Supplemental materials for the paper on Modeling Just Noticeable Differences in Charts accepted in IEEE VIS'21. paper link: https://deardeer.github.io/pub/VIS21_LSD.pdf. the bounds for conditions, meta data driven 7200 plots. the estimated jnds, sorted by chart type. Data dict:

Through an empirical study, we identify main effects on JND for distance in bar charts, intensity in pie charts, and both distance and intensity in bubble charts. By fitting a linear mixed effects model, we model JND and find that JND grows as the exponential function of variables.

One of the classic psychoacoustic experiments is the measurement of a just noticeable difference (jnd), which is also called a difference limen. In these tests a subject is asked to compare two sounds and to indicate which is higher in level, or in frequency.

In the branch of experimental psychology focused on sense, sensation, and perception, which is called psychophysics, a just-noticeable difference or JND is the amount something must be changed in order for a difference to be noticeable, detectable at least half the time. [1]

In this work, we model the perception of Just Noticeable Differences (JNDs), the minimum difference in visual attributes that allow faithfully comparing similar elements, in charts.

Feb 8, 2025 · Choose Colors Wisely: Ensure color gradients and palettes highlight differences above the JND threshold. Tools like ColorBrewer can assist in selecting effective color schemes. Refine Sizes and Shapes: In charts and graphs, adjust element sizes (e.g., bar widths, point sizes) to reflect meaningful differences. Step 3: Test and Refine

Oct 27, 2021 · Through an empirical study, we identify main effects on JND for distance in bar charts, intensity in pie charts, and both distance and intensity in bubble charts. By fitting a linear mixed effects model, we model JND and find that JND grows as …

Sep 29, 2021 · In this work, we model the perception of Just Noticeable Differences (JNDs), the minimum difference in visual attributes that allow faithfully comparing similar elements, in charts.

We explore the problem of predicting 窶徊ust noticeable differences窶・in a visual attribute. While some pairs of images have a clear ordering for an attribute (e.g., A is more sporty than B), for others the difference may be in- distinguishable to human observers.

Instantly share code, notes, and snippets. This interactive demo shows how changing the size and percent arguments affects just noticeable difference intervals for CIELAB L* (lightness), a* (redness-to-greenness), and b* (blueness-to-yellowness) color channels.