Abstract

Previous studies have shown that the radiant flux that needs to be emitted by an illumination system, can be significantly reduced by optimizing its spectral power distribution to the object reflectance spectra, without inducing perceptible chroma or hue shifts of the illuminated objects. In this paper, the idea is explored to vary the spectral power distribution at different positions in the illuminated scene, in order to tailor the color appearance of objects. For this, a spatially variable, laser diode based illumination system is considered with three primaries and large color gamut. The color rendering performance of the illumination system is quantified via the IES TM-30-2018 method. It is shown that it is possible to reach the maximal color gamut score that is theoretically allowed by the corresponding color fidelity score. This is a unique property of an illumination system with a spatially variable spectral power distribution. The radiant flux requirements of this laser diode based illumination system are theoretically investigated for various color rendering settings, showing reduced power requirements for higher color gamut. The possibility to tune color rendering is also experimentally demonstrated with a set-up that consists of a commercially available laser projector with a hyperspectral camera. By including a feedback optimization algorithm, it is possible to reach the targeted color rendering performance.

© 2019 Optical Society of America under the terms of the OSA Open Access Publishing Agreement

1. Introduction

For many lighting applications, color rendering is one of the most important properties of a light source [1]. Different metrics exist to evaluate color rendering [24]. Most of these metrics quantify the resulting color differences when illuminating a set of test color samples by the light source compared to illumination with a reference light source [58].

The idea of optimizing the spectral power distribution (SPD) of the illumination source for specific object reflectance spectra was theoretically explored in several papers [911]. When considering a single object reflectance spectrum, it is possible to minimize the required radiant flux of the illumination source while assuring the same or very similar color and brightness appearance as under a reference light source. This implies that the optimized SPD induces the same color rendering as the reference light source. With this approach, energy saving ratios of 40% and higher were reported for different object reflectance spectra. Higher energy saving ratios are possible when allowing noticeable color shifts but this typically corresponds with reduced color rendering performance. These theoretical results were also validated with a practical set-up in which specific colored objects were illuminated with optimized SPD’s that were generated by combining nine narrowband LEDs [12].

Of course, a typical illuminated scene contains many different objects, all with different reflectance spectra [13]. So, to make practical use of variable SPD’s, a smart lighting system is needed which monitors all object reflectance spectra in the scene and spatially adapts the SPD of the illumination to the object reflectance spectrum at each specific position. Some conceptual ideas for lighting systems capable of generating different illumination spectra in space are described in [14] and the practical aspect of visual clarity for such point-by-point light projection systems has recently been addressed [15].

Indeed, digital video projectors could in fact be seen as lighting systems capable of generating spatially variable illumination spectra. Even more, commercial projection systems are already being used to combine spot lighting, ambient illumination and still or moving images [16]. Unfortunately, most current video projectors use spatial light modulators (e.g. Digital Micromirror Device (DMD), Liquid Crystal Display (LCD) or Liquid Crystal on Silicon (LCOS)) that selectively block the light flowing from the light source through the projection system in order to create RGB images [17]. It is obvious that such a system can never by an energy efficient lighting luminaire. However, by combining direct laser diodes with advanced spatial light modulators it could become possible to create very efficient point-by-point light projection systems in the not too distant future.

An energy efficient method to generate high-resolution light patterns, is e.g. to employ a phase modulating spatial light modulator. Such a phase-only element redirects light when forming the light pattern, rather than absorbing or blocking light. While research of projection systems based on this technology is ongoing [1820], these type of systems are not yet able to provide full-color, high quality images. Another efficient projector approach that is already available are scanned laser pico-projectors [21]. Also in this case, light is redirected rather than absorbed, which allows high efficiency. The light output of these systems however is currently quite limited. What both types of projection systems have in common, is the fact that single-color laser diodes (typically red (R), green (G) and blue (B)) are the used light source technology and that the system redirects parts of their flux towards specific positions.

Due to the fact that laser diodes emit light with a very narrow spectrum, a wide color gamut can be obtained. This means that a spatially variable lighting system based on multiple laser diodes allows a profound color tuning of the illuminated scene. Indeed, if the object reflectance spectrum at a specific position is known; then, by illuminating that position with a certain ratio of narrow-band red, green and blue laser light, almost any color can be obtained for the light that is reflected from the object surface at that position. This implies that the color appearance of the complete illuminated scene can be fully tailored. For lighting applications, this is an aspect which has never been considered before.

In this paper, the color tuning possibilities and intrinsic energy saving potential of such laser diode based illumination systems are investigated for different lighting scenarios; both theoretically and experimentally. It is shown that exceptional color rendering performance can be realized with a spatially variable laser illumination that goes beyond what is possible with any other static illumination system. When analyzing the energy requirements of the system, only the optical power requirements are considered which is similar to the approach that is followed in other studies about SPD optimization for lighting.

2. Spatially variable laser illumination

2.1 Lighting setup

The lighting setup that is considered in this paper is conceptually shown in Fig. 1. A colorful scene is illuminated by a laser diode based lighting system. This system is capable of illuminating different positions in the scene with a variable spectrum S(x,y)(λ). A camera system monitors the object reflectance spectra R(x,y)(λ) at every position in the illuminated scene and uses therefore the incident light of a calibrated broadband light source. Two methods can be envisaged to do this in practice. The most straightforward option is to use a hyperspectral camera [22,23] and this approach is used for the experiments. An alternative and cheaper method could be to use state-of-the-art spectral reflectance estimation algorithms that use a common RGB camera [2426]. The position of the camera should be close to the position of the observer(s) of the illuminated scene, such that the monitored object reflectance spectra, correspond with the reflectances of the incident laser diode light towards the observer(s).

 figure: Fig. 1.

Fig. 1. Conceptual illustration of the considered smart lighting setup.

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2.2 Spectral power distribution

The SPD of the laser diode based illumination system onto a certain spatial area with a specific object reflectance is given by

$${S_{(x,y)}}(\lambda ) = {p_\textrm{b}}{S_\textrm{B}}(\lambda ) + {p_\textrm{g}}{S_\textrm{G}}(\lambda ) + {p_\textrm{r}}{S_\textrm{R}}(\lambda ).$$
In this Eq., SB(λ), SG(λ) and SR(λ) correspond with the SPD of the maximal amount of blue, green and red light that can be emitted by the system towards the considered spatial area. The values pb, pg and pr thus correspond with the fraction of blue, green and red light. For the sake of simplicity, it is assumed that the radiant flux corresponding with SB(λ), SG(λ) and SR(λ) are the same, and equal to 1 W. This implies that the total radiant flux is simply given by
$${{\Phi }_{\mathop{\textrm {e}}\nolimits} } = {p_{\textrm{b}}} + {p_{\textrm{g}}} + {p_{\mathop{\textrm {r}}\nolimits} }[{\textrm{in Watt}}].$$
In the theoretical analysis that is presented in section 3, the radiant flux requirements of this laser diode based illumination model are calculated for various object reflectance’s, in order to obtain the same color and brightness appearance as when illuminated by a reference light source. The CIE Standard Illuminant D50 with a total radiant flux of 1 W over the wavelength range from 380 nm to 780 nm, is always used as a reference. This means that the energy saving potential can simply be evaluated by comparing the result of Eq. (2) with the reference value of 1 W.

For all calculations, with one exception, the peak wavelengths of the laser diodes are chosen as 467 nm, 532 nm, and 630 nm, respectively. These peak wavelengths are derived from the Rec. 2020 standard [27]. By choosing these three primaries, the color gamut of the considered illumination covers 63.3% of all chromaticities and 99.9% of Pointer’s gamut [28] in the CIE 1931 chromaticity diagram (see Fig. 2). The SPD’s of the blue, green, and red laser diode light (SB(λ), SG(λ) and SR(λ)) are modelled by Ohno’s model (at 1 nm interval) [29] with spectral widths equal to 3 nm. This spectral model and width however, have a marginal impact on the results, as long as the spectral width of the chosen primaries is sufficiently small. This is a condition that typically holds for direct laser diodes.

 figure: Fig. 2.

Fig. 2. (a) An example of the SPD of the laser diode illumination. (b) The SPD of the reference illuminant D50. (c) The color gamut of the laser based illumination covers almost entirely Pointer’s gamut.

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2.3 Tuning color

With a spatially variable laser illumination with wide color gamut, almost any color can be obtained for the reflected light by objects. This means that if the object reflectance is known, that object can be illuminated by the needed fractions of blue, green and red laser diode light, in order to render exactly the same color as when the object would be illuminated by the reference illuminant. In other words, the CRI - Ra of a spatially variable laser lighting system can be made equal to 100.

The recent IES TM-30-2018 method [30] constitutes a two-measure system for evaluating light sources’ color rendition. It is quantified by a color fidelity score and a color gamut score. The color fidelity score Rf is an improved version of the CIE color rendering index Ra. To quantify the color differences of illumination by the test light source compared to a reference illuminant, the wide range of object reflectance’s that occur in practice, are sampled by a set of 99 test color samples (TCS’s). This is a significant improvement compared to CRI - Ra which considers only 8 TCS’s. These 99 TCS’s were selected on the basis of color space uniformity and spectral uniformity [31] and were derived from various types of objects [32].

The color gamut score Rg on the other hand, quantifies the average change in chroma (in CAM02UCS a'b’ space [33]) of the test light source compared to a reference illuminant, in which saturating color shifts correspond to a color gamut score increase [32,34]. The underlying idea of this metric is the fact that various studies have reported that light sources that increase chroma are described as more pleasant by most observers [5,3438]. Considering both color fidelity and color gamut results in a two-axis system for evaluating color rendering with IES TM-30-2018 (see Fig. 3(a)). Depending on the illumination task at hand, higher fidelity Rf or higher gamut Rg is desired.

 figure: Fig. 3.

Fig. 3. (a) Tradeoff between fidelity and gamut; light sources can only reside in the non-shaded area. With a spatially variable laser illumination an optimal trade-off between fidelity and gamut can be achieved: i.e. a combined fidelity/gamut score along the red dotted line. (b) The normalized (total radiant flux = 1W) spectral power distribution of the considered phosphor-converted LED (c) Color gamut shape of this LED and the reference illuminant (D50).

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However, for all light sources there is a fundamental limiting relationship between fidelity and gamut. Perfect fidelity (Rf = 100) can only be obtained when colors exactly match those under the reference illuminant, thus yielding no variation in chroma (→ Rg = 100). There is also a maximum amount of chroma that can be gained (or lost) for a given color shift and this is the case if all color shifts are in the positive (or negative) radial direction. Therefore, there is a theoretical maximum and minimum gamut Rg that can be achieved for a given fidelity Rf, what can also be seen in Fig. 3(a). The Rf and Rg values of light sources with optimal SPD in terms of both color fidelity and color gamut should therefore be close to this theoretical trade-off between both quantities with Rg maximal [39,40], indicated by the red dotted line in Fig. 3(a).

