Adaptive Colormap Optimization Based on Inserting Colors

Abstract

Traditional automatic color optimization methods face the challenge of expressing global features in dynamic data ranges. To solve this problem, an adaptive colormap optimization method based on inserting colors is proposed, which includes a process of estimating color inserting position and an inserting color optimization procedure. Firstly, color inserting positions are selected based on color discriminability and data histogram distribution. By keeping the color inserting positions, corresponding embedding colors are estimated through a novel energy optimization equation under the guidance of visual discriminability and the consistency to the original colormap. On the basis of the algorithm, an interactive visual data exploratory system is provided, which includes supporting global data perception and local ROI analysis. The effectiveness and applicability of the algorithm is evaluated via a user study and a case study, based on 6 colormaps with different color features and 8 datasets with different data distributions. The results demonstrate that proposed method can produce high quality visual data information compared with other algorithms, providing a condition for further data analysis.