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Optics Express

Optics Express

  • Editor: Andrew M. Weiner
  • Vol. 22, Iss. 3 — Feb. 10, 2014
  • pp: 3458–3467
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Measuring color differences in automotive samples with lightness flop: A test of the AUDI2000 color-difference formula

Manuel Melgosa, Juan Martínez-García, Luis Gómez-Robledo, Esther Perales, Francisco M. Martínez-Verdú, and Thomas Dauser  »View Author Affiliations


Optics Express, Vol. 22, Issue 3, pp. 3458-3467 (2014)
http://dx.doi.org/10.1364/OE.22.003458


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Abstract

From a set of gonioapparent automotive samples from different manufacturers we selected 28 low-chroma color pairs with relatively small color differences predominantly in lightness. These color pairs were visually assessed with a gray scale at six different viewing angles by a panel of 10 observers. Using the Standardized Residual Sum of Squares (STRESS) index, the results of our visual experiment were tested against predictions made by 12 modern color-difference formulas. From a weighted STRESS index accounting for the uncertainty in visual assessments, the best prediction of our whole experiment was achieved using AUDI2000, CAM02-SCD, CAM02-UCS and OSA-GP-Euclidean color-difference formulas, which were no statistically significant different among them. A two-step optimization of the original AUDI2000 color-difference formula resulted in a modified AUDI2000 formula which performed both, significantly better than the original formula and below the experimental inter-observer variability. Nevertheless the proposal of a new revised AUDI2000 color-difference formula requires additional experimental data.

© 2014 Optical Society of America

1. Introduction

In the middle of the 90’s AUDI developed a tolerance formula for the approval of effect paint batches when colors with strong flop effects were important in the automotive sector [18

18. DIN 6175–2, “Farbtoleranzen für Automobillackierungen – Teil 2: Effektlackierungen,” Deutsches Institut für Normung eV, Berlin, 2001–03.

]. Later this formula was modified to predict color tolerances for solids as well as for effect colors, leading to the AUDI2000 color-difference formula [19

19. T. Dauser, AUDI AG, I/PG-C41, 85045 Ingolstadt, Germany (personal communication, 2012). See http://www.detroitcc.org/files/Color%20Management%20at%20AUDI%20(DCC%20March%202012).pdf

], currently employed by different manufacturers in the automotive industry. Gonioapparent materials or materials with flop effects represent a big challenge for color-difference evaluation in the automotive industry, and nowadays AUDI2000 is the only available color-difference formula considering such effects. According to ASTM [20

20. American Society for Testing and Materials (ASTM), “Standard terminology of appearance,” ASTM E 284, 95a (1995).

], “appearance” can be defined as “the aspect of visual experience by which things are recognized”, “goniochromatism” as the “change in any of all attributes of color of a specimen on change in angular illuminating-viewing conditions but without change in light source or observer”, and “flop” as “a difference in appearance of a material viewed over two widely different aspecular angles”. While preliminary tests of AUDI2000 with homogeneous (solid) color pairs provided satisfactory results [21

21. M. Melgosa, L. Gómez-Robledo, J. Martínez-García, E. Perales, F. M. Martínez-Verdú, and T. Dauser, “Testing a colour-difference formula for the automotive industry using the experimental visual datasets employed in CIEDE2000 development,” in Proceedings CIE Centenary Conference (CIE, Paris, 2013), CIE x038:2013 Publication, pp. 465–469.

, 22

22. J. Martínez-García, M. Melgosa, L. Gómez-Robledo, C. Li, M. Huang, H. Liu, G. Cui, M. R. Luo, and T. Dauser, “Testing the AUDI2000 colour-difference formula for solid colours using some visual datasets with usefulness to automotive industry,” in Proceedings 8thIberoamerican Optics Meeting and 11th Latin American Meeting on Optics, Lasers and Applications (Proc. SPIE Vol. 8785, http://proceedings.spiedigitallibrary.org/proceeding.aspx?articleid=1782351). [CrossRef]

], the goal of the current paper is to perform a color-difference experiment using automotive samples with lightness flop effects, analyzing the performance of AUDI2000 and other advanced color-difference formulas with respect to our experimental results. As requested by CIE [23

23. M. Melgosa, “Request for existing experimental datasets on color differences,” Color Res. Appl. 32(2), 159 (2007). [CrossRef]

], it is desirable to have new reliable experimental color-difference data sets to test and improve current color-difference formulas, and color-difference data sets involving gonioapparent materials are very scarce in current literature [24

24. O. Gómez, F. J. Burgos, E. Perales, E. Chorro, V. Viqueira, F. M. Martínez-Verdú, and J. Pujol, “Preliminary comparative performance of the AUDI2000 and CIEDE2000 color difference formulas by visual assessments in directional lighting booth,” in Proceedings AIC Colour 2013 (Newcastle, UK, 2013), 4, pp. 1545–1548.

