Manuel Melgosa

Note. Visual and Instrumental Color Evaluation in Red Wines

The color of 15 red wines from several wineries within the renowned wine-producing region Rioja (Northern Spain) was measured by spectrophotometric and spectroradiometric techniques and was visually assessed in a pair-comparison experiment by a panel of 10 experienced observers having normal color vision. Correlation between instrumental color measurements made by spectrophotometric and spectroradiometric techniques was very low, as expected from major differences in the experimental conditions employed (different illumination, path lengths and glass effects). Spectroradiometric measurements at the center of the wine sampler and at positions displaced 1 cm in the horizontal and vertical directions were quite different, mainly because of an increase of the lightness L*, the average color differences between them being high (3.5 and 2.6 CIELAB units, respectively). A 50% acceptance percentage resulted for a color difference of 2.8 CIELAB units, using a reference anchor-pair of wine samples with 4.0 CIELAB units. Thus, a value around 3.0 CIELAB units should be considered a preliminary estimate of the acceptable tolerance by the human eye for red wines poured in standard wine samplers.

Measurement of the relationship between perceived and computed color differences

Using simulated data sets, we have analyzed some mathematical properties of different statistical measurements that have been employed in previous literature to test the performance of different color-difference formulas. Specifically, the properties of the combined index PF/3 (performance factor obtained as average of three terms), widely employed in current literature, have been considered. A new index named standardized residual sum of squares (STRESS), employed in multidimensional scaling techniques, is recommended. The main difference between PF/3 and STRESS is that the latter is simpler and allows inferences on the statistical significance of two color-difference formulas with respect to a given set of visual data.

Suprathreshold color-difference ellipsoids for surface colors

The RIT-DuPont visual color-difference data [Color Res. Appl. 16, 297–316 (1991)] have been used to estimate contours of equal color-differences (ellipsoids) at 19 color centers, in CIELAB and x, y, Y/100 color spaces. The ellipsoid fits are better in the CIELAB space than in x, y, Y/100, since the design of the RIT-DuPont experiment emphasized directional balance in CIELAB. The ellipsoids estimated are hardly tilted with respect to L* or Y/100, and they appear to be in overall good agreement with those reported for object colors in recent publications. From the characteristics and accuracy of the RIT-DuPont experiment, the current ellipsoids can be considered highly reliable and representative of color discrimination under the observational conditions employed, these closely following the “reference conditions” recently suggested by the CIE for industrial color-difference evaluation [Color Res. Appl. 20, 399–403 (1995)].

Performance of recent advanced color-difference formulas using the standardized residual sum of squares index.

The standardized residual sum of squares (STRESS) index was used to reevaluate four experimental datasets employed during the development of CIEDE2000, the current CIE recommended color-difference formula. This index enables statistical inferences not achievable by other metrics used commonly for performance evaluation. It was found that CIEDE2000 was statistically superior at a 95% confidence level to either CIE94, the previous recommended equation by the CIE, or the simple Euclidean distance in CIELAB, DeltaE*ab. Recent formulas based on the CIECAM02 color-appearance space and chroma-compressed variants of CIELAB were also evaluated and found to have only slightly reduced performance compared with CIEDE2000. These formulas have the advantage of simplicity and easier interpretation when used for quantifying color accuracy. Finally, each experimental dataset was evaluated separately rather than weight averaged as used during the development of CIEDE2000. Significant differences were found between datasets, suggesting that combining datasets may obscure important differences and that the practice of parameter optimization during formula development using combined data is likely suboptimal.

Testing CIELAB‐based color‐difference formulas

The CMC, BFD, and CIE94 color-difference for- mulas have been compared throughout their weighting functions to the CIELAB componentsDL*, DC*, DH*, and from their performance with respect to several wide data- sets from old and recent literature. Predicting the magni- tude of perceived color differences, a statistically significant improvement upon CIELAB should be recognized for these three formulas, in particular for CIE94.© 2000 John Wiley &

Euclidean color-difference formula for small-medium color différences in log-compressed OSA-UCS space

This work continues previous research by the same authors [J. Opt. Soc. Am. A23, 2077 (2006)], where empirical small-medium color differences were represented by an ellipsoidal equation DeltaEGP in the Uniform Color System of the Optical Society of America. Now logarithmic compressions on chroma and lightness are introduced to produce a new space with Euclidean color-difference formulas DeltaEE. The CIEDE2000, DeltaEGP, and DeltaEE formulas are found statistically equivalent in the prediction of many available empirical datasets. However, DeltaEE is the simplest formula providing relationships with visual processing. These analyses hold true for CIE 1964 Supplementary Standard Observer and D65 illuminant.

