Color mapping, which involves assigning colors to the individual elements of an underlying data distribution, is a commonly used method for data visualization. Although color maps are used in many disciplines and for a variety of tasks, in this study we focus on its usage for visualizing luminance maps. Specifically, we ask ourselves the question of how to best visualize a luminance distribution encoded in a high-dynamic-range (HDR) image using false colors such that the resulting visualization is the most descriptive. To this end, we first propose a definition for descriptiveness. We then propose a methodology to evaluate it subjectively. Then, we propose an objective metric that correlates well with the subjective evaluation results. Using this metric, we evaluate several false coloring strategies using a large number of HDR images. Finally, we conduct a second psychophysical experiment using images representing a diverse set of scenes. Our results indicate that the luminance compression method has a significant effect and the commonly used logarithmic compression is inferior to histogram equalization. Furthermore, we find that the default color scale of the Radiance global illumination software consistently performs well when combined with histogram equalization. On the other hand, the commonly used rainbow color scale was found to be inferior. We believe that the proposed methodology is suitable for evaluating future color mapping strategies as well.