Spectral Visualization Sharpening

Zhou, Liang; Netzel, Rudolf; Weiskopf, Daniel; Johnson, Chris R.

doi:10.1145/3343036.3343133

Cited by 2 publications

(3 citation statements)

References 35 publications

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“…Several methods use image processing operators to enhance colormap design [29], [30], [31], [32], so that the improved visualization resolves more information. Zhou et al integrate view distance into the contrast enhancement process [30], and then propose a visualization sharpening scheme based on the power spectrum [31]. However, their methods directly enhance images and, thus, cannot preserve one-to-one mappings between the data and colormap.…”

Section: Colormap Design In Visualizationmentioning

confidence: 99%

Data-Driven Colormap Adjustment for Exploring Spatial Variations in Scalar Fields

Qiong

Zhao

Wang

et al. 2022

IEEE Trans. Visual. Comput. Graphics

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Colormapping is an effective and popular visualization technique for analyzing patterns in scalar fields. Scientists usually adjust a default colormap to show hidden patterns by shifting the colors in a trial-and-error process. To improve efficiency, efforts have been made to automate the colormap adjustment process based on data properties (e.g., statistical data value or histogram distribution). However, as the data properties have no direct correlation to the spatial variations, previous methods may be insufficient to reveal the dynamic range of spatial variations hidden in the data. To address the above issues, we conduct a pilot analysis with domain experts and summarize three requirements for the colormap adjustment process. Based on the requirements, we formulate colormap adjustment as an objective function, composed of a boundary term and a fidelity term, which is flexible enough to support interactive functionalities. We compare our approach with alternative methods under a quantitative measure and a qualitative user study (25 participants), based on a set of data with broad distribution diversity. We further evaluate our approach via three case studies with six domain experts. Our method is not necessarily more optimal than alternative methods of revealing patterns, but rather is an additional color adjustment option for exploring data with a dynamic range of spatial variations.

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Section: Colormap Design In Visualizationmentioning

confidence: 99%

Data-Driven Colormap Adjustment for Exploring Spatial Variations in Scalar Fields

Qiong

Zhao

Wang

et al. 2022

IEEE Trans. Visual. Comput. Graphics

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“…In the context of visualization, perceptual aspects have been studied. The spectral visualization sharpening [21] method is closely related to this work. That method [21] also enhances the contrast of visualization images with a viewing distance parameter but is based on a spectral model of vision.…”

Section: Related Workmentioning

confidence: 99%

“…The spectral visualization sharpening [21] method is closely related to this work. That method [21] also enhances the contrast of visualization images with a viewing distance parameter but is based on a spectral model of vision. This spectral model could simulate perceptual effects due to the change of viewing distance and contrast enhancement is achieved by inverting weights in this model.…”

Section: Related Workmentioning

confidence: 99%

Perceptually guided contrast enhancement based on viewing distance

Zhou

Weiskopf

Johnson

2019

Journal of Computer Languages

Self Cite

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We propose an image-space contrast enhancement method for color-encoded visualization. The contrast of an image is enhanced through a perceptually guided approach that interfaces with the user with a single and intuitive parameter of the virtual viewing distance. To this end, we analyze a multiscale contrast model of the input image and test the visibility of bandpass images of all scales at a virtual viewing distance. By adapting weights of bandpass images with a threshold model of spatial vision, this image-based method enhances contrast to compensate for contrast loss caused by viewing the image at a certain distance. Relevant features in the color image can be further emphasized by the user using overcompensation. The weights can be assigned with a simple band-based approach, or with an efficient pixel-based approach that reduces ringing artifacts. The method is efficient and can be integrated into any visualization tool as it is a generic image-based post-processing technique. Using highly diverse datasets, we show the usefulness of perception compensation across a wide range of typical visualizations.

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Spectral Visualization Sharpening

Cited by 2 publications

References 35 publications

Data-Driven Colormap Adjustment for Exploring Spatial Variations in Scalar Fields

Data-Driven Colormap Adjustment for Exploring Spatial Variations in Scalar Fields

Perceptually guided contrast enhancement based on viewing distance

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