View metadata, citation and similar papers at core.ac.uk brought to you by CORE provided by Newcastle University E-Prints Available online at www.sciencedirect.com ScienceDirect Challenges to color constancy in a contemporary light Anya Hurlbert Color constancy is a prime example of a perceptual constancy, the illumination spectrum [1]. Its presumed behavioural giving stability to mental representations of objects in an purpose is to enable people to use object color as a robust unstable world. Yet color constancy is highly variable, and reliable cue for recognising and interacting with depending on the illumination, the object and its context, and objects, based on their invariant surface spectral reflectance the viewer. Color constancy is particularly challenged by properties [2]. artificial lights that differ from the natural illuminations under which human vision evolved. The rapid developments in solid- In color science, the term color constancy is scarcely used. state lighting technologies revive the need to scrutinise the Instead, it is expressly acknowledged that surface color limits of color constancy, to understand whether and how it is appearance depends on the illumination. Two types of optimised for natural illuminations, and, in turn, to optimise quantitative models exist to predict color appearance, novel lighting technologies for human color perception. For both extensively used in industrial applications. Color these goals, a deeper collaboration between the disciplines of appearance models, or CAMs [3 ,4,5], predict the appear- human vision science and color science is needed. ance of a stimulus specified by its retinal receptoral responses (or specifically, its standard observer tristimulus Address values [6]) and the adapting illumination. A chromatic Institute of Neuroscience, Newcastle University, Framlington Place, adaptation transform (CAT) normalises the stimulus Newcastle upon Tyne NE2 4HH, United Kingdom receptoral responses by the responses to a spectrally Corresponding author: Hurlbert, Anya ([email protected]) neutral surface (one which reflects light equally at all visible wavelengths) under the adapting illumination. Current Opinion in Behavioral Sciences 2019, 30:186–193 Color rendering models, or CRMs, apply to lights. CRMs This review comes from a themed issue on Visual perception characterise an illumination by its effects on the color Edited by Hannah Smithson and John S Werner appearances of a representative set of surfaces, relative to 1 a reference illumination. The reference illumination is a precisely defined broad-band spectrum, close to or on the daylight locus (the chromaticities of daylight, which vary https://doi.org/10.1016/j.cobeha.2019.10.004 from blue to yellow, closely following the Planckian 2352-1546/ã 2019 The Author. Published by Elsevier Ltd. This is an curve; see Figure 1). Effectively, CRMs describe how open access article under the CC BY-NC-ND license (http://creative- well a particular illumination reproduces the color appear- commons.org/licenses/by-nc-nd/4.0/). ance of surfaces under daylight with the most similar chromaticity. (See Figure 2 for an example of a CRM metric, the TM-30-18 [7]). CRMs assume complete Introduction adaptation to the tested illumination, and thus the best There has long been a disjunction between physics-based possible constancy (or fidelity) with respect to the most color science and biology-based vision science in the similar daylight (Table 1). Yet, even so, CRM fidelity approach to studying color, exemplified by the phenome- indices vary widely across illumination spectra, demon- nonofcolorconstancy. Invisionscience,colorconstancyisa strating that constancy of color appearance rarely reaches 2 prime example of a perceptual constancy. Color constancy even its best possible limit [7]. keeps the mental representation of object color stable despite changes in the retinal image due to changes in In vision science, despite the pre-eminence of the phe- the light reflected from objects, in turn due to changes in nomenon, color constancy is also acknowledged as neither 1 CRMs depend on CAMs. TM-30-18 indices use the CAM02-UCS color space [4] rather than the updated CAM16-UCS [5], which calculates coordinates corresponding to lightness, redness–greenness, and yellowness–blueness, and from these, values for hue, brightness, chroma, colorfulness and saturation. The TM-30-18 characterises illuminations by an average color fidelity index (Rf), a gamut area measure (Rg), 48 other hue-specific measures of illumination-dependent appearance changes, and one summary graphic (see Figure 2). Rf describes the similarity, in terms of metric distance, between the perceptual color coordinates of representative surfaces under the test illumination and a reference illumination. The reference illumination is specified as a broad-band illumination of the same correlated color temperature (CCT) (either Planckian radiation, CIE daylight, or a specified mixture of the two, depending on the CCT), that is with the chromaticity of the nearest point on the daylight locus. It is also worth noting that CAMs, and therefore CRMs, undergo continual refinement, utilising different color spaces, and also may be modified to incorporate individual differences in receptoral sensitivities. 