In practice, it is possible to optimize the SPD to enhance color gamut. However, ensuring color shifts in the positive radial direction for all object reflectance’s is not possible with a light source with a static SPD. This is illustrated in Fig. 3(c) for a specific case. There one can see the induced color shifts of a typical phosphor-converted LED compared to reference illuminant D50 for the 16 hue bins that are used in IES TM-30-2018. Together with undesired changes in hue, one notices both saturating and de-saturating color shifts. The light source SPD can certainly be optimized to have better color rendering performance, but a static SPD that induces only positive chroma shifts and no changes in hue for all object reflectance’s, cannot be attained.

However, this fundamental limitation disappears when considering a variable laser illumination with wide color gamut, because, any color can be obtained for the reflected light by different objects. This means that purely saturating color shifts with respect to the reference illuminant can be generated for each object of which the object reflectance is known. This further implies that for each scene with known object reflectance’s, the illumination can be tuned such that a chosen loss of color fidelity is maximally translated into an increase of color gamut. This is a unique property of a spatially variable laser illumination, which can never be realized with light sources with a fixed SPD.

In the following section, the radiant flux requirements in order to achieve a certain color rendering performance are investigated for a spatially variable laser illumination system. For evaluating color rendering, the IES TM-30-2018 metric is used. In this metric color fidelity and color gamut are calculated from color differences with respect to a reference illuminant. This reference illuminant normally depends on the correlated color temperature (CCT) of the test light source. In this case, the variable laser illumination can generate different SPD’s, and thus it has no inherent CCT. In this paper, the reference illuminant is chosen to be the CIE Standard Illuminant D50, which is the reference illuminant for sources with CCT = 5000 K [30].

3. Theoretical analysis

3.1 Calculation methods

The IES TM-30-2018 metric for color rendition relies on a set of 99 TCS’s for which the CAM02-UCS color coordinates (J′, a′, b′) [33] are calculated of the reflected light by these samples when illuminated by the test light source and the reference illuminant. The color-appearance difference of the ith TCS is then calculated as

$$\Delta {E_i} = \sqrt {{\Delta J}^{{\prime}2}_{i} + {\Delta a}^{{\prime}2}_{i} + {\Delta b}^{{\prime}2}_{i}} ,$$
where ${\Delta }{J^{\prime}_i}$, ${\Delta }{a^{\prime}_i}$, ${\Delta }{b^{\prime}_i}$ refer to the CAM02-UCS color coordinate differences of the ith TCS illuminated by the test light source and reference illuminant, respectively. Then the arithmetic mean is calculated of these color-appearance differences for all TCS’s in order to obtain an average color difference ΔE, from which the color fidelity can be calculated as follows
$${R_\textrm{f}}{ = }10 \times \ln ({e^{(100 - {c_\textrm{f}} \times \Delta E)/10}} + 1)\,{\textrm {with}} \,{c_{\mathop{\textrm {f}}\nolimits} }{ = 6}{.73}{.}$$
For the color gamut score, the (J′, a′, b′) color coordinates of the 99 TCS’s are grouped into 16 hue bins of equal width. In each bin the average values of a’ and b’ are computed, resulting in two 16-point polygons in the (a′, b′) plane for the test source and reference illuminant, respectively. The color gamut score is then equal to:
$${R_\textrm{g}} = 100 \times {A_{\textrm{test}}}/{A_{\textrm{ref}}},$$
with Atest and Aref the areas of the test and reference polygons in the (a′, b′) plane.

When analyzing the performance of the variable laser illumination configuration, each TCS is illuminated by a SPD that is described by Eq. (1) with (pb, pg, pr) as only variables. These three values should be calculated in such a way that the resulting SPD generates the desired color coordinates for that sample in order to comply with the desired Rf and Rg values.

Consider e.g. the case in which both Rf and Rg should be equal to 100, meaning that the resulting color coordinates of each TCS when illuminated by the laser diode SPD should be equal to those when the TCS is illuminated by the reference illuminant. First the XYZ tristimulus values (CIE 1964 10° Standard Observer) are calculated of the reflected light under the reference illumination. Since the (J′, a′, b′) color coordinates are directly related to these tristimulus values, it suffices to realize the same tristimulus values with the laser diode SPD. The XYZ values that are obtained with the laser illumination SPD, can be calculated from the XcYcZc values (with c= b, g, and r) that are obtained by illuminating the TCS with the SPD’s SB(λ), SG(λ) and SR(λ) of the individual laser diode peaks (see Eq. (1)). This relation can be expressed as

$$\left[ \begin{array}{l} X\\ Y\\ Z \end{array} \right]{\ =\ }\left[ \begin{array}{l} {X_\textrm{b}}{X_\textrm{g}}{X_\textrm{r}}\\ {Y_\textrm{b}}{Y_\textrm{g}}{Y_\textrm{r}}\\ {Z_\textrm{b}}{Z_\textrm{g}}{Z_\textrm{r}} \end{array} \right] \cdot \left[ \begin{array}{l} {p_\textrm{b}}\\ {p_\textrm{g}}\\ {p_\textrm{r}} \end{array} \right],$$
in which pb, pg and pr correspond again with the fractions of the illumination by the different laser diodes. Therefore, these (necessary) fractions can be easily calculated for each TCS by
$$\left[ \begin{array}{l} {p_\textrm{b}}\\ {p_\textrm{g}}\\ {p_\textrm{r}} \end{array} \right] = {\left[ \begin{array}{l} {X_\textrm{b}}{X_\textrm{g}}{X_\textrm{r}}\\ {Y_\textrm{b}}{Y_\textrm{g}}{Y_\textrm{r}}\\ {Z_\textrm{b}}{Z_\textrm{g}}{Z_\textrm{r}} \end{array} \right]^{ - 1}} \cdot \left[ \begin{array}{l} X\\ Y\\ Z \end{array} \right].$$
As mentioned before, the sum of pb, pg and pr is equal to the total radiant flux onto the considered TCS and this SPD will give the same brightness and color appearance as compared to illuminating the TCS with the reference illuminant D50 with a total radiant flux of 1 W.

Next, we consider the case in which a loss of color fidelity is maximally translated into an increase of color gamut. The XYZ values for each TCS, under reference illumination, can be transformed to the corresponding (J′, a′, b′) coordinates in the CAM02-UCS color-appearance space [30]. The J′ value refers to the lightness. The angle that the projected (J′, a′, b′) point makes in the (a′, b′) plane with respect to the positive a′-axis indicates the hue, and the distance to the origin indicates the chroma. When calculating the SPD in order to enhance the chroma of the i-th TCS, the lightness and hue should remain constant in order to ensure minimal reduction of the color fidelity. This implies that the color-appearance difference corresponds with a radial displacement in the (a′, b′) space. This allows to calculate the target (${J^{\prime}_{i,T}}$, ${a^{\prime}_{i,T}}$, ${b^{\prime}_{i,T}}$) values that should be obtained for each TCS when illuminated by the laser diode SPD.

When using Eq. (4), the chosen color fidelity value Rf can be converted to a color-appearance difference ΔE with the following Eq.:

$$\Delta E = \frac{{100 - 10 \cdot \ln ({e^{{R_\textrm{f}}/10}} - 1)}}{{{c_\textrm{f}}}},$$
Because of the fixed lightness and hue, the target values for the CAM02-UCS coordinates can be calculated from ΔE, as
$$\left\{ \begin{array}{l} {{J^{\prime}}_{i\textrm{,new}}} = {{J^{\prime}}_i}\\ {{a^{\prime}}_{i\textrm{,new}}} = a^{\prime} + \frac{{sign(a^{\prime}) \cdot \Delta {E_i}}}{{\sqrt {1 + {{{{b^{\prime 2}}}} \mathord{\left/ {\vphantom {{{{b^{\prime 2}}}} {{{a^{\prime 2}}}}}} \right.} {{{a^{\prime 2}}}}}} }}\\ {{b^{\prime}}_{i\textrm{,new}}} = k \cdot {{a^{\prime}}_{i\textrm{,new}}}\\ k = \frac{{{{a^{\prime}}_i}}}{{{{b^{\prime}}_i}}} \end{array} \right..$$
Then, these target color appearance coordinates for the i-th TCS are again transformed to (Xi,T, Yi,T, Zi,T) tristimulus values. The needed fractions pb, pg and pr, can again be calculated with Eq. (7).

3.2 Results

First the relevant benchmark configuration of a static laser diode illumination system with three narrow spectral peaks is considered. Such a light source can be realized by mixing the light of three different laser diodes and the SPD can also be described by Eq. (1). But, in this case, the peak wavelengths are not chosen to span large color gamut, but are optimized using the Nelder–Mead method in combination with the tools provided in [41], in order to give maximal color fidelity, for a CCT = 5000 K. The resulting peak wavelengths are 459 nm, 531 nm, and 602 nm. The resulting color fidelity score amounts to 74.5, while the gamut score is 103.2. When considering these values in Fig. 3(a), it can be seen that this static SPD does not reach the maximal color gamut score for the obtained color fidelity score.

With these peak wavelengths, the required radiant flux such that the reflected light by a perfect white sample (R(λ) = 1) gives the same tristimulus values as when the sample would be illuminated by the CIE Standard Illuminant D50 (at 1 W), is equal to 0.555W. When comparing the radiant flux requirements of the spatially variable laser diode illumination, both this value and the reference value of 1 W (D50) are both relevant.

In the case of the spatially variable laser illumination with peak wavelengths chosen to allow maximal color gamut (see section 2.2), it is now possible to vary the amount of blue, green and red laser diode light across different positions depending on the object reflectance spectrum at each position. The wide range of object reflectance’s that occur in practice are sampled by the 99 TCS’s of the IES TM-30-2018 metric. For each TCS, the SPD can be calculated such that the resulting color/brightness appearance corresponds with a specific color fidelity score and color gamut score, as explained in the previous section. It is e.g. possible to calculate the SPD’s such that the resulting (L, a′, b′) coordinates of the reflected light by the 99 TCS’s are exactly the same as under the reference illuminant. In that case, the variable laser illumination has a color fidelity score and color gamut score of 100.

The total radiant flux of each SPD for a specific TCS, are shown in Fig. 4(a), as well as the average radiant flux for all 99 TCS’s. This average flux value is clearly lower than the reference value of 1 W which implies that perfect color fidelity can be achieved with a significant reduction of the required radiant flux. When compared to the static laser illumination benchmark, more radiant flux is needed, but one has to keep in mind that this static laser illumination was optimized to reach maximal color fidelity, but it reaches only a fidelity score of 74.5.

 figure: Fig. 4.

Fig. 4. (a) The required radiant flux for all 99 TCS’s to obtain a color fidelity Rf = 100. (b) The variation of the average radiant flux (average for 99 TCS’s) and the color gamut index as a function of Rf. (c) The a'b’ coordinate shifts for all TCS’s when the Rf goes from 100 to 60.

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Similar calculations were now performed for the case in which the SPD’s for each TCS are adapted in order to obtain an Rf going from 100 to 60 in steps of 10, with the corresponding theoretically maximal Rg. The a'b’ coordinate shifts, when Rf changes from 100 to 60, for the 99 TCS’s, are shown in Fig. 4(c). It can be seen that the chroma (colorfulness) of the 99 TCS’s increases, while their hue keeps constant. The variation of the average radiant flux and color gamut index, as a function of Rf, are shown in Fig. 4(b). It is found that the average radiant flux (average for 99 TCS’s) decreases from 0.73 W to 0.68 W and that color gamut index increases from 100 to 158, when the color fidelity index decreases from 100 to 60. This implies that an increase of the color gamut index with the variable laser illumination also results in a reduction of the necessary optical power to yield similar lightness values (even without taking into account that more saturated colors are typically perceived as more bright colors due to the Helmholtz-Kohlrausch effect [42]).

