2026 | IEEE VIS Workshop on VISxVISION (IEEE VISxVISION)

Evaluating Just Noticeable Differences in Layered Opacity Visualizations

Caterina Ponti, Shano Liang, Lane T. Harrison, Alark Joshi

Overview of our study. We measure just-noticeable differences (JNDs) in layered opacity charts. (1) On each trial, participants view two side-by-side charts and select the one that appears more opaque; (2) Stimuli span a controlled design of four base opacity levels (0.2–0.8), six opacity differences, and the two color schemes drawn from Meng et al.: a recommended (“good”) scheme and a non-recommended (“bad”) scheme, for 96 trials per participant; (3) We fit a Bayesian logistic mixed-effects model to the responses and read the JND off each psychometric function as the opacity difference giving 75% correct performance; (4) Results suggest little difference between color schemes, but larger effects for individual differences and variance when comparisons approach extreme values (0.0 or 1.0).

Abstract

Opacity is a widely used channel in data visualization, but it remains less well understood compared to channels such as color, length, size, etc. Recent work from Meng et al. investigated the impact of opacity across competing color schemes, finding that certain color schemes were associated with better participant accuracy. We examine these effects further in a controlled two-alternative forced choice setup, to determine whether opacity differences are truly equal across possible opacity comparison ranges. In a within-subjects study with 96 trials, including two competing color schemes (best and worst from Meng et al.) and 48 opacity pairs, we find little differences between color schemes, but larger individual differences in accuracy. Further, results show stable performance in middle opacity ranges, with more errors occurring when comparing extreme values.

BibTeX

@article{ponti2026evaluatingjnd,
  title={Evaluating Just Noticeable Differences in Layered Opacity Visualizations},
  author={Ponti, Caterina and Liang, Shano and Harrison, Lane and Joshi, Alark},
  journal = {In Proceedings of the 2026 IEEE VIS Workshop on VISxVISION},
  volume = {},
  number = {},
  url={https://arxiv.org/abs/2610.04192},
  year={2026},
  organization={IEEE}
}