Illuminant Spectral Power Distribution Research Articles

Improved method for spectral reflectance estimation and application to mobile phone cameras.

We propose an improved method for estimating surface-spectral reflectance from the image data acquired by an RGB digital camera. We suppose a multispectral image acquisition system in the visible range, where a camera captures multiple images for the scene of an object under multiple light sources. First, the observed image data are described using the camera spectral sensitivities, the surface-spectral reflectance, the illuminant spectral power distributions, an additive noise term, and a gain parameter. Then, the optimal reflectance estimate is determined to minimize the mean-square error between the estimate and the original surface-spectral reflectance. We attempt to further improve the estimation accuracy and develop a novel linear estimator in a more general form than the Wiener estimator. Furthermore, we calibrate the imaging system using a reference standard sample. Finally, experiments are performed to validate the proposed method for estimating the surface-spectral reflectance using different mobile phone cameras.

Minimizing observer metamerism in display systems

AbstractObserver metamerism has long been known to be an issue of concern for color engineers. With the advent and mass proliferation of narrow‐band display devices, this article addresses the issue of observer metamerism and puts it in the context of first understanding the implication of interobserver variations in the color matching functions (CMFs) and additionally designing the ideal set of primary illuminant spectral power distributions (SPDs) such that observer metamerism is minimized. The article also suggests the use of the Full Width Half Maximum (FWHM) and the number of modes (peaks) in the primary SPDs as a means of assessing the susceptibility to observer‐to‐observer variations between various modern display technologies. © 2009 Wiley Periodicals, Inc. Col Res Appl, 34, 391–398, 2009

Analytic solution for separating spectra into illumination and surface reflectance components

The measured light spectrum is the result of an illuminant interacting with a surface. The illuminant spectral power distribution multiplies the surface spectral reflectance function to form a color signal--the light spectrum that gives rise to our perception. Disambiguation of the two factors, illuminant and surface, is difficult without prior knowledge. Previously [IEEE Trans. Pattern Anal. Mach. Intell.12, 966 (1990); J. Opt. Soc. Am. A21, 1825 (2004)], one approach to this problem applied a finite-dimensional basis function model to recover the separate illuminant and surface reflectance components that make up the color signal, using principal component bases for lights and for reflectances. We introduce the idea of making use of finite-dimensional models of logarithms of spectra for this problem. Recognizing that multiplications turn into additions in such a formulation, we can replace the original iterative method with a direct, analytic algorithm with no iteration, resulting in a speedup of several orders of magnitude. Moreover, in the new, logarithm-based approach, it is straightforward to further design new basis functions, for both illuminant and reflectance simultaneously, such that the initial basis function coefficients derived from the input color signal are optimally mapped onto separate coefficients that produce spectra that more closely approximate the illuminant and the surface reflectance for any given dimensionality. This is accomplished by using an extra bias correction step that maps the analytically determined basis function coefficients onto the optimal coefficient set, separately for lights and surfaces, for the training set. The analytic equation plus the bias correction is then used for unknown input color signals.

Feature Article: Omnidirectional Scene Illuminant Estimation Using A Mirrored Ball

This paper proposes a method for estimating the omnidirectional distribution of the scene illuminant spectrum from images taken by a camera aimed at a mirrored ball. First, we introduce measuring systems using a RGB color camera and a mirrored ball placed at an arbitrary location in a natural scene for acquiring ambient light from any direction. Second, in order to estimate the direction from which light comes to the ball, we calibrate the measuring systems and determine a mapping between the image coordinates of the spherical ball and the directions of light rays in the world. An omnidirectional image representing the spatial distribution of ambient light sources is created in a polar coordinate system. Third, we present an algorithm for recovering the illuminant spectral power distribution from the image data at each pixel and the reflectance data of the mirrored ball. Finally, we execute an experiment in a natural environment with multiple light sources for examining the feasibility of the proposed method.

Scene illuminant classification: brighter is better.