The reason why the average radiant flux decreases with decreasing Rf values can be easily explained by considering the example of a reddish sample, meaning that it has low reflectance for blue and green light and high reflectance for red light. The (red) color saturation of that sample can be enhanced (Rg goes up) by increasing the amount of red light in the illumination, and reducing the amount of blue and green light. Because of the higher reflectance for red light as compared to green and blue, a variation of the red illumination has a bigger impact on the lightness value than a variation of blue and green. So, in order to reach the same lightness value, but a more saturated red color, the red light should be increased be a smaller amount than the green and red light that should be reduced; resulting in a reduction of the total required radiant flux.

4. Experiments

4.1 Experimental results

Experiments were conducted in order to prove the feasibility of tuning the color rendering performance of a spatially variable laser illumination system.

The used test setup is shown in Fig. 5 and consists of a portable laser projector illuminating a Macbeth ColorChecker. The reflected light by a certain patch of the ColorChecker is captured by a spectrometer that is equipped with a direct view telescope. The projected image onto the ColorChecker is calibrated such that each pixel of the projected image is connected to a specific patch of the ColorChecker or the black border in between different patches. The reflectance spectrum of each patch is found by illuminating the ColorChecker with a calibrated light source and measuring the absolute SPD of the reflected light. The SPD’s of the laser projector primaries onto each patch, were characterized as a function of the RGB values of the input image. This allows calculating the necessary RGB values for each pixel in the projected image, such that the color coordinates of the reflected light by each patch correspond with a specific color rendering performance.

 figure: Fig. 5.

Fig. 5. (a) Test set-up in order to demonstrate the feasibility of tuning the color rendering performance of a spatially variable laser illumination system: 1. Macbeth ColorChecker, 2. Laser projector, 3. Laptop, 4. Spectrometer, 5. Spectral telescope. The Macbeth ColorChecker under (b) static laser diode illumination, (c) tuned laser diode illumination for Rf = 100, and (d) tuned laser diode illumination for Rf = 60.

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First, the color tuning capacities of the system were tested for individual color patches. A color rendering performance with Rf = Rg = 100 was targeted, and the RGB values were thus calculated to assure that the SPD of the reflected light by a specific patch gave color coordinates that are “equal” to the color coordinates when the patch is illuminated by the reference illuminant. The term “equal” means in this case that the CIE 1976 chromaticity differences (Δu'v’) and luminance differences (ΔY) between the measured and targeted chromaticity and luminance values, are smaller than the just noticeable color difference (i.e. Δu'v’ < 0.003 [43,44]) and the just noticeable luminance difference (i.e. ΔY/YT < 1% [45] – for YT under 1.9∼51), respectively. The CIE 1976 u'v’ chromaticity coordinates are chosen for this analysis, because it is a very uniform chromaticity diagram for color and luminance differences.

It was noticed however that the targeted chromaticity and luminance values were not reached when using the calculated RGB values for the projected image. This is directly related with the non-linear relationship between the RGB values and the SPD’s of the projector primaries, which changes over time and as a function of temperature. As such, it is very difficult to take this behaviour fully into account. A solution to this problem is offered by including a simple feedback optimization algorithm to the system that optimizes the initial RGB values according to the measured spectrum of the reflected light. In this way it is possible to achieve the targeted color rendering performance. In Fig. 6(a) one can see the result for the green patch of the ColorChecker (row 3, column 2). The initial Δu'v’ value ( = 0.0129) and ΔY/YT value ( = 0.088%) correspond with a noticeable appearance difference. One can see how the Δu'v’ and ΔY/YT (%) values improve after a couple of optimization iterations and stabilize below the just noticeable color and luminance differences. The time in between two successive iterations is mainly determined by the time it takes to capture the SPD of the reflected light with the spectrometer. Similar results were obtained for each of the other color patches and for other color rendering targets (RfRg ≠ 100).

 figure: Fig. 6.

Fig. 6. The Δu'v' and ΔY/YT values (target Rf = 100) as a function of time for (a) the green color patch, when measured with the spectrometer and (b) the orange-yellow color patch, when measured with the hyperspectral camera.

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The experimental setup was then adapted to prove the color tuning efficiency for multiple color patches simultaneously. For this, the spectrometer with direct view telescope was replaced by a hyperspectral camera (GagaField-V10, Sichuan Dualix Spectral Image Technology Co. Ltd, China), which can simultaneously measure the SPD of the reflected light by the 24 patches. Also in this case it was necessary to include the feedback optimization algorithm in order to decrease the initial differences between the measured and targeted chromaticity and luminance values, for all 24 patches at the same time. The variation of the chromaticity and luminance differences over multiple optimization iterations are shown in Fig. 6(b), for the orange-yellow patch of the ColorChecker (row 2, column 6). It is apparent that the feedback optimization algorithm needs significantly more iterations in this case, in order to reach chromaticity and luminance differences below the just noticeable thresholds. This is mainly due to the less accurate and less stable measurements of the reflected SPDs by the hyperspectral camera. This loss in accuracy however is compensated by the smaller integration time that is needed to capture these SPDs.

Photographs of the Macbeth ColorChecker are shown in Fig. 5 for three different cases. In Fig. 5(b) one can see the ColorChecker under homogenous illumination by the laser projector. Similar to the static benchmark case in the theoretical analysis, the SPD of the emitted light is calculated such that the reflected light by a perfect white sample gives the same chromaticity and luminance values, as when the sample would be illuminated by the reference illuminant. This results in a very poor color fidelity score of 25 and a color gamut score of 130; the color appearance is clearly very unnatural. In Fig. 5(c) the color appearance of the scene is shown for the case when the laser projector is optimized to reach perfect color fidelity (Rf =Rg=100). This situation is reached after sufficient feedback optimization cycles. In Fig. 5(d) one can see the color appearance of the scene when the laser projector is optimized to reach a color fidelity score of 60 and an increased color gamut score. The colors of the different patches are saturated and vivid. Depending on the application, the color fidelity could be judged as being acceptable or not.

Finally, the radiant flux requirements of the laser projector were investigated for the 18 chromatic patches of the Macbeth ColorChecker for the case with Rf = 100. Again, these radiant flux requirements can be compared with a radiant flux of 1 W for the reference illuminant D50. The results are shown in Fig. 7(b). It can be seen that the radiant flux requirements with the real projector are on average not below the level of the reference illuminant. The reason that this is not the case for the used laser projector lies in the significant difference between the SPDs of the real laser projector primaries and the theoretical laser diode primaries that are considered in Eq. (1). The measured SPDs of the laser projector (for R, G, B values equal to 125) are shown in Fig. 7(a). It can be seen that apparently two different green and red laser diodes are used in the laser projector, with slightly different peak wavelengths. Some light leakage from the green channel (RGB = [0,125,0]) to the red channel (RGB = [125,0,0]) is also visible. Furthermore, there is a significant difference of the peak wavelengths in this laser projector as compared to the laser diode peak wavelengths that were considered in the theoretical calculations. The used laser projector is therefore not optimal in order to reach minimal radiant flux requirements and also not for having maximum color tuning potential. Indeed, the chromaticity coordinates of the laser projector primaries, are not located on the boundaries of the CIE 1931 chromaticity diagram (see Fig. 7(c)). This results in a more narrow color gamut than in the theoretical case (see Fig. 2(c)).

 figure: Fig. 7.

Fig. 7. (a) The measured spectral power distribution of the laser projector primaries (for R, G, B values equal to 125 separately). (b) Required radiant flux for the 18 chromatic Macbeth color patches, with the real laser projector, and with the considered spatially variable laser diode illumination system with Rf = 100. (c) The color gamut of the laser projector.

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4.2 Experimental methods

The portable laser projector that is used for the experimental results is a PicoBit (Celluon Inc., USA), which offers a resolution of 1280 pixels by 720 pixels. It is used to generate the spatially variable SPD by varying the RGB values of the pixels in the projected image. The incident light of the projector that is reflected by a certain patch of the Macbeth ColorChecker is collected by a direct view telescope (Bentham Instruments Inc., UK), and then measured with a spectrometer QE65 Pro (Ocean Optics Inc., USA).

An important aspect of the experimental set-up was to derive the non-linear relationship between the RGB values of the projected image and the SPD’s SB(λ), SG(λ) and SR(λ) of the three primaries of the laser projector onto each patch. This was done by measuring the reflected light from the projector by each patch with the wavelength and radiometric calibrated spectrometer, and taking the measured reflectance spectra of the different patches into account. The knowledge of SB(λ), SG(λ) and SR(λ) onto each patch for maximal RGB values, allows to calculate the required laser diode ratio’s pb, pg and pr for each patch such that the desired color rendering performance is reached. These ratio’s correspond with certain RGB values, and the knowledge of SB(λ), SG(λ) and SR(λ) for those specific RGB values can be used to update pb, pg and pr for each patch. A couple of these iterations help to accommodate for the non-linear dependence between the RGB values and projector primaries SPD’s, but the resulting RGB values were still not sufficiently accurate. Therefore, a feedback optimization algorithm was included.

A continuous conditional loop is used that relies on the measurement of the reflected light with the spectrometer or hyperspectral camera. From the measured SPD, the luminance and chromaticity values Y and (u′, v′) are calculated and compared with the target values. If the difference between measured and target luminance values is not below the just noticeable luminance differences (i.e. ΔY/YT < 1% [45] – for YT under 1.9∼51), the RGB values are increased or decreased by 1, simultaneously. Additionally, only R values are adapted if the difference between measured and target u’ values are above the just noticeable color difference (i.e. Δu'v’ < 0.003 [43,44]), and R values are adapted if the difference between measured and target u’ values are above the just noticeable color difference.

5. Conclusion and discussion

This paper investigates the color tuning potential and related energy requirements of a spatially variable laser illumination system, in view of the new IES TM-30-2018 method for color rendering. It is explained how such a system allows to reach the maximal color gamut score that is theoretically allowed by the corresponding chosen color fidelity score. Calculations of the optical energy requirements reveal that the needed radiant flux with such a system is below that of the chosen reference illuminant, while it realizes exactly the same color appearance. Energy requirements are further reduced when higher color gamut scores are realized. An experimental setup in which a hyperspectral camera is combined with a commercial laser projector demonstrates that accurate color tuning is also feasible in practice, at least for simple illuminated scenes and when real-time feedback is included.

However, a few relevant remarks are certainly in order. A first important remark is related with the energy saving potential of the system. It is clear that the energy consumption of a lighting system is not only determined by the emitted radiant flux but also by the radiant efficiency. As mentioned in the introduction, the theoretical results focus only on the optical power requirements of the illumination. However, since this study assumes explicitly the use of direct laser diodes as light sources, it is certainly required to point to the fact that the radiant efficiency of direct laser diodes is currently well below 50% for emission in the visible domain. Similar to LEDs, the radiant efficiency of green-emitting laser diodes is even below that of blue or red-emitting devices [46], reaching efficiencies of “only” 15% [47]. But direct laser diode technology is maturing fast and radiant efficiencies are improving continuously. Apart from the radiant efficiency also the system efficiency of the point-by-point light projection system should be taken into account. Spatial light modulators that redirect light such as phase-only elements or scanning mirrors, are from an efficiency point-of-view, superior to devices that block light. These technologies are currently not yet ready to be used as lighting systems, but that could change in the near future.