Relative significance of the terms in the CIEDE2000 and CIE94 color-difference formulas.

CIELAB-based color-difference formulas are used to improve the prediction of visually perceived color differences through the introduction of various corrections to CIELAB. In our study we analyze the relative importance of these corrections. From the combined dataset employed for the development of CIEDE2000, we found that the improvement of CIE94 over CIELAB was considerably greater than that of CIEDE2000 over CIE94. Chroma-difference correction was the most important correction in both CIE94 and CIEDE2000. With an arbitrary value of 100 assigned to this correction, the score of the hue-difference correction in CIE94 was 21, and the scores of the four remaining corrections in CIEDE2000 were as follows: hue difference, 29; rotation term, 8; lightness difference, 8; and gray correction, 6. At 95% confidence level each of the corrections introduced in CIEDE2000 or CIE94 was statistically significant for the whole combined dataset, in agreement with the results reported by CIE TC 1-47 and 1-29. For the combined dataset, the differences between CMC and CIEDE2000 were found to be statistically significant at 95% confidence level, but the differences between CMC and CIE94 were not. From subsets of the combined dataset it was concluded that further analyses of the lightness-difference and gray corrections proposed by CIEDE2000 would be desirable, using new experimental data.

Characterization of the human iris spectral reflectance with a multispectral imaging system.

We present a multispectral system developed and optimized for measurement of the spectral reflectance and the color of the human iris. We tested several sets of filters as acquisition channels, analyzed different reconstruction algorithms, and used different samples as training sets. The results obtained show that a conventional three-channel color camera (RGB) was enough to reconstruct the analyzed reflectances with high accuracy, obtaining averaged color differences of around 2-3 CIEDE2000 units and root mean square errors of around 0.01. The device developed was used to characterize 100 real irises corresponding to 50 subjects, 68 prostheses used in clinical practice, and 17 cosmetic colored contact lenses.

Performance of a color-difference formula based on OSA-UCS space using small-medium color differences

An investigation of the color metrics and the complexity of the CIEDE2000 formula shows that CIELAB space is inadequate to represent small-medium color differences. The OSA-UCS (Uniform Color Space) Committee has shown that no space with uniform scale for large color differences exists. Therefore the practical way for color-difference specification is a color-difference formula in a nonuniform space. First, the BFD (Bradford University) ellipses are considered in the OSA-UCS space, and their very high regularity suggests a new and very simple color-difference formula at constant luminance. Then the COM (combined) data set used for the development of the CIEDE2000 formula is considered in the OSA-UCS space, and the color-difference formula is extended to sample pairs with a different luminance factor. The value of the performance factor PF/3 for the proposed OSA-UCS-based formula shows that the formula performs like the more complex CIEDE2000 formula for small-medium color differences.

Are We Able to Distinguish Color Attributes

The perception and understanding of the three color attributes have been analyzed from two experiments using pairs of Munsell samples, where only one of the three color attributes were changed/unchanged (Experiment I/II) at a time. In each experiment, 36 pairs with color differences of 3 different sizes (average values of 15.8 and 21.7 CIELAB units for Experiments I and II, respectively) were assessed under standardized conditions by 40 normal observers, 20 of them with previous knowledge and experience in colorimetry. At a 95% confidence level, the results from the two experiments were not significantly different, indicating that color attributes were not easily distinguished: for example, for experienced observers, the percentage of correct answers for identifying the color attribute responsible for a color difference was only 72.4%, the random probability being 33.3%. There were no significant differences between the results found by men and women. The worst distinguished attribute was Chroma, that is, the least frequent confusion was between Hue and Value or vice versa. Value differences were more easily detected for achromatic than for chromatic pairs, both for experienced and inexperienced observers. With respect to the size of the color differences, we observed that large hue differences were more easily identifiable than smaller ones, and a constant Hue was more identifiable when the entire color difference was small.