Optimal colors can predict luminosity thresholds in natural scenes

Abstract

Luminosity thresholds define the luminance boundary at which a surface color shifts in appearance from being perceived as an illuminated surface to appearing self-luminous. Previous research suggests that the human visual system infers these thresholds based on internal references of physically realizable surface colors under a given illumination, referred to as the physical gamut. A surface is perceived as self-luminous when its luminance exceeds the upper limit of this empirically internalized gamut. However, the precise structure and boundaries of these gamuts remain uncertain. Optimal colors, which represent theoretical surface reflectances under specific illuminants, have been shown to provide an effective model for visualizing and computing the physical gamuts. In prior studies, optimal colors have successfully predicted luminosity thresholds; however, these findings were limited to highly simplified, abstract stimuli. Whether this framework generalizes to more naturalistic viewing conditions has remained an open question. In the present study, we demonstrate that the theory of an internal reference in the form of an empirically constructed physical gamut, visualized through optimal colors, remains valid under more natural conditions. Our results confirm that optimal colors can still accurately predict luminosity thresholds in such settings. Moreover, our findings suggest that the luminosity thresholds encompass both self-luminosity and naturalness concepts. Consequently, this may imply that the notion of physical gamut could encompass both concepts as well and could be defined as “all physically possible colors in a scene for an object that does not emit light.” These insights have profound potential implications for applied fields (e.g., XR or projection mapping). For example, they provide a theoretical framework that specifies the luminance and color boundaries virtual objects should not exceed to remain realistically integrated into AR/MR scenes. In fundamental science, these findings can improve our understanding of the perception of naturalness in images.