This means that from a practical point of view, saving energy is clearly not the main reason for considering a spatially variable laser-based illumination system, at this moment in time. Its current potential lies in allowing a color rendering performance that is not achievable with other illumination systems. Already at this stage, one could imagine using a laser projector for shop or museum lighting in order to tailor the color appearance of a certain scene in a very flexible and dynamic manner. Apart from reducing energy consumption or tuning color, optimizing the local SPD with a spatially variable illumination system can help to minimize the absorption of light by sensitive objects such as artwork [48] or enhancing contrast in the illuminated scene.

The fact that some time is needed to evolve the system towards the desired color performance is not a real hindrance for such applications. The cost of a hyperspectral camera however might be an issue, and for that reason it could be very interesting to investigate the possibility of spectral estimation algorithms with a common RGB camera. In order to have real-time color tuning of dynamic scenes, further research is certainly needed. There is room for improvement on both the hardware side (faster camera capture) and software side (faster feedback optimization algorithms). But in order to realize a real-time system one should also provide an answer on how to combine the object reflectance spectra measurements (needing a calibrated broadband illumination) and the adaptive laser illumination.

A final topic that warrants further research is the impact of the camera position relative to the observer position. In our study it is assumed that the monitored object reflectance spectra correspond with the reflectances of the laser diode light towards the observer. This assumption is certainly not always valid for many materials (e.g. glossy materials), if there is a relative large angle between the observation direction and camera monitoring direction. This effect can further complicate the already difficult alignment issue of the pixels of the camera system, with the “pixels” of the point-by-point illumination system.

Funding

National Natural Science Foundation of China (61604135); China Scholarship Council (201706415021); Fundamental Research Funds for the Central Universities (CUGL180404).

Acknowledgments

The work was carried out at ESAT/Light & Lighting Laboratory, KU Leuven, Ghent, Belgium. We thank Prof. Peter Hanselear for authorizing the use of equipment at the lab, and Dr. Jan Audenaert and Shining Ma for help with the experiments.

References

1. J. H. Oh, S. J. Yang, and Y. R. Do, “Healthy, natural, efficient and tunable lighting: four-package white LEDs for optimizing the circadian effect, color quality and vision performance,” Light: Sci. Appl. 3(2), e141 (2014). [CrossRef]  

2. W. Davis and Y. Ohno, “Color quality scale,” Opt. Eng. 49(3), 033602 (2010). [CrossRef]  

3. K. A. G. Smet, W. R. Ryckaert, M. R. Pointer, G. Deconinck, and P. Hanselaer, “A memory colour quality metric for white light sources,” Energy. Buildings. 49, 216–225 (2012). [CrossRef]  

4. K. Houser, M. Mossman, K. Smet, and L. Whitehead, “Tutorial: Color rendering and its applications in lighting,” Leukos 12(1–2), 7–26 (2016). [CrossRef]  

5. K. A. G. Smet and P. Hanselaer, “Memory and preferred colours and the colour rendition of white light sources,” Lighting Res. Technol. 48(4), 393–411 (2016). [CrossRef]  

6. K. A. G. Smet, J. Schanda, L. Whitehead, and R. M. Luo, “CRI2012: A proposal for updating the CIE colour rendering index,” Lighting Res. Technol. 45(6), 689–709 (2013). [CrossRef]  

7. K. A. G. Smet, L. Whitehead, J. Schanda, and R. M. Luo, “Toward a replacement of the CIE color rendering index for white light sources,” Leukos 12(1–2), 61–69 (2016). [CrossRef]  

8. A. David, “Color fidelity of light sources evaluated over large sets of reflectance samples,” Leukos 10(2), 59–75 (2014). [CrossRef]  

9. D. Durmus and W. Davis, “Optimising light source spectrum for object reflectance,” Opt. Express 23(11), A456–A464 (2015). [CrossRef]  

10. J. Zhang, R. Hu, B. Xie, X. Yu, X. Luo, Z. Yu, L. Zhang, H. Wang, and X. Jin, “Energy-saving light source spectrum optimization by considering object’s reflectance,” IEEE Photonics J. 9(2), 1–11 (2017). [CrossRef]  

11. D. Durmus and W. Davis, “Appearance of achromatic colors under optimized light source spectrum,” IEEE Photonics J. 10(6), 1–11 (2018). [CrossRef]  

12. D. Durmus and W. Davis, “Object color naturalness and attractiveness with spectrally optimized illumination,” Opt. Express 25(11), 12839–12850 (2017). [CrossRef]  

13. F. David and A. Kinjiro, “Hyperspectral imaging in color vision research: tutorial,” J. Opt. Soc. Am. A 36(4), 606–627 (2019). [CrossRef]  

14. J. Y. Tsao, M. H. Crawford, M. E. Coltrin, A. J. Fischer, D. D. Koleske, G. S. Subramania, G. T. Wang, J. J. Wierer, and R. F. Karlicek, “Toward smart and ultra-efficient solid-state lighting,” Adv. Opt. Mater. 2(9), 809–836 (2014). [CrossRef]  

15. D. Durmus and W. Davis, “Blur perception and visual clarity in light projection systems,” Opt. Express 27(4), A216–A223 (2019). [CrossRef]  

16. Panasonic Corp. “What's a space player?” https://panasonic.net/cns/projector/products/spaceplayer.

17. M. S. Brennesholtz and E. H. Stupp, Projection displays (Wiley Publishing, 2008).

18. G. Damberg and W. Heidrich, “Efficient freeform lens optimization for computational caustic displays,” Opt. Express 23(8), 10224–10232 (2015). [CrossRef]  

19. W. F. Hsu and M. H. Weng, “Compact holographic projection display using liquid-crystal-on-Silicon spatial light modulator,” Materials 9(9), 768–776 (2016). [CrossRef]  

20. M. Bawart, S. Bernet, and M. Ritsch-Marte, “Programmable freeform optical elements,” Opt. Express 25(5), 4898–4906 (2017). [CrossRef]  

21. M. Freeman, M. Champion, and S. Madhavan, “Scanned laser pico-projectors: Seeing the bg picture (with a small device),” Opt. Photonics News 20(5), 28–34 (2009). [CrossRef]  

22. J. Shen, S. Chang, H. Wang, and Z. Zheng, “Optimal illumination for visual enhancement based on color entropy evaluation,” Opt. Express 24(17), 19788–19800 (2016). [CrossRef]  

23. C. Chi, H. Yoo, and M. Ben-Ezra, “Multi-spectral imaging by optimized wide band illumination,” Int. J. Comput. Vis. 86(2–3), 140–151 (2010). [CrossRef]  

24. S. Han, I. Sato, T. Okabe, and Y. Sato, “Fast spectral reflectance recovery using DLP projector,” Int. J. Comput. Vis. 110(2), 172–184 (2014). [CrossRef]  

25. S. W. Oh, M. S. Brown, M. Pollefeys, and S. J. Kim, “Do it yourself hyperspectral imaging with everyday digital cameras,” in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (IEEE, 2016), pp. 2461–2469.

26. Z. Liu, Q. Liu, G. A. Gao, and C. Li, “Optimized spectral reconstruction based on adaptive training set selection,” Opt. Express 25(11), 12435–12445 (2017). [CrossRef]  

27. BT2020, I. T. U. R. “Parameter values for ultra-high definition television systems for production and international programme exchange,” (2012)

28. M. R. Pointer, “The gamut of real surface colours,” Color Res. Appl. 5(3), 145–155 (1980). [CrossRef]  

29. Y. Ohno, “Spectral design considerations for white LED color rendering,” Opt. Eng. 44(11), 111302 (2005). [CrossRef]  

30. Illuminating engineering society of north America, “TM-30-18: IES method for evaluating light source color rendition,” (2018).

31. K. A. G. Smet, A. David, and L. Whitehead, “Why color space uniformity and sample set spectral uniformity are essential for color rendering measures,” Leukos 12(1–2), 39–50 (2016). [CrossRef]  

32. A. David, P. T. Fini, K. W. Houser, Y. Ohno, M. P. Royer, K. A. G. Smet, W. Minchen, and L. Whitehead, “Development of the IES method for evaluating the color rendition of light sources,” Opt. Express 23(12), 15888–11590 (2015). [CrossRef]  

33. M. R. Luo, G. Cui, and C. Li, “Uniform colour spaces based on CIECAM02 colour appearance model,” Color Res. Appl. 31(4), 320–330 (2006). [CrossRef]  

34. T. Esposito and K. Houser, “Models of colour quality over a wide range of spectral power distributions,” Lighting Res. Technol. 51(3), 331–352 (2019). [CrossRef]  

35. X. Feng, W. Xu, Q. Han, and S. Zhang, “Colour-enhanced light emitting diode light with high gamut area for retail lighting,” Lighting Res. Technol. 49(3), 329–342 (2017). [CrossRef]  

36. K. A. G. Smet, W. R. Ryckaert, M. R. Pointer, G. Deconinck, and P. Hanselaer, “Memory colours and colour quality evaluation of conventional and solid-state lamps,” Opt. Express 18(25), 26229–26244 (2010). [CrossRef]  

37. M. Wei, K. W. Houser, A. David, and M. R. Krames, “Perceptual responses to LED illumination with colour rendering indices of 85 and 97,” Lighting Res. Technol. 47(7), 810–827 (2015). [CrossRef]  

38. Y. Ohno, M. Fein, and C. Miller, “Vision experiment on chroma saturation for colour quality preference,” Light Eng. 23(4), 6–14 (2015).

39. M. P. Royer, A. Wilkerson, and M. Wei, “Human perceptions of colour rendition at different chromaticities,” Lighting Res. Technol. 50(7), 965–994 (2018). [CrossRef]  

40. M. Royer, A. Wilkerson, M. Wei, K. Houser, and R. Davis, “Human perceptions of colour rendition vary with average fidelity, average gamut, and gamut shape,” Lighting Res. Technol. 49(8), 966–991 (2017). [CrossRef]  

41. K. A. G. Smet, “Tutorial: The LuxPy Python toolbox for lighting and color science,” Leukos. (2019). [CrossRef]  

42. S. Hermans, K. A. G. Smet, and P. Hanselaer, “Color appearance model for self-luminous stimuli,” J. Opt. Soc. Am. A 35(12), 2000–2009 (2018). [CrossRef]  

43. M. Luo, G. Cui, and M. Georgoula, “Colour difference evaluation for white light sources,” Lighting Res. Technol. 47(3), 360–369 (2015). [CrossRef]  

44. Commission internationale de l'éclairage, “CIE Technical Note 001:2014 Chromaticity difference specification for light sources,” (2014).

45. G. Wyszecki and W. S. Stiles, Color science: concepts and methods, quantitative data and formulas, 2nd edition (John Wiley & Sons. Press, 2000).

