Influence of random-dot textures on perception of suprathreshold color differences

Optimizing Color-Difference Formulas for 3D-Printed Objects

Based on previous visual assessments of 440 color pairs of 3D-printed samples, we tested the performance of eight color-difference formulas (CIELAB, CIEDE2000, CAM02-LCD, CAM02-SCD, CAM02-UCS, CAM16-LCD, CAM16-SCD, and CAM16-UCS) using the standardized residual sum of squares (STRESS) index. For the whole set of 440 color pairs, the introduction of k_L (lightness parametric factor), b (exponent in total color difference), and k_L + b produced an average STRESS decrease of 2.6%, 26.9%, and 29.6%, respectively. In most cases, the CIELAB formula was significantly worse statistically than the remaining seven formulas, for which no statistically significant differences were found. Therefore, based on visual results using 3D-object colors with the specific shape, size, gloss, and magnitude of color differences considered here, we concluded that the CIEDE2000, CAM02-, and CAM16-based formulas were equivalent and thus cannot recommend only one of them. Disregarding CIELAB, the average STRESS decreases in the k_L + b-optimized formulas from changes in each one of the four analyzed parametric factors were not statistically significant and had the following values: 6.2 units changing from color pairs with less to more than 5.0 CIELAB units; 2.9 units changing the shape of the samples (lowest STRESS values for cylinders); 0.7 units changing from nearly-matte to high-gloss samples; and 0.5 units changing from 4 cm to 2 cm samples.

Parametric effects in color-difference evaluation

An experiment was conducted to investigate three parameters affecting color-difference evaluation on a display: 4 sample sizes (2°, 4°, 10°, and 20°), 2 color-difference magnitudes (4 and 8 CIELAB units), and 2 separations (inclusion or exclusion of the separation line between two colors in a pair). Sample pairs surrounding 5 CIE recommended color centers were prepared. In total, 1120 sample pairs of colors were assessed 20 times using the grey-scale method. The experimental results were used to reveal various parametric effects and to verify the performance of different color matching functions (CMFs) and four color difference formulae and uniform color spaces. It was found that there was little difference in terms of ΔE values calculated using different CMFs for all the color models tested. A parametric formula was proposed to predict three parametric effects for sample pairs having no-separation line: 1) differences in sample size, 2) media (surface and self-luminous colors), and 3) color-difference magnitudes.

Optimizing Parametric Factors in CIELAB and CIEDE2000 Color-Difference Formulas for 3D-Printed Spherical Objects

The current color-difference formulas were developed based on 2D samples and there is no standard guidance for the color-difference evaluation of 3D objects. The aim of this study was to test and optimize the CIELAB and CIEDE2000 color-difference formulas by using 42 pairs of 3D-printed spherical samples in Experiment I and 40 sample pairs in Experiment II. Fifteen human observers with normal color vision were invited to attend the visual experiments under simulated D65 illumination and assess the color differences of the 82 pairs of 3D spherical samples using the gray-scale method. The performances of the CIELAB and CIEDE2000 formulas were quantified by the STRESS index and F-test with respect to the collected visual results and three different optimization methods were performed on the original color-difference formulas by using the data from the 42 sample pairs in Experiment I. It was found that the optimum parametric factors for CIELAB were kL = 1.4 and kC = 1.9, whereas for CIEDE2000, kL = 1.5. The visual data of the 40 sample pairs in Experiment II were used to test the performance of the optimized formulas and the STRESS values obtained for CIELAB/CIEDE2000 were 32.8/32.9 for the original formulas and 25.3/25.4 for the optimized formulas. The F-test results indicated that a significant improvement was achieved using the proposed optimization of the parametric factors applied to both color-difference formulas for 3D-printed spherical samples.

Influences of shape, size, and gloss on the perceived color difference of 3D printed objects

In order to study the influence and mechanisms of color differences using 3D-shaped objects, 440 pairs of 3D samples surrounding five CIE color centers (gray, red, yellow, green, and blue) with the variations of gloss, size, and shape were prepared by a Sailner 3D color printer, and their color differences were assessed by 26∼45 observers using the gray-scale method. The new color difference data were used to investigate the parametric effects (gloss, 3D shape, and size) on the perceived color difference. Results indicate that, for 3D objects, high gloss and small size objects (2 cm) raise smaller visual color differences than matte and large size objects (4 cm), and the visual color difference of spheres is larger than that of the cone and cylinder sample pairs. The chromaticity ellipses indicated that the glossy samples with different shapes will arouse fairly different visual perceptions, especially for sphere and cylinder samples.

