Perceptually guided contrast enhancement based on viewing distance

Abstract

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.

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

“…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.…”