46. D. Sizov, R. Bhat, and C. E. Zah, “Gallium indium Nitride-based green lasers,” J. Lightwave Technol. 30(5), 679–699 (2012). [CrossRef]  

47. Nichia Corp, “Laser diode,” https://www.nichia.co.jp/en/product/laser.html (2019).

48. D. Durmus, D. Abdalla, A. Duis, and W. Davis, “Spectral optimization to minimize light absorbed by artwork,” Leukos. (2018).

References

  • View by:

  1. J. H. Oh, S. J. Yang, and Y. R. Do, “Healthy, natural, efficient and tunable lighting: four-package white LEDs for optimizing the circadian effect, color quality and vision performance,” Light: Sci. Appl. 3(2), e141 (2014).
    [Crossref]
  2. W. Davis and Y. Ohno, “Color quality scale,” Opt. Eng. 49(3), 033602 (2010).
    [Crossref]
  3. K. A. G. Smet, W. R. Ryckaert, M. R. Pointer, G. Deconinck, and P. Hanselaer, “A memory colour quality metric for white light sources,” Energy. Buildings. 49, 216–225 (2012).
    [Crossref]
  4. K. Houser, M. Mossman, K. Smet, and L. Whitehead, “Tutorial: Color rendering and its applications in lighting,” Leukos 12(1–2), 7–26 (2016).
    [Crossref]
  5. K. A. G. Smet and P. Hanselaer, “Memory and preferred colours and the colour rendition of white light sources,” Lighting Res. Technol. 48(4), 393–411 (2016).
    [Crossref]
  6. K. A. G. Smet, J. Schanda, L. Whitehead, and R. M. Luo, “CRI2012: A proposal for updating the CIE colour rendering index,” Lighting Res. Technol. 45(6), 689–709 (2013).
    [Crossref]
  7. K. A. G. Smet, L. Whitehead, J. Schanda, and R. M. Luo, “Toward a replacement of the CIE color rendering index for white light sources,” Leukos 12(1–2), 61–69 (2016).
    [Crossref]
  8. A. David, “Color fidelity of light sources evaluated over large sets of reflectance samples,” Leukos 10(2), 59–75 (2014).
    [Crossref]
  9. D. Durmus and W. Davis, “Optimising light source spectrum for object reflectance,” Opt. Express 23(11), A456–A464 (2015).
    [Crossref]
  10. J. Zhang, R. Hu, B. Xie, X. Yu, X. Luo, Z. Yu, L. Zhang, H. Wang, and X. Jin, “Energy-saving light source spectrum optimization by considering object’s reflectance,” IEEE Photonics J. 9(2), 1–11 (2017).
    [Crossref]
  11. D. Durmus and W. Davis, “Appearance of achromatic colors under optimized light source spectrum,” IEEE Photonics J. 10(6), 1–11 (2018).
    [Crossref]
  12. D. Durmus and W. Davis, “Object color naturalness and attractiveness with spectrally optimized illumination,” Opt. Express 25(11), 12839–12850 (2017).
    [Crossref]
  13. F. David and A. Kinjiro, “Hyperspectral imaging in color vision research: tutorial,” J. Opt. Soc. Am. A 36(4), 606–627 (2019).
    [Crossref]
  14. J. Y. Tsao, M. H. Crawford, M. E. Coltrin, A. J. Fischer, D. D. Koleske, G. S. Subramania, G. T. Wang, J. J. Wierer, and R. F. Karlicek, “Toward smart and ultra-efficient solid-state lighting,” Adv. Opt. Mater. 2(9), 809–836 (2014).
    [Crossref]
  15. D. Durmus and W. Davis, “Blur perception and visual clarity in light projection systems,” Opt. Express 27(4), A216–A223 (2019).
    [Crossref]
  16. Panasonic Corp. “What's a space player?” https://panasonic.net/cns/projector/products/spaceplayer .
  17. M. S. Brennesholtz and E. H. Stupp, Projection displays (Wiley Publishing, 2008).
  18. G. Damberg and W. Heidrich, “Efficient freeform lens optimization for computational caustic displays,” Opt. Express 23(8), 10224–10232 (2015).
    [Crossref]
  19. W. F. Hsu and M. H. Weng, “Compact holographic projection display using liquid-crystal-on-Silicon spatial light modulator,” Materials 9(9), 768–776 (2016).
    [Crossref]
  20. M. Bawart, S. Bernet, and M. Ritsch-Marte, “Programmable freeform optical elements,” Opt. Express 25(5), 4898–4906 (2017).
    [Crossref]
  21. M. Freeman, M. Champion, and S. Madhavan, “Scanned laser pico-projectors: Seeing the bg picture (with a small device),” Opt. Photonics News 20(5), 28–34 (2009).
    [Crossref]
  22. J. Shen, S. Chang, H. Wang, and Z. Zheng, “Optimal illumination for visual enhancement based on color entropy evaluation,” Opt. Express 24(17), 19788–19800 (2016).
    [Crossref]
  23. C. Chi, H. Yoo, and M. Ben-Ezra, “Multi-spectral imaging by optimized wide band illumination,” Int. J. Comput. Vis. 86(2–3), 140–151 (2010).
    [Crossref]
  24. S. Han, I. Sato, T. Okabe, and Y. Sato, “Fast spectral reflectance recovery using DLP projector,” Int. J. Comput. Vis. 110(2), 172–184 (2014).
    [Crossref]
  25. S. W. Oh, M. S. Brown, M. Pollefeys, and S. J. Kim, “Do it yourself hyperspectral imaging with everyday digital cameras,” in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (IEEE, 2016), pp. 2461–2469.
  26. Z. Liu, Q. Liu, G. A. Gao, and C. Li, “Optimized spectral reconstruction based on adaptive training set selection,” Opt. Express 25(11), 12435–12445 (2017).
    [Crossref]
  27. BT2020, I. T. U. R. “Parameter values for ultra-high definition television systems for production and international programme exchange,” (2012)
  28. M. R. Pointer, “The gamut of real surface colours,” Color Res. Appl. 5(3), 145–155 (1980).
    [Crossref]
  29. Y. Ohno, “Spectral design considerations for white LED color rendering,” Opt. Eng. 44(11), 111302 (2005).
    [Crossref]
  30. Illuminating engineering society of north America, “TM-30-18: IES method for evaluating light source color rendition,” (2018).
  31. K. A. G. Smet, A. David, and L. Whitehead, “Why color space uniformity and sample set spectral uniformity are essential for color rendering measures,” Leukos 12(1–2), 39–50 (2016).
    [Crossref]
  32. A. David, P. T. Fini, K. W. Houser, Y. Ohno, M. P. Royer, K. A. G. Smet, W. Minchen, and L. Whitehead, “Development of the IES method for evaluating the color rendition of light sources,” Opt. Express 23(12), 15888–11590 (2015).
    [Crossref]
  33. M. R. Luo, G. Cui, and C. Li, “Uniform colour spaces based on CIECAM02 colour appearance model,” Color Res. Appl. 31(4), 320–330 (2006).
    [Crossref]
  34. T. Esposito and K. Houser, “Models of colour quality over a wide range of spectral power distributions,” Lighting Res. Technol. 51(3), 331–352 (2019).
    [Crossref]
  35. X. Feng, W. Xu, Q. Han, and S. Zhang, “Colour-enhanced light emitting diode light with high gamut area for retail lighting,” Lighting Res. Technol. 49(3), 329–342 (2017).
    [Crossref]
  36. K. A. G. Smet, W. R. Ryckaert, M. R. Pointer, G. Deconinck, and P. Hanselaer, “Memory colours and colour quality evaluation of conventional and solid-state lamps,” Opt. Express 18(25), 26229–26244 (2010).
    [Crossref]
  37. M. Wei, K. W. Houser, A. David, and M. R. Krames, “Perceptual responses to LED illumination with colour rendering indices of 85 and 97,” Lighting Res. Technol. 47(7), 810–827 (2015).
    [Crossref]
  38. Y. Ohno, M. Fein, and C. Miller, “Vision experiment on chroma saturation for colour quality preference,” Light Eng. 23(4), 6–14 (2015).
  39. M. P. Royer, A. Wilkerson, and M. Wei, “Human perceptions of colour rendition at different chromaticities,” Lighting Res. Technol. 50(7), 965–994 (2018).
    [Crossref]
  40. M. Royer, A. Wilkerson, M. Wei, K. Houser, and R. Davis, “Human perceptions of colour rendition vary with average fidelity, average gamut, and gamut shape,” Lighting Res. Technol. 49(8), 966–991 (2017).
    [Crossref]
  41. K. A. G. Smet, “Tutorial: The LuxPy Python toolbox for lighting and color science,” Leukos. (2019).
    [Crossref]
  42. S. Hermans, K. A. G. Smet, and P. Hanselaer, “Color appearance model for self-luminous stimuli,” J. Opt. Soc. Am. A 35(12), 2000–2009 (2018).
    [Crossref]
  43. M. Luo, G. Cui, and M. Georgoula, “Colour difference evaluation for white light sources,” Lighting Res. Technol. 47(3), 360–369 (2015).
    [Crossref]
  44. Commission internationale de l'éclairage, “CIE Technical Note 001:2014 Chromaticity difference specification for light sources,” (2014).
  45. G. Wyszecki and W. S. Stiles, Color science: concepts and methods, quantitative data and formulas, 2nd edition (John Wiley & Sons. Press, 2000).
  46. D. Sizov, R. Bhat, and C. E. Zah, “Gallium indium Nitride-based green lasers,” J. Lightwave Technol. 30(5), 679–699 (2012).
    [Crossref]
  47. Nichia Corp, “Laser diode,” https://www.nichia.co.jp/en/product/laser.html (2019).
  48. D. Durmus, D. Abdalla, A. Duis, and W. Davis, “Spectral optimization to minimize light absorbed by artwork,” Leukos. (2018).