2 This variation in color fidelity between illuminations is largely because the chromatic adaptation transform cannot capture the nonlinear changes in cone responses induced by illumination changes on surfaces, especially those with highly chromatic, or highly variable, reflectance functions. Current Opinion in Behavioral Sciences 2019, 30:186–193 www.sciencedirect.com Challenges to colour constancy Hurlbert 187 Figure 1 250 0.8 200 0.7 150 100 Relative Power 0.6 50 0 350 400 450 500 550 600 650 700 750 Wavelength (nm) 0.5 y 0.4 0.3 0.2 0.1 0 00.10.2 0.3 0.4 0.5 0.6 0.7 0.8 x Current Opinion in Behavioral Sciences CIE chromaticity diagram. Blue line: daylight locus, with indicated locations of daylights of CCTs 4000 (D40), 6500 (D65), and 25000 (D250) K. Red line: locus of chromaticities orthogonal (in a perceptually uniform chromaticity plane) to the daylight locus at D65. Grey ellipse: Rough size and orientation of variability in perceptual whitepoints in a dark surround, redrawn from Bosten et al. [48]. Inset: Daylight spectra of 4000 K (orange), 6500 K (black), and 25 000 K (blue). all or none. Estimates of the biological attainability of the sensory level, and, specifically, highest for an object constancy range from the depressing – because of the selection task. Similarly, color constancy as measured by metamerism built into a trichromatic system [8 ,9] (see assessing color category identity across illumination below) – to the optimistic – empirical measurements of changes, is generally high [14,15] and higher than at the color constancy performance, as tabulated in Ref. [10], color appearance level [16]. show an increasing trend from 1986 to 2010 [11]. Yet there is another important distinction between the two fields. It is also increasingly recognised in vision science that In color science, constancy (or rather, the lack thereof) is color constancy, however it is defined or measured, varies measured at the appearance level, where surfaces are considerably between individuals. #thedress – the viral matched in their colorimetric properties only (e.g. hue, internet phenomenon of 2015, in which people argued saturation and brightness). In vision science, this corre- over the color of a dress in a single photograph – brought sponds to the ‘sensory’ level, appropriate for a chromatic home this inter-individual variability [17,18]. Differences adaptation transform that acts directly on retinal in individuals’ underlying color constancy explain their responses. But it is acknowledged that color constancy differences in color naming: those who infer the illumi- is achieved by multiple mechanisms acting on multiple nation to be dim and bluish name the dress as white and levels, from retina to higher cortical areas. Accordingly, gold, whereas those who infer the illumination as bright constancy is also measured at different levels: not only the and yellowish name it blue and black [19–25]. Yet this sensory level (e.g. the ‘hue/saturation’ match in Ref. [12]), ambiguity between illumination and surface reflectance but also the ‘cognitive’ level, where people assess surface seems peculiar to the distribution of chromaticities in the identity, regardless of changes in color appearance (e.g. image, which closely parallel the daylight locus: rotating the ‘paper’ match in Ref. [12]). Using distinct instructions the distribution in the chromaticity plane so that it aligns on distinct tasks, Radonjic and Brainard [13] found that with a red/green axis destroys the polymorphism [26,27]. color constancy is indeed higher on the cognitive level than The phenomenon therefore provides additional impetus www.sciencedirect.com Current Opinion in Behavioral Sciences 2019, 30:186–193 188 Visual perception Figure 2 sensory cognitive M1 same -3 ×10 7 6 ) 2 5 4 M1 M2 3 M2 2 same Spectral power (W/m 1 0 400 450 500 550 600 650 700 750 Wavelength (nm) M3 different Current Opinion in Behavioral Sciences Effects of metameric illuminations on colour appearance. Left panel: Spectral power distributions of 3 illuminations (M1, M2 and M3), metameric to daylight of 6500 K, generated by tuneable multi-channel LED lamps. Middle left panel: Summary TM-30-18 colour distortion graphics for the corresponding metameric illuminations.