Colormap optimization with data equality

Colormap is a visualization tool to realize the mapping from data to colors. The data patterns can be revealed by color distribution, and the data details can be explored by the mapping. Most colormaps use a linear mapping between data and colors. When the data are unevenly distributed, most data samples are encoded in very few colors, so that the patterns hidden in these huge amount of data samples encoded by very small range of colors cannot be explored. Every data sample is equally important, it should map to the exclusive color in the colormap. Inspired by force-directed model-based node layout in network visualization, we propose a novel colormap optimization algorithm with data equality, called spring model-based colormap. It formulates the proposed proportionality rule and data ink rule by a dynamically balanced spring system. The proportionality rule is that the color perception difference is proportional to the values of data samples for better identification of data values. The data ink rule lets the spring system make colors associated with data samples as separate as possible in the color space for better data distribution reveal. To accelerate the colormap generation, a fast solution for the colormap optimization algorithm is proposed. The effectiveness of our method is evaluated by eye tracking experiments. The results show that the fixations on both our colormap and the encoded visualization are more dispersed, which indicates that our method is better at both data distribution reveal and identification of data values.