].

2. Materials and method

2.1 Selection of color pairs

Color measurements of 277 samples from different automotive suppliers were carried out with a BYK-mac 23 mm multi-angle spectrophotometer, assuming D65 illuminant and CIE 1964 standard colorimetric observer. This instrument provides color measurements considering a light source placed 45° with respect to the perpendicular to the sample, and detection at six different angles [25

25. American Society for Testing and Materials (ASTM), Standard practice for specifying the geometry of multiangle spectrophotometers, Technical Report ASTM E2194.01 (2011).

]: −15°, + 15°, + 25°, + 45°, + 75° and + 110°. The negative/positive signs of these six angles indicate clockwise/counterclockwise rotation angles with respect to the specular reflection of the incident light (see Fig. 1
Fig. 1 Illumination and detection (viewing) geometries [25] considered in the current work.
). These six illumination-detection geometries are also designed by CIE [26

26. Commission Internationale de l’Éclairage (CIE), Colorimetry, 3rd Edition, CIE Publication 15:2004 (CIE Central Bureau, Vienna, 2004).

] as 45°x:-60°, 45°x:-30°, 45°x:-20°, 45°x:0°, 45°x:30° and 45°x:65°, respectively.

2.2 Visual assessments

Using a gray scale method [27

27. S. Guan and M. R. Luo, “Investigation of parametric effects using small colour-differences,” Color Res. Appl. 24(5), 331–343 (1999). [CrossRef]

], each one of the selected 28 color pairs was visually assessed in a multi-angle byko-spectra effect light booth at the same six viewing angles previously mentioned: −15°, + 15°, + 25°, + 45°, + 75° and + 110° (see Fig. 1). Two issues of the SDC Grey Scale for Change in Colour [28

28. http://www.sdcenterprises.co.uk/products/testing.asp See also ISO 105–A02:1993, “Test for colour fastness – Part A02: Gray scale for assessing change in colour,” International Organization for Standardization (Geneva, Switzerland).

] were used to provide the observer a set of 9 gray color pairs with increasing color differences. These 9 color pairs were placed on the same plane than the test color pair, the distance between the center of the test pair and the central gray color pair #3 being 7.5 cm. The illuminance at the center of the test pair was 1800 lx. Bearing in mind that our selected samples had different sizes, looking also for more accurate visual assessments, we used a black gray mask providing a rectangular 3.5 cm x 3.7 cm test pair, with the same size than the color pairs in the gray scale. The color and texture of this mask (L* = 18.6, a* = 0.2, b* = 0.1) were very similar to the ones of the background in the light booth (L* = 24.2, a* = 0.5, b* = −0.6).

2.3 Spectroradiometric color measurements

From our spectroradiometric measurements at the six viewing angles we found that color differences in our 28 color pairs were below 6.0 CIELAB units in more than 83% of the cases [Fig. 3
Fig. 3 Histograms with magnitudes of color differences (left) and lightness flop (right) for the pairs/samples in our visual experiment (CS 2000 spectroradiometric measurements in the byko-spectra effect light booth).
(left)], which is an appropriate range considering that the maximum color differences in our gray scale were in the range 10-15 CIELAB units, depending on the viewing angle we consider. We also found from our spectroradiometric measurements that the average CIELAB lightness difference in our 28 color pairs was high (71% of the total color difference), as desired in our experiment. Finally, from spectroradiometric measurements in the light booth, we measured the lightness flop (defined as the difference between maximum and minimum L* values in the six measured angles) of the color samples involved in our selected 28 color pairs [Fig. 3(right)], obtaining an average value of 33.4 with a high standard deviation of 32.2. The samples with moderate lightness flop were predominant, and this flop range was considered appropriate to test the performance of the lightness flop term in AUDI2000.

