Exploiting Colorimetry for Fidelity in Data Visualization
This work addresses the need for high-fidelity data visualization in materials-chemistry research, though it is incremental as it builds on existing colorimetry concepts.
The paper tackles the problem of accurately visualizing higher-dimensional data like vector and composition fields in materials-chemistry by extending perceptually uniform color mapping principles from scalar fields, resulting in immediate improvements in data readability and interpretation for microscopies and spectroscopies.
Advances in multimodal characterization methods fuel a generation of increasing immense hyper-dimensional datasets. Color mapping is employed for conveying higher dimensional data in two-dimensional (2D) representations for human consumption without relying on multiple projections. How one constructs these color maps, however, critically affects how accurately one perceives data. For simple scalar fields, perceptually uniform color maps and color selection have been shown to improve data readability and interpretation across research fields. Here we review core concepts underlying the design of perceptually uniform color map and extend the concepts from scalar fields to two-dimensional vector fields and three-component composition fields frequently found in materials-chemistry research to enable high-fidelity visualization. We develop the software tools PAPUC and CMPUC to enable researchers to utilize these colorimetry principles and employ perceptually uniform color spaces for rigorously meaningful color mapping of higher dimensional data representations. Last, we demonstrate how these approaches deliver immediate improvements in data readability and interpretation in microscopies and spectroscopies routinely used in discerning materials structure, chemistry, and properties.