Two-Tone Pseudo Coloring: Compact Visualization for One-Dimensional Data

Abstract

A new pseudo coloring technique for large scale one-dimensional datasets is proposed. For visualization of a large scale dataset, user interaction is indispensable for selecting focus areas in the dataset. However, excessive switching of the visualized image makes it difficult for the user to recognize overview/ detail and detail/ detail relationships. The goal of this research is to develop techniques for visualizing details as precisely as possible in overview display. In this paper, visualization of a one-dimensional but very large dataset is considered. The proposed method is based on pseudo coloring, however, each scalar value corresponds to two discrete colors. By painting with two colors at each value, users can read out the value precisely. This method has many advantages: it requires little image space for visualization; both the overview and details of the dataset are visible in one image without distortion; and implementation is very simple. Several application examples, such as meteorological observation data and train convenience evaluation data, show the effectiveness of the method.