The raw gray scale values reported by the observers were transformed into true ΔV values, as usually made in the literature [27

27. S. Guan and M. R. Luo, “Investigation of parametric effects using small colour-differences,” Color Res. Appl. 24(5), 331–343 (1999). [CrossRef]

], using a fourth polynomial function fitted from spectroradiometric CIELAB color differences measured in the gray scale color pairs at each one of the six observation angles (Fig. 4
Fig. 4 CIELAB color differences (CS 2000 spectroradiometric measurements in the byko-spectra effect light booth) for each one of the nine color pairs in the SDC gray scale [28] at each one of the six viewing geometries (−15°, + 15°, + 25°, + 45°, + 75° and + 110°). Lines connecting points correspond to fitted fourth degree polynomial functions, used for transformations from raw gray scale values to true ΔV values.
). The color pairs of the SDC gray scale [28

28. http://www.sdcenterprises.co.uk/products/testing.asp See also ISO 105–A02:1993, “Test for colour fastness – Part A02: Gray scale for assessing change in colour,” International Organization for Standardization (Geneva, Switzerland).

] had different color differences at different viewing angles. This is not surprising bearing in mind that gonioapparent effects have been also reported for other reflectance standards [30

30. A. Ferrero, A. M. Rabal, J. Campos, A. Pons, and M. L. Hernanz, “Spectral and geometrical variation of the bidirectional reflectance distribution function of diffuse reflectance standards,” Appl. Opt. 51(36), 8535–8540 (2012). [CrossRef] [PubMed]

].

2.4 Performance of color-difference formulas

When γi = −15°, the SL,-15°, SC,-15° and SH,-15° functions are equal to SL, + 15°, SC, + 15° and SH, + 15° respectively, multiplying the slopes of all flop terms by a factor 1.2. That is, for −15° the slopes of each one of the flop terms are changed as follows: 1.0 x 1.2 = 1.2 = a5, 1.478 x 1.2 = 1.774 and 0.800 x 1.2 = 0.960. Analogously, when γi = + 110°, the SL,110°, SC,110° and SH,110° functions are equal to SL, + 75°, SC, + 75° and SH, + 75° respectively, multiplying the slopes of each one of the flop terms by a factor 0.5. That is, for + 110° the slopes of each one of the flop terms are changed as follows: 1.0 x 0.5 = 0.5 = a6, 1.478 x 0.5 = 0.739 and 0.800 x 0.5 = 0.400.

In summary, coefficients a1 and a2 are the slope and exponent in the lightness flop term, respectively, coefficients a3 and a4 are not in the lightness flop term but in the weighting function for lightness [Eq. (2)], and coefficients a5 and a6, are the slopes of the lightness flop terms for angles −15° and + 110°, respectively. The values of the six ai (i = 1,…,6) coefficients in the original AUDI2000 color-difference formula will be optimized later.

In usual automotive practice there is a master or reference sample, and Eqs. (2) to (4) are computed using color measurements from this master sample. However, in our current experiment we have two different samples and there is no reason to establish one of them as master. Therefore, here we computed Eqs. (2) to (4) for the two samples in each color pair and the corresponding arithmetical means were adopted as the final weighting functions in computations using Eq. (1). Note also that Eq. (1) provides a different value for each one of the six γi angles: the average and maximum of these six values are usually employed to establish pass/fail tolerances in the automotive industry.

The STRESS index [35

35. P. A. García, R. Huertas, M. Melgosa, and G. Cui, “Measurement of the relationship between perceived and computed color differences,” J. Opt. Soc. Am. A 24(7), 1823–1829 (2007). [CrossRef] [PubMed]

] has been employed to measure the goodness of the predictions made by each tested color-difference formulas with respect to the whole set of results in our experiment (i.e. 28 color pairs x 6 geometries = 168 color pairs). The ΔVi (i = 1,…,168) values in our STRESS computations were the average of true ΔV values reported by our 10 observers. Low STRESS values (always in the range 0-100) indicate better performance of a color-difference formula. F-tests can be used with the STRESS index in order to know whether two color-difference formulas are or not statistically significant different [35

35. P. A. García, R. Huertas, M. Melgosa, and G. Cui, “Measurement of the relationship between perceived and computed color differences,” J. Opt. Soc. Am. A 24(7), 1823–1829 (2007). [CrossRef] [PubMed]

]: the square of the ratio of the STRESS values from two color-difference formulas is compared with a specific confidence interval, which in our case was [0.74 ; 1.36], assuming a 95% confidence interval and taking into account that we have 168 color pairs. We have also used the STRESS index to compute intra- and inter-observer variability in our experiment [36

36. M. Melgosa, P. A. García, L. Gómez-Robledo, R. Shamey, D. Hinks, G. Cui, and M. R. Luo, “Notes on the application of the standardized residual sum of square index for the assessment of intra- and inter-observer variability in color-difference experiments,” J. Opt. Soc. Am. A 28(5), 949–953 (2011). [CrossRef]