Potential Use of Carrageenans against the Limestone Proliferation of the Cyanobacterium Parakomarekiella sesnandensis

Stone biodeterioration by cyanobacteria is a common issue in the field of cultural heritage. As they are considered the first stone colonizers, the need to control their growth has increased. In this study, we evaluated the effectiveness of kappa/iota carrageenans from the red seaweed Chondracanthus teedei var. lusitanicus against the limestone proliferation of the cyanobacterium Parakomarekiella sesnandensis, under laboratory conditions. For this purpose, 200 μL of kappa/iota carrageenans (0.01 g mL−1) were applied into the surface of the limestone replicas prior to their inoculation with P. sesnandensis. Results were evaluated after 4 months of incubation through visual inspection, stereomicroscopy, scanning electron microscopy and colorimetric analyses. The gathered data demonstrated that these types of carrageenans have the potential to reduce the colonization of P. sesnandensis. With one sole application, the aesthetical alterations caused by the proliferation of P. sesnandensis were drastically reduced. This study highlights the need to explore marine-based products, particularly those derived from seaweeds with antimicrobial properties, as alternative methods for biocleaning cultural heritage assets.

Color-difference evaluation for 3D objects

A psychophysical experiment using 3D printed samples was conducted to investigate the change of perceived color differences caused by two different illuminations and two 3D sample shapes. 150 pairs of 3D printed samples around five CIE color centers [Color Res. Appl. 20, 399-403, 1995], consisting of 75 pairs of spherical samples and 75 pairs of flat samples, with a wide range of color differences covering from small to large magnitude, were printed by an Mcor Iris paper-based 3D color printer. Each pair was assessed twice by a panel of 10 observers using a gray-scale psychophysical method in a spectral tunable LED viewing cabinet with two types of light sources: diffuse lighting with and without an additional overhead spotlight. The experimental results confirmed that the lighting conditions had more effect on the perceived color difference between complex 3D shapes than between 2D objects. The results for 3D and 2D objects were more similar under only diffuse lighting. Current 3D results had good correlations with previous ones [Color Res. Appl. 24, 356-368, 1999; J. Opt. Soc. Am. A 36, 789-799, 2019] using 2D samples with large color differences, meaning that color-difference magnitude had more effect on perceived color differences than sample shape and lighting. Considering ten modern color-difference formulas, the best predictions of the current experimental data were found for CAM02-LCD formula [Color Res. Appl. 31, 320-330, 2006]. For current results, it was also found that predictions of current color-difference formulas were below average inter-observer variability, and remarkable improvements were found by adding power corrections [Opt. Express 23, 597-610, 2015].

How Good Are RGB Cameras Retrieving Colors of Natural Scenes and Paintings?-A Study Based on Hyperspectral Imaging

RGB digital cameras (RGB) compress the spectral information into a trichromatic system capable of approximately representing the actual colors of objects. Although RGB digital cameras follow the same compression philosophy as the human eye (OBS), the spectral sensitivity is different. To what extent they provide the same chromatic experiences is still an open question, especially with complex images. We addressed this question by comparing the actual colors derived from spectral imaging with those obtained with RGB cameras. The data from hyperspectral imaging of 50 natural scenes and 89 paintings was used to estimate the chromatic differences between OBS and RGB. The corresponding color errors were estimated and analyzed in the color spaces CIELAB (using the color difference formulas ΔE^*_ab and CIEDE2000), J_za_zb_z, and iCAM06. In CIELAB the most frequent error (using ΔE^*_ab) found was 5 for both paintings and natural scenes, a similarity that held for the other spaces tested. In addition, the distribution of errors across the color space shows that the errors are small in the achromatic region and increase with saturation. Overall, the results indicate that the chromatic errors estimated are close to the acceptance error and therefore RGB digital cameras are able to produce quite realistic colors of complex scenarios.

How color difference formulas depend on reference pairs in the underlying constant stimuli experiment

For calculating color differences, the CIEDE2000 and CIE94 equations are widely used and recommended. These equations were derived more than a decade ago, based for a large part on the RIT-Dupont set of visual data. This data was collected from a series of psychophysical tests that use the method of constant stimuli. In this method, observers need to compare the color difference within a sample pair to that between a reference pair. In the current investigation, we show that the color difference equation significantly changes if reference pairs are chosen in the underlying visual experiments that differ from what was used when creating the RIT-Dupont dataset. The investigation is done using metallic paint samples representing two color centers, red and yellow-green. We show that the reproducibility differs for three different reference pairs, and that for modeling the visual data for the yellow-green color center, extra model terms are required as compared to the CIEDE2000 equation. Our results suggest that observers differ in their ability to mentally convert a color difference recognized in a sample pair into an equivalent color difference along the color difference direction represented by the reference pair. We also find that in these tests the tolerance to lightness differences is widened by a factor of 1.3 to 1.6, and that for the red color center the tolerance ellipsoid is rotated by 30° as compared to the CIEDE2000 equation. The latter observations are possibly due to the metallic texture in the samples used for the current experiment.

