Metamer mismatching in practice versus theory

Abstract

Metamer mismatching (the phenomenon that two objects matching in color under one illuminant may not match under a different illuminant) potentially has important consequences for color perception. Logvinenko et al. [PLoS ONE10, e0135029 (2015)] show that in theory the extent of metamer mismatching can be very significant. This paper examines metamer mismatching in practice by computing the volumes of the empirical metamer mismatch bodies and comparing them to the volumes of the theoretical mismatch bodies. A set of more than 25 million unique reflectance spectra is assembled using datasets from several sources. For a given color signal (e.g., CIE XYZ) recorded under a given first illuminant, its empirical metamer mismatch body for a change to a second illuminant is computed as follows: the reflectances having the same color signal when lit by the first illuminant (i.e., reflect metameric light) are computationally relit by the second illuminant, and the convex hull of the resulting color signals then defines the empirical metamer mismatch body. The volume of these bodies is shown to vary systematically with Munsell value and chroma. The empirical mismatch bodies are compared to the theoretical mismatch bodies computed using the algorithm of Logvinenko et al. [IEEE Trans. Image Process.23, 34 (2014)]. There are three key findings: (1) the empirical bodies are found to be substantially smaller than the theoretical ones; (2) the sizes of both the empirical and theoretical bodies show a systematic variation with Munsell value and chroma; and (3) applied to the problem of color-signal prediction, the centroid of the empirical metamer mismatch body is shown to be a better predictor of what a given color signal might become under a specified illuminant than state-of-the-art methods.

Uncertainty of sensory signal explains variation of color constancy

“…Lightness and chroma were positively correlated with the size of metamer mismatch areas [r(22) ¼ 0.53; p ¼ 0.008 and r(22) ¼ 0.57, p ¼ 0.004]. While the positive correlation with lightness is in line with previous observations, the positive correlation for chroma is not (Zhang et al, 2015(Zhang et al, , 2016. Lightness was also positively correlated to deviations from the natural target predictions [r(22) ¼ 0.56, p ¼ 0.004] in the color constancy task.…”

“…However, higher levels of metamerism may exist going from one natural scene to another, implying that metamer mismatching might be much higher than suggested by the above study (Foster et al, 2006). In fact, other studies observed that real surfaces show considerable degrees of metamerism and metamer mismatching (Logvinenko et al, 2015;Zhang et al, 2015Zhang et al, , 2016. Moreover, metamer mismatching is known to be an important problem in many practical contexts, such as printing (e.g., Fairchild & Johnson, 2004;Samadzadegan & Urban, 2013), lighting (e.g., Schanda, 2007;Viénot, Coron, & Lavédrine, 2011), photography (e.g., Belt, 2008, and art restoration (e.g., Berns, 2016).…”

“…Nevertheless, the metamer mismatch volumes for reflectances in the visual environment are still considered to be much smaller than the complete metamer mismatch volumes used here (Zhang et al, 2016). For this reason, it would be important to test whether the correlations observed in the present study still hold for metamer mismatching that occurs in the visual environment.…”

The optimal number of sensors for a digital imaging system from the perspective of metamer mismatching

“…Both the optimal and practical spectral reflectance are used to analyze the degree of metamer mismatching for the digital imaging systems with different numbers of sensors; 25 303 486 practical spectral reflectance distributions were collected in a previous study, 13 and an additional 22 282 536 practical spectral reflectance distributions have been derived from 50 indoor and outdoor images captured under daylight, 27 indoor images F I G U R E 1 Spectral sensitivity functions of the digital imaging system with five (A) and seven (B) sensors captured under mixed artificial illumination, 32 and 201 images captured in natural outdoor lighting environments 33 by hyperspectral imaging systems. Overall, a database containing the spectral reflectance of 47 586 022 practical samples, from 400 nm to 700 nm at 10 nm intervals, was assembled.…”

Construction of Spectral Discoloration Model for Red Lead Pigment by Aging Test and Simulating Degradation Experiment

“…The main purpose of constructing the discoloration model is to restore the fading 2 Journal of Spectroscopy artworks to their original colors in correct way. However, current discoloration models for red lead pigment are all based on the colorimetric theory, which were constructed in RGB or Lab color spaces and inevitably influenced by metameric problem [14].…”