]. For each observer, intra-observer variability was computed as the average of STRESS values of each one of the 3 replications made by this observer with respect to the average of the 3 replications, while inter-observer variability was computed as the STRESS value considering the average result of the 3 replications of this observer with respect to the average results of all 10 observers. Final intra- and inter-observer variability in our experiment were defined as the average intra- and inter-observer variability STRESS values from the 10 observers, respectively. A weighted STRESS, WSTRESS [37

37. R. S. Berns and B. Hou, “RIT-DuPont supra-threshold color-tolerance individual color-difference pair dataset,” Color Res. Appl. 35(4), 274–283 (2010). [CrossRef]

], has been also computed here bearing in mind that it is convenient to take into account the consistency of visual assessments made by the observers, giving a higher weight to color pairs where visual assessments have low standard deviations. Specifically, we used as appropriate weight the ratio ΔVi /4σi (i = 1,…,168), where ΔVi and σi are the average and standard deviation of true ΔV values from the 10 observers in each color pair, respectively. In our experiment the average weight was 0.90 with a standard deviation of 0.50.

3. Results and discussion

STRESS values corresponding to intra- and inter-observer variability in our whole experiment were 25.2 and 23.2, respectively. Similar values can be found in recent literature [30

30. A. Ferrero, A. M. Rabal, J. Campos, A. Pons, and M. L. Hernanz, “Spectral and geometrical variation of the bidirectional reflectance distribution function of diffuse reflectance standards,” Appl. Opt. 51(36), 8535–8540 (2012). [CrossRef] [PubMed]

].

4. Conclusion

A visual data set with automotive samples showing flop effects has been developed. Detailed results in this data set (i.e. color coordinates of samples and visual differences) are available from the authors. While AUDI2000 was the best formula predicting our visual results, it seems that this formula can be improved. Further tests of AUDI2000 or other potential future color-difference formulas including flop effects are encouraged.

Acknowledgments

To all volunteer observers participating in our experiment. We are also very grateful to BYK Additive and Instruments (https://www.byk.com/en/instruments/products/color-measurement.html), for the loan of the byko-spectra effect light booth used in our experiments. Thanks also to the Color in Informatics and Media Technology (CIMET) Erasmus-Mundus Master (http://www.master-erasmusmundus-color.eu/); the results of this paper and references [21

21. M. Melgosa, L. Gómez-Robledo, J. Martínez-García, E. Perales, F. M. Martínez-Verdú, and T. Dauser, “Testing a colour-difference formula for the automotive industry using the experimental visual datasets employed in CIEDE2000 development,” in Proceedings CIE Centenary Conference (CIE, Paris, 2013), CIE x038:2013 Publication, pp. 465–469.

] and [22

22. J. Martínez-García, M. Melgosa, L. Gómez-Robledo, C. Li, M. Huang, H. Liu, G. Cui, M. R. Luo, and T. Dauser, “Testing the AUDI2000 colour-difference formula for solid colours using some visual datasets with usefulness to automotive industry,” in Proceedings 8thIberoamerican Optics Meeting and 11th Latin American Meeting on Optics, Lasers and Applications (Proc. SPIE Vol. 8785, http://proceedings.spiedigitallibrary.org/proceeding.aspx?articleid=1782351). [CrossRef]

], constituted Juan Martínez-Garcia’s CIMET Master Thesis. This research was supported by the Ministry of Economy and Competitiveness (Spain), research projects DPI2011-30090-C02 and FIS2010-19839, with European Regional Development Fund.

References and links

1.

M. Melgosa, A. Trémeau, and G. Cui, Advanced Color Image Processing and Analysis (Springer, 2013), Chap. 3.

2.

W. V. Longley, “Automotive color certification,” Color Res. Appl. 20(1), 50–54 (1995). [CrossRef]

3.

E. J. J. Kirchner and J. Ravi, “Setting tolerances on color and texture for automotive coatings,” Color Res. Appl. , 39(1), 88–98 (2012).

4.

M. Melgosa, “Testing CIELAB-based color-difference formulas,” Color Res. Appl. 25(1), 49–55 (2000). [CrossRef]

5.

Commission Internationale de l’Éclairage (CIE), Improvement to industrial colour-difference evaluation, CIE Publication 142–2001 (CIE Central Bureau, Vienna, 2001).

6.