2019 (3)

2018 (3)

M. P. Royer, A. Wilkerson, and M. Wei, “Human perceptions of colour rendition at different chromaticities,” Lighting Res. Technol. 50(7), 965–994 (2018).
[Crossref]

D. Durmus and W. Davis, “Appearance of achromatic colors under optimized light source spectrum,” IEEE Photonics J. 10(6), 1–11 (2018).
[Crossref]

S. Hermans, K. A. G. Smet, and P. Hanselaer, “Color appearance model for self-luminous stimuli,” J. Opt. Soc. Am. A 35(12), 2000–2009 (2018).
[Crossref]

2017 (6)

D. Durmus and W. Davis, “Object color naturalness and attractiveness with spectrally optimized illumination,” Opt. Express 25(11), 12839–12850 (2017).
[Crossref]

J. Zhang, R. Hu, B. Xie, X. Yu, X. Luo, Z. Yu, L. Zhang, H. Wang, and X. Jin, “Energy-saving light source spectrum optimization by considering object’s reflectance,” IEEE Photonics J. 9(2), 1–11 (2017).
[Crossref]

M. Royer, A. Wilkerson, M. Wei, K. Houser, and R. Davis, “Human perceptions of colour rendition vary with average fidelity, average gamut, and gamut shape,” Lighting Res. Technol. 49(8), 966–991 (2017).
[Crossref]

X. Feng, W. Xu, Q. Han, and S. Zhang, “Colour-enhanced light emitting diode light with high gamut area for retail lighting,” Lighting Res. Technol. 49(3), 329–342 (2017).
[Crossref]

M. Bawart, S. Bernet, and M. Ritsch-Marte, “Programmable freeform optical elements,” Opt. Express 25(5), 4898–4906 (2017).
[Crossref]

Z. Liu, Q. Liu, G. A. Gao, and C. Li, “Optimized spectral reconstruction based on adaptive training set selection,” Opt. Express 25(11), 12435–12445 (2017).
[Crossref]

2016 (6)

J. Shen, S. Chang, H. Wang, and Z. Zheng, “Optimal illumination for visual enhancement based on color entropy evaluation,” Opt. Express 24(17), 19788–19800 (2016).
[Crossref]

K. A. G. Smet, A. David, and L. Whitehead, “Why color space uniformity and sample set spectral uniformity are essential for color rendering measures,” Leukos 12(1–2), 39–50 (2016).
[Crossref]

W. F. Hsu and M. H. Weng, “Compact holographic projection display using liquid-crystal-on-Silicon spatial light modulator,” Materials 9(9), 768–776 (2016).
[Crossref]

K. Houser, M. Mossman, K. Smet, and L. Whitehead, “Tutorial: Color rendering and its applications in lighting,” Leukos 12(1–2), 7–26 (2016).
[Crossref]

K. A. G. Smet and P. Hanselaer, “Memory and preferred colours and the colour rendition of white light sources,” Lighting Res. Technol. 48(4), 393–411 (2016).
[Crossref]

K. A. G. Smet, L. Whitehead, J. Schanda, and R. M. Luo, “Toward a replacement of the CIE color rendering index for white light sources,” Leukos 12(1–2), 61–69 (2016).
[Crossref]

2015 (6)

D. Durmus and W. Davis, “Optimising light source spectrum for object reflectance,” Opt. Express 23(11), A456–A464 (2015).
[Crossref]

G. Damberg and W. Heidrich, “Efficient freeform lens optimization for computational caustic displays,” Opt. Express 23(8), 10224–10232 (2015).
[Crossref]

A. David, P. T. Fini, K. W. Houser, Y. Ohno, M. P. Royer, K. A. G. Smet, W. Minchen, and L. Whitehead, “Development of the IES method for evaluating the color rendition of light sources,” Opt. Express 23(12), 15888–11590 (2015).
[Crossref]

M. Wei, K. W. Houser, A. David, and M. R. Krames, “Perceptual responses to LED illumination with colour rendering indices of 85 and 97,” Lighting Res. Technol. 47(7), 810–827 (2015).
[Crossref]

Y. Ohno, M. Fein, and C. Miller, “Vision experiment on chroma saturation for colour quality preference,” Light Eng. 23(4), 6–14 (2015).

M. Luo, G. Cui, and M. Georgoula, “Colour difference evaluation for white light sources,” Lighting Res. Technol. 47(3), 360–369 (2015).
[Crossref]

2014 (4)

S. Han, I. Sato, T. Okabe, and Y. Sato, “Fast spectral reflectance recovery using DLP projector,” Int. J. Comput. Vis. 110(2), 172–184 (2014).
[Crossref]

J. Y. Tsao, M. H. Crawford, M. E. Coltrin, A. J. Fischer, D. D. Koleske, G. S. Subramania, G. T. Wang, J. J. Wierer, and R. F. Karlicek, “Toward smart and ultra-efficient solid-state lighting,” Adv. Opt. Mater. 2(9), 809–836 (2014).
[Crossref]

A. David, “Color fidelity of light sources evaluated over large sets of reflectance samples,” Leukos 10(2), 59–75 (2014).
[Crossref]

J. H. Oh, S. J. Yang, and Y. R. Do, “Healthy, natural, efficient and tunable lighting: four-package white LEDs for optimizing the circadian effect, color quality and vision performance,” Light: Sci. Appl. 3(2), e141 (2014).
[Crossref]

2013 (1)

K. A. G. Smet, J. Schanda, L. Whitehead, and R. M. Luo, “CRI2012: A proposal for updating the CIE colour rendering index,” Lighting Res. Technol. 45(6), 689–709 (2013).
[Crossref]

2012 (2)

K. A. G. Smet, W. R. Ryckaert, M. R. Pointer, G. Deconinck, and P. Hanselaer, “A memory colour quality metric for white light sources,” Energy. Buildings. 49, 216–225 (2012).
[Crossref]

D. Sizov, R. Bhat, and C. E. Zah, “Gallium indium Nitride-based green lasers,” J. Lightwave Technol. 30(5), 679–699 (2012).
[Crossref]

2010 (3)

W. Davis and Y. Ohno, “Color quality scale,” Opt. Eng. 49(3), 033602 (2010).
[Crossref]

C. Chi, H. Yoo, and M. Ben-Ezra, “Multi-spectral imaging by optimized wide band illumination,” Int. J. Comput. Vis. 86(2–3), 140–151 (2010).
[Crossref]

K. A. G. Smet, W. R. Ryckaert, M. R. Pointer, G. Deconinck, and P. Hanselaer, “Memory colours and colour quality evaluation of conventional and solid-state lamps,” Opt. Express 18(25), 26229–26244 (2010).
[Crossref]

2009 (1)

M. Freeman, M. Champion, and S. Madhavan, “Scanned laser pico-projectors: Seeing the bg picture (with a small device),” Opt. Photonics News 20(5), 28–34 (2009).
[Crossref]

2006 (1)

M. R. Luo, G. Cui, and C. Li, “Uniform colour spaces based on CIECAM02 colour appearance model,” Color Res. Appl. 31(4), 320–330 (2006).
[Crossref]

2005 (1)

Y. Ohno, “Spectral design considerations for white LED color rendering,” Opt. Eng. 44(11), 111302 (2005).
[Crossref]

1980 (1)

M. R. Pointer, “The gamut of real surface colours,” Color Res. Appl. 5(3), 145–155 (1980).
[Crossref]

Abdalla, D.

D. Durmus, D. Abdalla, A. Duis, and W. Davis, “Spectral optimization to minimize light absorbed by artwork,” Leukos. (2018).

Bawart, M.

Ben-Ezra, M.

C. Chi, H. Yoo, and M. Ben-Ezra, “Multi-spectral imaging by optimized wide band illumination,” Int. J. Comput. Vis. 86(2–3), 140–151 (2010).
[Crossref]

Bernet, S.

Bhat, R.

Brennesholtz, M. S.

M. S. Brennesholtz and E. H. Stupp, Projection displays (Wiley Publishing, 2008).

Brown, M. S.

S. W. Oh, M. S. Brown, M. Pollefeys, and S. J. Kim, “Do it yourself hyperspectral imaging with everyday digital cameras,” in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (IEEE, 2016), pp. 2461–2469.

Champion, M.

M. Freeman, M. Champion, and S. Madhavan, “Scanned laser pico-projectors: Seeing the bg picture (with a small device),” Opt. Photonics News 20(5), 28–34 (2009).
[Crossref]

Chang, S.

Chi, C.

C. Chi, H. Yoo, and M. Ben-Ezra, “Multi-spectral imaging by optimized wide band illumination,” Int. J. Comput. Vis. 86(2–3), 140–151 (2010).
[Crossref]

Coltrin, M. E.

J. Y. Tsao, M. H. Crawford, M. E. Coltrin, A. J. Fischer, D. D. Koleske, G. S. Subramania, G. T. Wang, J. J. Wierer, and R. F. Karlicek, “Toward smart and ultra-efficient solid-state lighting,” Adv. Opt. Mater. 2(9), 809–836 (2014).
[Crossref]

Corp, Nichia

Nichia Corp, “Laser diode,” https://www.nichia.co.jp/en/product/laser.html (2019).

Crawford, M. H.

J. Y. Tsao, M. H. Crawford, M. E. Coltrin, A. J. Fischer, D. D. Koleske, G. S. Subramania, G. T. Wang, J. J. Wierer, and R. F. Karlicek, “Toward smart and ultra-efficient solid-state lighting,” Adv. Opt. Mater. 2(9), 809–836 (2014).
[Crossref]

Cui, G.

M. Luo, G. Cui, and M. Georgoula, “Colour difference evaluation for white light sources,” Lighting Res. Technol. 47(3), 360–369 (2015).
[Crossref]

M. R. Luo, G. Cui, and C. Li, “Uniform colour spaces based on CIECAM02 colour appearance model,” Color Res. Appl. 31(4), 320–330 (2006).
[Crossref]

Damberg, G.

David, A.

K. A. G. Smet, A. David, and L. Whitehead, “Why color space uniformity and sample set spectral uniformity are essential for color rendering measures,” Leukos 12(1–2), 39–50 (2016).
[Crossref]

A. David, P. T. Fini, K. W. Houser, Y. Ohno, M. P. Royer, K. A. G. Smet, W. Minchen, and L. Whitehead, “Development of the IES method for evaluating the color rendition of light sources,” Opt. Express 23(12), 15888–11590 (2015).
[Crossref]

M. Wei, K. W. Houser, A. David, and M. R. Krames, “Perceptual responses to LED illumination with colour rendering indices of 85 and 97,” Lighting Res. Technol. 47(7), 810–827 (2015).
[Crossref]

A. David, “Color fidelity of light sources evaluated over large sets of reflectance samples,” Leukos 10(2), 59–75 (2014).
[Crossref]

David, F.

Davis, R.

M. Royer, A. Wilkerson, M. Wei, K. Houser, and R. Davis, “Human perceptions of colour rendition vary with average fidelity, average gamut, and gamut shape,” Lighting Res. Technol. 49(8), 966–991 (2017).
[Crossref]

Davis, W.

D. Durmus and W. Davis, “Blur perception and visual clarity in light projection systems,” Opt. Express 27(4), A216–A223 (2019).
[Crossref]

D. Durmus and W. Davis, “Appearance of achromatic colors under optimized light source spectrum,” IEEE Photonics J. 10(6), 1–11 (2018).
[Crossref]

D. Durmus and W. Davis, “Object color naturalness and attractiveness with spectrally optimized illumination,” Opt. Express 25(11), 12839–12850 (2017).
[Crossref]

D. Durmus and W. Davis, “Optimising light source spectrum for object reflectance,” Opt. Express 23(11), A456–A464 (2015).
[Crossref]

W. Davis and Y. Ohno, “Color quality scale,” Opt. Eng. 49(3), 033602 (2010).
[Crossref]

D. Durmus, D. Abdalla, A. Duis, and W. Davis, “Spectral optimization to minimize light absorbed by artwork,” Leukos. (2018).

Deconinck, G.

K. A. G. Smet, W. R. Ryckaert, M. R. Pointer, G. Deconinck, and P. Hanselaer, “A memory colour quality metric for white light sources,” Energy. Buildings. 49, 216–225 (2012).
[Crossref]

K. A. G. Smet, W. R. Ryckaert, M. R. Pointer, G. Deconinck, and P. Hanselaer, “Memory colours and colour quality evaluation of conventional and solid-state lamps,” Opt. Express 18(25), 26229–26244 (2010).
[Crossref]

Do, Y. R.

