An Entropy-Based Test and Development Framework for Uncertainty Modeling in Level-Set Visualizations

TL;DR

This paper introduces an entropy-based framework for testing and developing uncertainty models in level-set visualizations, focusing on ensemble data and various probability models to improve accuracy and efficiency.

Contribution

It proposes a comparative entropy-based method to evaluate and improve uncertainty modeling in level-set visualizations, highlighting the effectiveness of different probability models.

Findings

Matching models accurately reflect ensemble entropy.

Fewer bins in histogram models improve effectiveness.

More bins in quantile models enhance data accuracy.

Abstract

We present a simple comparative framework for testing and developing uncertainty modeling in uncertain marching cubes implementations. The selection of a model to represent the probability distribution of uncertain values directly influences the memory use, run time, and accuracy of an uncertainty visualization algorithm. We use an entropy calculation directly on ensemble data to establish an expected result and then compare the entropy from various probability models, including uniform, Gaussian, histogram, and quantile models. Our results verify that models matching the distribution of the ensemble indeed match the entropy. We further show that fewer bins in nonparametric histogram models are more effective whereas large numbers of bins in quantile models approach data accuracy.