Commission Internationale de l’Éclairage (CIE), Colorimetry - Part 6: CIEDE2000 colour-difference formula, CIE S 014–6 E:2013 (CIE Central Bureau, Vienna, 2013).

7.

R. G. Kuehni, “CIEDE2000, milestone or final answer?” Color Res. Appl. 27(2), 126–127 (2002). [CrossRef]

8.

S. Wen, “A color difference metric based on the chromaticity discrimination ellipses,” Opt. Express 20(24), 26441–26447 (2012), http://www.opticsinfobase.org/oe/abstract.cfm?uri=oe-20-24-26441. [CrossRef] [PubMed]

9.

F. J. J. Clarke, R. McDonald, and B. Rigg, “Modification to the JPC79 colour-difference formula,” J. Soc. Dyers Colour. 100(4), 128–132 (1984). [CrossRef]

10.

Commission Internationale de l’Éclairage (CIE), Industrial Colour-difference evaluation, CIE Publication 116–1995 (CIE Central Bureau, Vienna, 1995).

11.

G. Cui, M. R. Luo, B. Rigg, G. Roesler, and K. Witt, “Uniform colour spaces based on the DIN99 colour-difference formula,” Color Res. Appl. 27(4), 282–290 (2002). [CrossRef]

12.

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]

13.

C. Oleari, M. Melgosa, and R. Huertas, “Euclidean color-difference formula for small-medium color differences in log-compressed OSA-UCS space,” J. Opt. Soc. Am. A 26(1), 121–134 (2009). [CrossRef] [PubMed]

14.

M. Melgosa, R. Huertas, and R. S. Berns, “Performance of recent advanced color-difference formulae using the Standardized Residual Sum of Squares index,” J. Opt. Soc. Am. A 25(7), 1828–1834 (2008). [CrossRef]

15.

Z. Huang, H. Xu, M. R. Luo, G. Cui, and H. Feng, “Assessing total differences for effective samples having variations in color coarseness and glint,” Chin. Opt. Lett. 8, 717–720 (2010). [CrossRef]

16.

Z. Huang, H. Xu, and M. R. Luo, “Camera-based model to predict the total difference between effect coatings under directional illumination,” Chin. Opt. Lett. 9, 093301 (2011). [CrossRef]

17.

Commission Internationale de l’Éclairage (CIE), Methods for evaluating colour differences in images, CIE Publication 199–2011 (CIE Central Bureau, Vienna, 2011).

18.

DIN 6175–2, “Farbtoleranzen für Automobillackierungen – Teil 2: Effektlackierungen,” Deutsches Institut für Normung eV, Berlin, 2001–03.

19.

T. Dauser, AUDI AG, I/PG-C41, 85045 Ingolstadt, Germany (personal communication, 2012). See http://www.detroitcc.org/files/Color%20Management%20at%20AUDI%20(DCC%20March%202012).pdf

20.

American Society for Testing and Materials (ASTM), “Standard terminology of appearance,” ASTM E 284, 95a (1995).

21.

M. Melgosa, L. Gómez-Robledo, J. Martínez-García, E. Perales, F. M. Martínez-Verdú, and T. Dauser, “Testing a colour-difference formula for the automotive industry using the experimental visual datasets employed in CIEDE2000 development,” in Proceedings CIE Centenary Conference (CIE, Paris, 2013), CIE x038:2013 Publication, pp. 465–469.

22.

J. Martínez-García, M. Melgosa, L. Gómez-Robledo, C. Li, M. Huang, H. Liu, G. Cui, M. R. Luo, and T. Dauser, “Testing the AUDI2000 colour-difference formula for solid colours using some visual datasets with usefulness to automotive industry,” in Proceedings 8thIberoamerican Optics Meeting and 11th Latin American Meeting on Optics, Lasers and Applications (Proc. SPIE Vol. 8785, http://proceedings.spiedigitallibrary.org/proceeding.aspx?articleid=1782351). [CrossRef]

23.

M. Melgosa, “Request for existing experimental datasets on color differences,” Color Res. Appl. 32(2), 159 (2007). [CrossRef]

24.

O. Gómez, F. J. Burgos, E. Perales, E. Chorro, V. Viqueira, F. M. Martínez-Verdú, and J. Pujol, “Preliminary comparative performance of the AUDI2000 and CIEDE2000 color difference formulas by visual assessments in directional lighting booth,” in Proceedings AIC Colour 2013 (Newcastle, UK, 2013), 4, pp. 1545–1548.

25.

American Society for Testing and Materials (ASTM), Standard practice for specifying the geometry of multiangle spectrophotometers, Technical Report ASTM E2194.01 (2011).