Knowledge of the scene illuminant spectral power distribution is useful for many imaging applications, such as color image reproduction and automatic algorithms for image database applications. In many applications accurate spectral characterization of the illuminant is impossible because the input device acquires only three spectral samples. In such applications it is sensible to set a more limited objective of classifying the illuminant as belonging to one of several likely types. We describe a data set of natural images with measured illuminants for testing illuminant classification algorithms. One simple type of algorithm is described and evaluated by using the new data set. The empirical measurements show that illuminant information is more reliable in bright regions than in dark regions. Theoretical predictions of the algorithm's classification performance with respect to scene illuminant blackbody color temperature are tested and confirmed by using the natural-image data set.

Possible re-definition of the CIE Standard Daylight Illuminant spectral power distribution

Possible re-definition of the CIE Standard Daylight Illuminant spectral power distribution

Multichannel vision system for estimating surface and illumination functions

This paper describes a set of experimental measurements and theoretical calculations designed to recover both the surface-spectral reflectance function and the illuminant spectral-power distribution from the image data. A multichannel vision system comprising six color channels was created with the use of a monochrome CCD camera and color filters. The spectral sensitivity of each color channel is calibrated, and the dynamic range of the camera is extended for sensing a wide range of intensity levels. Three algorithms and the corresponding results are introduced. First, a method of choosing the appropriate dimension of the linear model dimensions is introduced. Second, the illuminant parameters are estimated from the sensor measurements made at multiple points within separate objects. Third, the sensor responses are corrected for highlight and shading variations. The body reflectance parameters, unique to each surface, are recovered from these corrected values. Experimental results with a small number of test surfaces and a simple illumination geometry demonstrate that the illuminant spectrum and the surface-spectral reflectance functions can be recovered to within typical deviations of 1% and 4%, respectively.

Luminosity thresholds: effects of test chromaticity and ambient illumination.

Color constancy is often modeled on the assumption that color appearance in natural scenes is a function of the visual system's estimates of surface reflectance. Some stimuli, however, do not look like illuminated surfaces. Instead, they appear to be self-luminous. We hypothesized that the appearance of luminosity occurs when the visual system estimates a reflectance spectrum that is outside the gamut of physically realizable surfaces. To test this idea, we measured luminosity thresholds as a function of stimulus chromaticity and illuminant spectral power distribution. Observers adjusted the luminance of a test patch until it just appeared self-luminous. The test patch was spot illuminated by a computer-controlled projection colorimeter viewed in an experimental room lit diffusely by computer-controlled theater lamps. Luminosity thresholds were determined for a number of test patch chromaticities under five experimental illuminants. The luminosity thresholds define a surface in color space. The shape of this surface depends on the illuminant. We were able to describe much of the luminosity threshold variation with a simple model whose parameters define an equivalent illuminant. In the context of our model, the equivalent illuminant may be interpreted as the illuminant perceived by the observer. As part of our model calculations we generalized the classic notion of optimal stimuli by incorporating linear-model constraints. Given the equivalent illuminant, the model predicts that a patch will appear self-luminous when it is not consistent with any physically realizable surface seen under that illuminant. In addition, we show that much of the variation of the equivalent illuminant with the physical illuminant can be modeled with a simple linearity principle. The fact that our model provides a good account of our data extends the physics-based approach to judgements of self-luminosity. This in turn might be taken as support for the notion that the visual system has internalized the physics of reflectance.

The relation between the color of the illuminant and the color of the illuminated object

The relation between the color of the illuminant and the color of the illuminated object

Color constancy. III. General linear recovery of spectral descriptions for lights and surfaces.

We present a color-constancy algorithm that uses quantum-catch data from reflected lights to recover surface reflectance functions and illuminant spectral power distributions. The algorithm recovers both surface and light-source spectral properties simultaneously. The method works in all situations that were handled by the earlier two-stage algorithms of Maloney and Wandell [J. Opt. Soc. Am. A 3, 29 (1986)] and D'Zmura and Iverson [J. Opt. Soc. Am. A 9, 490 (1992); 10, 2148, 2166 (1993); 11, 1970 (1994)]. In addition, the method handles problems that lie outside the scope of earlier algorithms. Using this method, a trichromatic visual system can recover, when provided adequate information, spectral descriptions of arbitrarily high accuracy for lights and surfaces. We determine conditions under which bilinear models can be used to recover color properties uniquely with the new procedure, and we formulate an algorithm for checking whether a particular bilinear model provides perfect color constancy. This research extends our analysis of linear methods for color constancy begun earlier [J. Opt. Soc. Am. A 10, 2148, 2166 (1993)].