J. H. Oh, S. J. Yang, and Y. R. Do, “Healthy, natural, efficient and tunable lighting: four-package white LEDs for optimizing the circadian effect, color quality and vision performance,” Light: Sci. Appl. 3(2), e141 (2014).
[Crossref]

Duis, A.

D. Durmus, D. Abdalla, A. Duis, and W. Davis, “Spectral optimization to minimize light absorbed by artwork,” Leukos. (2018).

Durmus, D.

Esposito, T.

T. Esposito and K. Houser, “Models of colour quality over a wide range of spectral power distributions,” Lighting Res. Technol. 51(3), 331–352 (2019).
[Crossref]

Fein, M.

Y. Ohno, M. Fein, and C. Miller, “Vision experiment on chroma saturation for colour quality preference,” Light Eng. 23(4), 6–14 (2015).

Feng, X.

X. Feng, W. Xu, Q. Han, and S. Zhang, “Colour-enhanced light emitting diode light with high gamut area for retail lighting,” Lighting Res. Technol. 49(3), 329–342 (2017).
[Crossref]

Fini, P. T.

Fischer, A. J.

J. Y. Tsao, M. H. Crawford, M. E. Coltrin, A. J. Fischer, D. D. Koleske, G. S. Subramania, G. T. Wang, J. J. Wierer, and R. F. Karlicek, “Toward smart and ultra-efficient solid-state lighting,” Adv. Opt. Mater. 2(9), 809–836 (2014).
[Crossref]

Freeman, M.

M. Freeman, M. Champion, and S. Madhavan, “Scanned laser pico-projectors: Seeing the bg picture (with a small device),” Opt. Photonics News 20(5), 28–34 (2009).
[Crossref]

Gao, G. A.

Georgoula, M.

M. Luo, G. Cui, and M. Georgoula, “Colour difference evaluation for white light sources,” Lighting Res. Technol. 47(3), 360–369 (2015).
[Crossref]

Han, Q.

X. Feng, W. Xu, Q. Han, and S. Zhang, “Colour-enhanced light emitting diode light with high gamut area for retail lighting,” Lighting Res. Technol. 49(3), 329–342 (2017).
[Crossref]

Han, S.

S. Han, I. Sato, T. Okabe, and Y. Sato, “Fast spectral reflectance recovery using DLP projector,” Int. J. Comput. Vis. 110(2), 172–184 (2014).
[Crossref]

Hanselaer, P.

S. Hermans, K. A. G. Smet, and P. Hanselaer, “Color appearance model for self-luminous stimuli,” J. Opt. Soc. Am. A 35(12), 2000–2009 (2018).
[Crossref]

K. A. G. Smet and P. Hanselaer, “Memory and preferred colours and the colour rendition of white light sources,” Lighting Res. Technol. 48(4), 393–411 (2016).
[Crossref]

K. A. G. Smet, W. R. Ryckaert, M. R. Pointer, G. Deconinck, and P. Hanselaer, “A memory colour quality metric for white light sources,” Energy. Buildings. 49, 216–225 (2012).
[Crossref]

K. A. G. Smet, W. R. Ryckaert, M. R. Pointer, G. Deconinck, and P. Hanselaer, “Memory colours and colour quality evaluation of conventional and solid-state lamps,” Opt. Express 18(25), 26229–26244 (2010).
[Crossref]

Heidrich, W.

Hermans, S.

Houser, K.

T. Esposito and K. Houser, “Models of colour quality over a wide range of spectral power distributions,” Lighting Res. Technol. 51(3), 331–352 (2019).
[Crossref]

M. Royer, A. Wilkerson, M. Wei, K. Houser, and R. Davis, “Human perceptions of colour rendition vary with average fidelity, average gamut, and gamut shape,” Lighting Res. Technol. 49(8), 966–991 (2017).
[Crossref]

K. Houser, M. Mossman, K. Smet, and L. Whitehead, “Tutorial: Color rendering and its applications in lighting,” Leukos 12(1–2), 7–26 (2016).
[Crossref]

Houser, K. W.

M. Wei, K. W. Houser, A. David, and M. R. Krames, “Perceptual responses to LED illumination with colour rendering indices of 85 and 97,” Lighting Res. Technol. 47(7), 810–827 (2015).
[Crossref]

A. David, P. T. Fini, K. W. Houser, Y. Ohno, M. P. Royer, K. A. G. Smet, W. Minchen, and L. Whitehead, “Development of the IES method for evaluating the color rendition of light sources,” Opt. Express 23(12), 15888–11590 (2015).
[Crossref]

Hsu, W. F.

W. F. Hsu and M. H. Weng, “Compact holographic projection display using liquid-crystal-on-Silicon spatial light modulator,” Materials 9(9), 768–776 (2016).
[Crossref]

Hu, R.

J. Zhang, R. Hu, B. Xie, X. Yu, X. Luo, Z. Yu, L. Zhang, H. Wang, and X. Jin, “Energy-saving light source spectrum optimization by considering object’s reflectance,” IEEE Photonics J. 9(2), 1–11 (2017).
[Crossref]

Jin, X.

J. Zhang, R. Hu, B. Xie, X. Yu, X. Luo, Z. Yu, L. Zhang, H. Wang, and X. Jin, “Energy-saving light source spectrum optimization by considering object’s reflectance,” IEEE Photonics J. 9(2), 1–11 (2017).
[Crossref]

Karlicek, R. F.

J. Y. Tsao, M. H. Crawford, M. E. Coltrin, A. J. Fischer, D. D. Koleske, G. S. Subramania, G. T. Wang, J. J. Wierer, and R. F. Karlicek, “Toward smart and ultra-efficient solid-state lighting,” Adv. Opt. Mater. 2(9), 809–836 (2014).
[Crossref]

Kim, S. J.

S. W. Oh, M. S. Brown, M. Pollefeys, and S. J. Kim, “Do it yourself hyperspectral imaging with everyday digital cameras,” in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (IEEE, 2016), pp. 2461–2469.

Kinjiro, A.

Koleske, D. D.

J. Y. Tsao, M. H. Crawford, M. E. Coltrin, A. J. Fischer, D. D. Koleske, G. S. Subramania, G. T. Wang, J. J. Wierer, and R. F. Karlicek, “Toward smart and ultra-efficient solid-state lighting,” Adv. Opt. Mater. 2(9), 809–836 (2014).
[Crossref]

Krames, M. R.

M. Wei, K. W. Houser, A. David, and M. R. Krames, “Perceptual responses to LED illumination with colour rendering indices of 85 and 97,” Lighting Res. Technol. 47(7), 810–827 (2015).
[Crossref]

Li, C.

Z. Liu, Q. Liu, G. A. Gao, and C. Li, “Optimized spectral reconstruction based on adaptive training set selection,” Opt. Express 25(11), 12435–12445 (2017).
[Crossref]

M. R. Luo, G. Cui, and C. Li, “Uniform colour spaces based on CIECAM02 colour appearance model,” Color Res. Appl. 31(4), 320–330 (2006).
[Crossref]

Liu, Q.

Liu, Z.

Luo, M.

M. Luo, G. Cui, and M. Georgoula, “Colour difference evaluation for white light sources,” Lighting Res. Technol. 47(3), 360–369 (2015).
[Crossref]

Luo, M. R.

M. R. Luo, G. Cui, and C. Li, “Uniform colour spaces based on CIECAM02 colour appearance model,” Color Res. Appl. 31(4), 320–330 (2006).
[Crossref]

Luo, R. M.

K. A. G. Smet, L. Whitehead, J. Schanda, and R. M. Luo, “Toward a replacement of the CIE color rendering index for white light sources,” Leukos 12(1–2), 61–69 (2016).
[Crossref]

K. A. G. Smet, J. Schanda, L. Whitehead, and R. M. Luo, “CRI2012: A proposal for updating the CIE colour rendering index,” Lighting Res. Technol. 45(6), 689–709 (2013).
[Crossref]

Luo, X.

J. Zhang, R. Hu, B. Xie, X. Yu, X. Luo, Z. Yu, L. Zhang, H. Wang, and X. Jin, “Energy-saving light source spectrum optimization by considering object’s reflectance,” IEEE Photonics J. 9(2), 1–11 (2017).
[Crossref]

Madhavan, S.

M. Freeman, M. Champion, and S. Madhavan, “Scanned laser pico-projectors: Seeing the bg picture (with a small device),” Opt. Photonics News 20(5), 28–34 (2009).
[Crossref]

Miller, C.

Y. Ohno, M. Fein, and C. Miller, “Vision experiment on chroma saturation for colour quality preference,” Light Eng. 23(4), 6–14 (2015).

Minchen, W.

Mossman, M.

K. Houser, M. Mossman, K. Smet, and L. Whitehead, “Tutorial: Color rendering and its applications in lighting,” Leukos 12(1–2), 7–26 (2016).
[Crossref]

Oh, J. H.

J. H. Oh, S. J. Yang, and Y. R. Do, “Healthy, natural, efficient and tunable lighting: four-package white LEDs for optimizing the circadian effect, color quality and vision performance,” Light: Sci. Appl. 3(2), e141 (2014).
[Crossref]

Oh, S. W.

S. W. Oh, M. S. Brown, M. Pollefeys, and S. J. Kim, “Do it yourself hyperspectral imaging with everyday digital cameras,” in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (IEEE, 2016), pp. 2461–2469.

Ohno, Y.

A. David, P. T. Fini, K. W. Houser, Y. Ohno, M. P. Royer, K. A. G. Smet, W. Minchen, and L. Whitehead, “Development of the IES method for evaluating the color rendition of light sources,” Opt. Express 23(12), 15888–11590 (2015).
[Crossref]

Y. Ohno, M. Fein, and C. Miller, “Vision experiment on chroma saturation for colour quality preference,” Light Eng. 23(4), 6–14 (2015).

W. Davis and Y. Ohno, “Color quality scale,” Opt. Eng. 49(3), 033602 (2010).
[Crossref]

Y. Ohno, “Spectral design considerations for white LED color rendering,” Opt. Eng. 44(11), 111302 (2005).
[Crossref]

Okabe, T.

S. Han, I. Sato, T. Okabe, and Y. Sato, “Fast spectral reflectance recovery using DLP projector,” Int. J. Comput. Vis. 110(2), 172–184 (2014).
[Crossref]

Pointer, M. R.

K. A. G. Smet, W. R. Ryckaert, M. R. Pointer, G. Deconinck, and P. Hanselaer, “A memory colour quality metric for white light sources,” Energy. Buildings. 49, 216–225 (2012).
[Crossref]

K. A. G. Smet, W. R. Ryckaert, M. R. Pointer, G. Deconinck, and P. Hanselaer, “Memory colours and colour quality evaluation of conventional and solid-state lamps,” Opt. Express 18(25), 26229–26244 (2010).
[Crossref]

M. R. Pointer, “The gamut of real surface colours,” Color Res. Appl. 5(3), 145–155 (1980).
[Crossref]

Pollefeys, M.

