The Extent of Metamer Mismatching

Metamer mismatching refers to the fact that two objects reflecting light causing identical colour signals (i.e., cone response or XYZ) under one illunimation may reflect light causing non-identical colour signals under a second illumination_ As a consequence of metamer mismatching, two objects appearing the same under one illuminant can be expected to appear different under the second illunimant. To investigate the potential extent of metamer mismatching, we calculated the metamer mismatching effect for 20 Munsell papers and 8 pairs of illunimants (Logvinenko & Tokunaga, 20 11) using the recent method (Logvinenko, Funt, & Godau, 2012) of computing the exact metan2er mismatch volume boundary. The results show that metamer mismatching is very significant for some lights. In fact, metamer mismatching was found to be so significant that it can lead to the prediction of some paradoxical phenomena, such as the possibility of 20 objects having the same colour under a neutral ("white") light dispersing into a whole hue circle of colours under a red light, and vice versa

Metamer Mismatch Volumes of Flat Grey

Metamer mismatching refers to the fact that two objects reflecting light causing identical colour signals (i.e., cone response or XYZ) under one illumination may reflect light causing nonidentical colour signals under a second illumination. As a consequence of metamer mismatching, two objects appearing the same under the first illuminant can be expected to appear different under the second illuminant. Metamers of the flat grey reflectance (i.e., 50% across the visible spectrum) are of particular interest since they show the potential seriousness of metamer mismatching. Metamer mismatching of flat grey is very significant for some lights and includes the possibility of 20 objects having the same colour signal as flat grey under red light dispersing into a whole hue circle under a neutral (“white”) light. Flat grey under LED illumination is also shown to have a significant metamer mismatch volume when the light is changed to D65

Comparing Colour Camera Sensors Using Metamer Mismatch Indices

A new method of evaluating the colorimetric accuracy of a color camera is proposed that is based on the size (appropriately normalized) of the metamer mismatch volume induced by a change of \u27observer\u27 from camera to human eye and vice-versa. The degree of metamer mismatching indicates the range in the discrepancy of the colour signals that can arise and as such is a more well-founded measure of colorimetric accuracy than traditional spectral-based measures such as the root mean squared difference in fit between the camera and eye\u27s sensitivity functions

A Colour Rendering Index for Dichromats

We propose a method of evaluating the colour rendering quality of light sources tailored to dichromatic vision. To the best of our knowledge, the proposed color rendering index (CRI) is the first such index to be specifically designed for dichromats. Previous CRIs have been defined only for trichromats and they generally rely on measuring ΔE colour differences in a standardized uniform colour space such as CAM02-UCS. Since these spaces are defined only for trichromatic colour vision these methods do not generalize to the case of dichromatic colour vision. The proposed dichromat CRI is based on the metamer mismatch index, which applies to both the trichromatic and dichromatic cases

Evaluation of the IES Method for Evaluating Light Source Color Rendition in terms of Metamer Mismatching

The Illumination Engineering Society’s Rf color rendering index [IES TM-30-15, 201] is compared to the MMCRI [Metamer Mismatching as a Measure of the Color Rendering of Lights, Mirzaei & Funt, Proc. AIC 2015]. IES Rf is based on color differences using a special set of 99 surface reflectances; while, in contrast, MMCRI is based on all theoretically possible reflectances. The two indices evaluate many lights similarly, but the MMCRI ranks some lights—especially those having strong peaks and wavelength regions of minimal power—lower than does Rf. Is this difference in rating simply due to the fact that MMCRI uses all theoretically possible reflectances including step functions? A ‘practical’ version of MMCRI based on a set of 41 million real, measured spectral reflectances, rather than all theoretically possible reflectances, turns out to concur with the original MMCRI and shows that the disagreement between Rf and MMCRI is more fundamental. Overall, the present study suggests that Rf may overrate the color rendering properties of some lights; and, at the very least, indicate the type of lights upon which future psychophysical testing should concentrate

Metamer Mismatching as a Measure of the Color Rendering of Lights

We propose a new method for evaluating the colour rendering properties of lights. The new method uses the degree of metamer mismatching for the CIE XYZ corresponding to flat grey (constant reflectance of 0.5) quantified in terms of the metamer mismatch volume index proposed by Logvinenko et al. (Logvinenko 2014). A major advantage of this method is that unlike many previous color rendering indices it does not depend on the properties of a chosen set of representative test objects

Camera Color Accuracy Evaluated via Metamer Mismatch Moments

A novel method for evaluating the colorimetric accuracy of digital color cameras is proposed based on a new measure of metamer mismatch body (MMB) induced by the change from the camera as an observer to the human standard observer. Previous methods of evaluating the colorimetric accuracy of a camera at the Luther condition [1], the mean CIE&nbsp

Metamer Mismatching in Practice versus Theory

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 ONE 10, 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

Spherical sampling methods for the calculation of metamer mismatch volumes

In this paper, we propose two methods of calculating theoretically maximal metamer mismatch volumes. Unlike prior art techniques, our methods do not make any assumptions on the shape of spectra on the boundary of the mismatch volumes. Both methods utilize a spherical sampling approach, but they calculate mismatch volumes in two different ways. The first method uses a linear programming optimization, while the second is a computational geometry approach based on half-space intersection. We show that under certain conditions the theoretically maximal metamer mismatch volume is significantly larger than the one approximated using a prior art method

Object-Color Description Under Varying Illumination

Given a fixed and uniform illumination, metameric objects appear as the same color. However, when the illumination is altered, two metameric reflecting objects under the first illuminant may no longer produce the same color signal under the second. This situation is called metamer mismatching. Metamer mismatching poses several challenges for the camera and display industries as well as color-based computer vision technology.In light of metamer mismatching, the present study criticizes the conventional approaches to color description when the illuminant alters, and then lays a foundation to robustly describe object colors under varying illumination conditions. Later, the degree of metamer mismatching is used as a measure of the quality of lights. We demonstrate that although the common color spaces such as CIELAB and related spaces in the literature may work well for a fixed illuminant, they can lead to poor results when the illuminant is changed. In view of these problems, new descriptors for hue, lightness and chroma are presented that are based on properties of a Gaussian-like spectrum metameric to the given color tristimulus coordinates. Experiments show that the new Gaussian-based appearance descriptors correlate with different descriptors as well as the CIECAM02 appearance model does on average. Furthermore, the Gaussian-based descriptors are significantly more stable than the descriptors defined in the CIECAM02 appearance model.Afterwards, the problem of predicting how the color signal arising in response to light reflected from the surface of an object is likely to change when the lighting alters is investigated. A new method, called the Gaussian Metamer (GM) method is proposed for predicting what a color signal observed from a surface under a first light is likely to be when the same surface is lit instead by a second light. Due to metamer mismatching, there is not a unique answer for this problem. Our approach is to use one of the possible metamers that is likely to do well on average. The results outperform other state-of-the-art prediction methods