Color constancy II Results for two-stage linear recovery of spectral descriptions for lights and surfaces

Our analysis of color constancy in a companion paper [J. Opt. Soc. Am A 10, 2148 (1993)] provided an algorithm that lets one test how well linear color constancy schemes work. Here we present the results of applying the algorithm to a large parametric class of color constancy problems involving bilinear models that relate photoreceptoral spectral sensitivities, surface reflectance functions, and illuminant spectral power distributions. These results, supported by simulation and further analysis, provide a detailed classification of two-stage linear methods for recovering the spectral properties of reflectances and illuminants from reflected lights.

Rethinking the White Point

In this talk we will discuss calibration transforms that map the XYZ values generated by the same surface under different illuminants. We use the phrase calibration transforms to distinguish between analyses based on the physical properties of surfaces and illuminants, and to distinguish them from appearance transforms based on measurements of color appearance. Calibration transforms describe how the XYZ coordinates measured for a surface change with illumination. Appearance transforms describe how the XYZ coordinates of a particular appearance change with illumination. The change in XYZ values due to calibration and appearance transforms do not generally coincide.Calibration and appearance transforms serve different and useful functions in color management systems. Calibration transforms can be used to correct the device-independent color descriptors of surfaces that have been calibrated for one illuminant but will be rendered under a different illuminant. These calculations play an important role in device calibration. Appearance transforms share a similar computational structure, but they have a different goal and are not XYZ matches.One step in performing automated calibration transforms is to estimate the illuminant spectral power distribution (SPD). Performing the transform is much simpler if one can estimate the illuminant SPD from a three-sensor device, rather than using a spectroradiometer. We first analyze an illuminant estimation method based on using linear models of the illuminant SPD. This solution is taken from the color constancy literature and assumes very little or no information about the objects in the image.In many practical applications the need for accurate calibration transforms outweighs the advantages of algorithms based on little or no information about the surfaces and illuminants in a scene. In this paper we describe how a measurement of a single calibration with known surface target, such as the Macbeth Color-Checker, improves our ability to estimate the illuminant SPD. This method may be useful in practical applications where a single calibration measurement is permitted.

Remarks on signal-processing explanations of the trichromacy of vision

We present criticism of signal-processing arguments recently used to explain the trichromacy of color vision. In particular, we note that illuminant spectral power distributions (SPD's) can be metameric even when the signal-processing arguments state that trichromatic vision represents such SPD's without ambiguity. Being statistical in nature, these arguments need not apply to each individual SPD; however, further use of the arguments calls for attributing them to some ensemble of SPD's and then testing whether the ensemble satisfies the underlying statistical assumptions.

Limitations of color constancy

Theories of adaptation, such as those based on the proportionality law of von Kries, provide detailed predictions concerning perception of object colors when illuminant spectral power distribution is changed. Since these predictions depart from the simple ideal of color constancy, a question arises of the relationships among data, theories, and the ideal of constancy. Projecting the data of a constancy experiment into an opponent-color system indicates that constancy tends to hold well for illuminant shifts in the blue–yellow direction but less well for shifts in the red–green direction. This observation is consistent with a theory based on von Kries adaptation.

Necessary and sufficient conditions for Von Kries chromatic adaptation to give color constancy.

Necessary and sufficient spectral conditions are presented for Von Kries chromatic adaptation to give color constancy. Von-Kries-invariant reflectance spectra are computed for illuminant spectral power distributions that are arbitrary linear combinations of the first three daylight phases. Experiments are suggested to test models of color constancy using computed spectra (either exact or approximate) within the illuminant-invariant framework.