Color-difference evaluation for digital images using a categorical judgment method

The CIELAB lightness and chroma values of pixels in five of the eight ISO SCID natural images were modified to produce sample images. Pairs of images were displayed on a calibrated monitor and assessed by a panel of 12 observers with normal color vision using a categorical judgment method. The experimental results showed that assuming the lightness parametric factor k(L)=1 to predict color differences in images, CIELAB performed better than CIEDE2000, CIE94, or CMC, which is a different result to the one found in color-difference literature for homogeneous color pairs. However, observers perceived CIELAB lightness and chroma differences in images in different ways. To fit current experimental data, a specific methodology is proposed to optimize k(L) in the color-difference formulas CIELAB, CIEDE2000, CIE94, and CMC. From the standardized residual sum of squares (STRESS) index, it was found that the optimized formulas, CIEDE2000(2.3:1), CIE94(3.0:1), and CMC(3.4:1), performed significantly better than their corresponding original forms with lightness parametric factor k(L)=1. Specifically, CIEDE2000(2.3:1) performed the best, with a satisfactory average STRESS value of 25.8, which is very similar to the 27.5 value that was found from the CIEDE2000(1:1) formula for the combined weighted dataset of homogeneous color samples employed at the development of this formula [J. Opt. Soc. Am. A25, 1828 (2008), Table 2]. However, fitting our experimental data, none of the four optimized formulas CIELAB(1.5:1), CIEDE2000(2.3:1), CIE94(3.0:1), and CMC(3.4:1) is significantly better than the others. Current results roughly agree with the recent CIE recommendation that color difference in images can be predicted by simply adopting a lightness parametric factor k(L)=2 in CIELAB or CIEDE2000 [CIE Publication 199:2011]. It was also found that the different contents of the five images have considerable influence on the performance of the tested color-difference formulas.

Evaluation of threshold color differences using printed samples

The performances of uniform color spaces and color-difference formulae for predicting threshold color differences were investigated based on visual assessments of 893 pairs of printed color patches under a D65 source. The average ΔE(ab,10)* of the pairs was 1.1 units. A threshold psychophysical experiment was repeated three times by a panel of 16 observers with normal color vision. The experimental data were used to evaluate nine color-difference formulae and uniform color spaces using the standardized residual sum of squares (STRESS) measure. The results indicated that all formulae and spaces performed very similarly to each other, and outperformed CIELAB for threshold color differences. The chromaticity-discrimination ellipses were used to compare with previous results from small color differences [Color Res. Appl. (2011), doi:10.1002/col.20689], and they agreed with each other, except for the purple color center.

Investigations of suprathreshold color-difference tolerances with different visual scales and different perceptual correlates using CRT colors

In order to investigate the performance of suprathreshold color-difference tolerances with different visual scales and different perceptual correlates, a psychophysical experiment was carried out by the method of constant stimuli using CRT colors. Five hue circles at three lightness (L*=30, 50, and 70) and chroma (C*ab=10, 20, and 30) levels were selected to ensure that the color-difference tolerances did not exceed the color gamut of the CRT display. Twelve color centers distributed evenly every 30 degrees along each hue circle were assessed by a panel of eight observers, and the corresponding color-difference tolerances were obtained. The hue circle with L*=50 and C*ab=20 was assessed with three different visual scales (DeltaV=3.06, 5.92, and 8.87 CIELAB units), which ranged from small to large visual scales, while the remaining hue circles were observed only with the small visual scale. The lightness tolerances had no significant correlation with the hue angles, while chroma and hue tolerances showed considerable hue angle dependences. The color-difference tolerances were linearly proportional to the visual scales but with different slopes. The lightness tolerances with different lightness levels but the same chroma showed the crispening effect to some extent, while the chroma and hue tolerances decreased with the increment of the lightness. For the color-difference tolerances with different chroma levels but the same lightness, there was no correlation between the lightness tolerances and the chroma levels, while the chroma and hue tolerances were nearly linearly proportional to the chroma levels.