshold Functions of Colormaps Journal Article
In: IEEE Transactions on Visualization and Computer Graphics, pp. 1-1, 2018, ISSN: 1077-2626, (LA-UR-18-21476).
@article{8413174,
title = {Measuring and Modeling the Feature Detection Threshold Functions of Colormaps},
author = {Colin Ware and Terece Turton and Roxana Bujack and Francesca Samsel and Piyush Shrivastava and David Rogers},
url = {https://ieeexplore.ieee.org/document/8413174},
doi = {10.1109/TVCG.2018.2855742},
issn = {1077-2626},
year = {2018},
date = {2018-01-01},
journal = {IEEE Transactions on Visualization and Computer Graphics},
pages = {1-1},
abstract = {Pseudocoloring is one of the most common techniques used in scientific visualization. To apply pseudocoloring to a scalar field, the field value at each point is represented using one of a sequence of colors (called a colormap). One of the principles applied in generating colormaps is uniformity and previously the main method for determining uniformity has been the application of uniform color spaces. Here we present a new method for evaluating the feature discrimination threshold function across a colormap. The method is used in crowdsourced studies for the direct evaluation of nine colormaps for three feature sizes. The results are used to test the hypothesis that a uniform color space (CIELAB) gives too much weight to chromatic differences compared to luminance differences because of the way it was constructed. The hypothesis that feature discrimination can be predicted solely on the basis of luminance is also tested. The results reject both hypotheses and we demonstrate how reduced weights on the green-red and blue-yellow terms of the CIELAB color space creates a more accurate model when the task is the detection of smaller features in colormapped data. Both the method itself and modified CIELAB can be used in colormap design and evaluation.},
note = {LA-UR-18-21476},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
Pseudocoloring is one of the most common techniques used in scientific visualization. To apply pseudocoloring to a scalar field, the field value at each point is represented using one of a sequence of colors (called a colormap). One of the principles applied in generating colormaps is uniformity and previously the main method for determining uniformity has been the application of uniform color spaces. Here we present a new method for evaluating the feature discrimination threshold function across a colormap. The method is used in crowdsourced studies for the direct evaluation of nine colormaps for three feature sizes. The results are used to test the hypothesis that a uniform color space (CIELAB) gives too much weight to chromatic differences compared to luminance differences because of the way it was constructed. The hypothesis that feature discrimination can be predicted solely on the basis of luminance is also tested. The results reject both hypotheses and we demonstrate how reduced weights on the green-red and blue-yellow terms of the CIELAB color space creates a more accurate model when the task is the detection of smaller features in colormapped data. Both the method itself and modified CIELAB can be used in colormap design and evaluation.