S. W. Oh, M. S. Brown, M. Pollefeys, and S. J. Kim, “Do it yourself hyperspectral imaging with everyday digital cameras,” in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (IEEE, 2016), pp. 2461–2469.

Ritsch-Marte, M.

Royer, M.

M. Royer, A. Wilkerson, M. Wei, K. Houser, and R. Davis, “Human perceptions of colour rendition vary with average fidelity, average gamut, and gamut shape,” Lighting Res. Technol. 49(8), 966–991 (2017).
[Crossref]

Royer, M. P.

Ryckaert, W. R.

K. A. G. Smet, W. R. Ryckaert, M. R. Pointer, G. Deconinck, and P. Hanselaer, “A memory colour quality metric for white light sources,” Energy. Buildings. 49, 216–225 (2012).
[Crossref]

K. A. G. Smet, W. R. Ryckaert, M. R. Pointer, G. Deconinck, and P. Hanselaer, “Memory colours and colour quality evaluation of conventional and solid-state lamps,” Opt. Express 18(25), 26229–26244 (2010).
[Crossref]

Sato, I.

S. Han, I. Sato, T. Okabe, and Y. Sato, “Fast spectral reflectance recovery using DLP projector,” Int. J. Comput. Vis. 110(2), 172–184 (2014).
[Crossref]

Sato, Y.

S. Han, I. Sato, T. Okabe, and Y. Sato, “Fast spectral reflectance recovery using DLP projector,” Int. J. Comput. Vis. 110(2), 172–184 (2014).
[Crossref]

Schanda, J.

K. A. G. Smet, L. Whitehead, J. Schanda, and R. M. Luo, “Toward a replacement of the CIE color rendering index for white light sources,” Leukos 12(1–2), 61–69 (2016).
[Crossref]

K. A. G. Smet, J. Schanda, L. Whitehead, and R. M. Luo, “CRI2012: A proposal for updating the CIE colour rendering index,” Lighting Res. Technol. 45(6), 689–709 (2013).
[Crossref]

Shen, J.

Sizov, D.

Smet, K.

K. Houser, M. Mossman, K. Smet, and L. Whitehead, “Tutorial: Color rendering and its applications in lighting,” Leukos 12(1–2), 7–26 (2016).
[Crossref]

Smet, K. A. G.

S. Hermans, K. A. G. Smet, and P. Hanselaer, “Color appearance model for self-luminous stimuli,” J. Opt. Soc. Am. A 35(12), 2000–2009 (2018).
[Crossref]

K. A. G. Smet, A. David, and L. Whitehead, “Why color space uniformity and sample set spectral uniformity are essential for color rendering measures,” Leukos 12(1–2), 39–50 (2016).
[Crossref]

K. A. G. Smet and P. Hanselaer, “Memory and preferred colours and the colour rendition of white light sources,” Lighting Res. Technol. 48(4), 393–411 (2016).
[Crossref]

K. A. G. Smet, L. Whitehead, J. Schanda, and R. M. Luo, “Toward a replacement of the CIE color rendering index for white light sources,” Leukos 12(1–2), 61–69 (2016).
[Crossref]

A. David, P. T. Fini, K. W. Houser, Y. Ohno, M. P. Royer, K. A. G. Smet, W. Minchen, and L. Whitehead, “Development of the IES method for evaluating the color rendition of light sources,” Opt. Express 23(12), 15888–11590 (2015).
[Crossref]

K. A. G. Smet, J. Schanda, L. Whitehead, and R. M. Luo, “CRI2012: A proposal for updating the CIE colour rendering index,” Lighting Res. Technol. 45(6), 689–709 (2013).
[Crossref]

K. A. G. Smet, W. R. Ryckaert, M. R. Pointer, G. Deconinck, and P. Hanselaer, “A memory colour quality metric for white light sources,” Energy. Buildings. 49, 216–225 (2012).
[Crossref]

K. A. G. Smet, W. R. Ryckaert, M. R. Pointer, G. Deconinck, and P. Hanselaer, “Memory colours and colour quality evaluation of conventional and solid-state lamps,” Opt. Express 18(25), 26229–26244 (2010).
[Crossref]

K. A. G. Smet, “Tutorial: The LuxPy Python toolbox for lighting and color science,” Leukos. (2019).
[Crossref]

Stiles, W. S.

G. Wyszecki and W. S. Stiles, Color science: concepts and methods, quantitative data and formulas, 2nd edition (John Wiley & Sons. Press, 2000).

Stupp, E. H.

M. S. Brennesholtz and E. H. Stupp, Projection displays (Wiley Publishing, 2008).

Subramania, G. S.

J. Y. Tsao, M. H. Crawford, M. E. Coltrin, A. J. Fischer, D. D. Koleske, G. S. Subramania, G. T. Wang, J. J. Wierer, and R. F. Karlicek, “Toward smart and ultra-efficient solid-state lighting,” Adv. Opt. Mater. 2(9), 809–836 (2014).
[Crossref]

Tsao, J. Y.

J. Y. Tsao, M. H. Crawford, M. E. Coltrin, A. J. Fischer, D. D. Koleske, G. S. Subramania, G. T. Wang, J. J. Wierer, and R. F. Karlicek, “Toward smart and ultra-efficient solid-state lighting,” Adv. Opt. Mater. 2(9), 809–836 (2014).
[Crossref]

Wang, G. T.

J. Y. Tsao, M. H. Crawford, M. E. Coltrin, A. J. Fischer, D. D. Koleske, G. S. Subramania, G. T. Wang, J. J. Wierer, and R. F. Karlicek, “Toward smart and ultra-efficient solid-state lighting,” Adv. Opt. Mater. 2(9), 809–836 (2014).
[Crossref]

Wang, H.

J. Zhang, R. Hu, B. Xie, X. Yu, X. Luo, Z. Yu, L. Zhang, H. Wang, and X. Jin, “Energy-saving light source spectrum optimization by considering object’s reflectance,” IEEE Photonics J. 9(2), 1–11 (2017).
[Crossref]

J. Shen, S. Chang, H. Wang, and Z. Zheng, “Optimal illumination for visual enhancement based on color entropy evaluation,” Opt. Express 24(17), 19788–19800 (2016).
[Crossref]

Wei, M.

M. P. Royer, A. Wilkerson, and M. Wei, “Human perceptions of colour rendition at different chromaticities,” Lighting Res. Technol. 50(7), 965–994 (2018).
[Crossref]

M. Royer, A. Wilkerson, M. Wei, K. Houser, and R. Davis, “Human perceptions of colour rendition vary with average fidelity, average gamut, and gamut shape,” Lighting Res. Technol. 49(8), 966–991 (2017).
[Crossref]

M. Wei, K. W. Houser, A. David, and M. R. Krames, “Perceptual responses to LED illumination with colour rendering indices of 85 and 97,” Lighting Res. Technol. 47(7), 810–827 (2015).
[Crossref]

Weng, M. H.

W. F. Hsu and M. H. Weng, “Compact holographic projection display using liquid-crystal-on-Silicon spatial light modulator,” Materials 9(9), 768–776 (2016).
[Crossref]

Whitehead, L.

K. A. G. Smet, L. Whitehead, J. Schanda, and R. M. Luo, “Toward a replacement of the CIE color rendering index for white light sources,” Leukos 12(1–2), 61–69 (2016).
[Crossref]

K. Houser, M. Mossman, K. Smet, and L. Whitehead, “Tutorial: Color rendering and its applications in lighting,” Leukos 12(1–2), 7–26 (2016).
[Crossref]

K. A. G. Smet, A. David, and L. Whitehead, “Why color space uniformity and sample set spectral uniformity are essential for color rendering measures,” Leukos 12(1–2), 39–50 (2016).
[Crossref]

A. David, P. T. Fini, K. W. Houser, Y. Ohno, M. P. Royer, K. A. G. Smet, W. Minchen, and L. Whitehead, “Development of the IES method for evaluating the color rendition of light sources,” Opt. Express 23(12), 15888–11590 (2015).
[Crossref]

K. A. G. Smet, J. Schanda, L. Whitehead, and R. M. Luo, “CRI2012: A proposal for updating the CIE colour rendering index,” Lighting Res. Technol. 45(6), 689–709 (2013).
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Figures (7)

Fig. 1.
Fig. 1. Conceptual illustration of the considered smart lighting setup.
Fig. 2.
Fig. 2. (a) An example of the SPD of the laser diode illumination. (b) The SPD of the reference illuminant D50. (c) The color gamut of the laser based illumination covers almost entirely Pointer’s gamut.
Fig. 3.
Fig. 3. (a) Tradeoff between fidelity and gamut; light sources can only reside in the non-shaded area. With a spatially variable laser illumination an optimal trade-off between fidelity and gamut can be achieved: i.e. a combined fidelity/gamut score along the red dotted line. (b) The normalized (total radiant flux = 1W) spectral power distribution of the considered phosphor-converted LED (c) Color gamut shape of this LED and the reference illuminant (D50).
Fig. 4.
Fig. 4. (a) The required radiant flux for all 99 TCS’s to obtain a color fidelity Rf = 100. (b) The variation of the average radiant flux (average for 99 TCS’s) and the color gamut index as a function of Rf. (c) The a'b’ coordinate shifts for all TCS’s when the Rf goes from 100 to 60.
Fig. 5.
Fig. 5. (a) Test set-up in order to demonstrate the feasibility of tuning the color rendering performance of a spatially variable laser illumination system: 1. Macbeth ColorChecker, 2. Laser projector, 3. Laptop, 4. Spectrometer, 5. Spectral telescope. The Macbeth ColorChecker under (b) static laser diode illumination, (c) tuned laser diode illumination for Rf = 100, and (d) tuned laser diode illumination for Rf = 60.
Fig. 6.
Fig. 6. The Δu'v' and ΔY/YT values (target Rf = 100) as a function of time for (a) the green color patch, when measured with the spectrometer and (b) the orange-yellow color patch, when measured with the hyperspectral camera.
Fig. 7.
Fig. 7. (a) The measured spectral power distribution of the laser projector primaries (for R, G, B values equal to 125 separately). (b) Required radiant flux for the 18 chromatic Macbeth color patches, with the real laser projector, and with the considered spatially variable laser diode illumination system with Rf = 100. (c) The color gamut of the laser projector.

Equations (9)

Equations on this page are rendered with MathJax. Learn more.

S ( x , y ) ( λ ) = p b S B ( λ ) + p g S G ( λ ) + p r S R ( λ ) .
Φ e = p b + p g + p r [ in Watt ] .
Δ E i = Δ J i 2 + Δ a i 2 + Δ b i 2 ,
R f = 10 × ln ( e ( 100 c f × Δ E ) / 10 + 1 ) with c f = 6 .73 .
R g = 100 × A test / A ref ,
[ X Y Z ]   =   [ X b X g X r Y b Y g Y r Z b Z g Z r ] [ p b p g p r ] ,
[ p b p g p r ] = [ X b X g X r Y b Y g Y r Z b Z g Z r ] 1 [ X Y Z ] .
Δ E = 100 10 ln ( e R f / 10 1 ) c f ,
{ J i ,new = J i a i ,new = a + s i g n ( a ) Δ E i 1 + b 2 / b 2 a 2 a 2 b i ,new = k a i ,new k = a i b i .

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