26.

Commission Internationale de l’Éclairage (CIE), Colorimetry, 3rd Edition, CIE Publication 15:2004 (CIE Central Bureau, Vienna, 2004).

27.

S. Guan and M. R. Luo, “Investigation of parametric effects using small colour-differences,” Color Res. Appl. 24(5), 331–343 (1999). [CrossRef]

28.

http://www.sdcenterprises.co.uk/products/testing.asp See also ISO 105–A02:1993, “Test for colour fastness – Part A02: Gray scale for assessing change in colour,” International Organization for Standardization (Geneva, Switzerland).

29.

F. J. Burgos, E. Perales, O. Gómez, E. Chorro, V. Viqueira, F. M. Martínez-Verdú, and J. Pujol, “Instrumental and visual correlation between a multiangle spectrophotometer and a directional lighting booth,” in Proceedings AIC Colour 2013 (Newcastle, UK, 2013), 4, pp. 1373–1376.

30.

A. Ferrero, A. M. Rabal, J. Campos, A. Pons, and M. L. Hernanz, “Spectral and geometrical variation of the bidirectional reflectance distribution function of diffuse reflectance standards,” Appl. Opt. 51(36), 8535–8540 (2012). [CrossRef] [PubMed]

31.

M. Huang, H. Liu, G. Cui, M. R. Luo, and M. Melgosa, “Evaluation of threshold color differences using printed samples,” J. Opt. Soc. Am. A 29(6), 883–891 (2012). [CrossRef] [PubMed]

32.

Commission Internationale de l’Éclairage (CIE), CIE Colorimetry – Part 5: CIE 1976 L*u*v* Colour Space and u’,v’ Uniform Chromaticity Diagram, ISO 11664–5:2009(E) / CIE S 014–5 E:2009 (CIE Central Bureau, Vienna, 2009).

33.

Commission Internationale de l’Éclairage (CIE), CIE Colorimetry – Part 4: 1976 L*a*b* Colour Space, ISO 11664–4:2008(E) / CIE S 014–4 E:2007 (CIE Central Bureau, Vienna, 2007).

34.

M. R. Luo and B. Rigg, “BFD(l:c) colour-difference formula. Part 1 – Development of the formula,” J. Soc. Dyers Colour. 103(2), 86–94 (1987). [CrossRef]

35.

P. A. García, R. Huertas, M. Melgosa, and G. Cui, “Measurement of the relationship between perceived and computed color differences,” J. Opt. Soc. Am. A 24(7), 1823–1829 (2007). [CrossRef] [PubMed]

36.

M. Melgosa, P. A. García, L. Gómez-Robledo, R. Shamey, D. Hinks, G. Cui, and M. R. Luo, “Notes on the application of the standardized residual sum of square index for the assessment of intra- and inter-observer variability in color-difference experiments,” J. Opt. Soc. Am. A 28(5), 949–953 (2011). [CrossRef]

37.

R. S. Berns and B. Hou, “RIT-DuPont supra-threshold color-tolerance individual color-difference pair dataset,” Color Res. Appl. 35(4), 274–283 (2010). [CrossRef]

38.

G. G. Attridge and M. R. Pointer, “Some aspects of the visual scaling of large colour differences – II,” Color Res. Appl. 25(2), 116–122 (2000). [CrossRef]

39.

M. Huang, H. Liu, G. Cui, M. R. Luo, N. Liao, M. Melgosa, Y. Zhang, and C. Zheng, “Assessing color differences in a wide range of magnitudes,” Proceedings AIC Colour 2013 (Newcastle, UK, 2013), 4, 1533–1536.

OCIS Codes
(330.1690) Vision, color, and visual optics : Color
(330.1710) Vision, color, and visual optics : Color, measurement
(330.1730) Vision, color, and visual optics : Colorimetry

ToC Category:
Vision, Color, and Visual Optics

History
Original Manuscript: October 10, 2013
Revised Manuscript: December 23, 2013
Manuscript Accepted: January 3, 2014
Published: February 6, 2014

Virtual Issues
Vol. 9, Iss. 4 Virtual Journal for Biomedical Optics

Citation
Manuel Melgosa, Juan Martínez-García, Luis Gómez-Robledo, Esther Perales, Francisco M. Martínez-Verdú, and Thomas Dauser, "Measuring color differences in automotive samples with lightness flop: A test of the AUDI2000 color-difference formula," Opt. Express 22, 3458-3467 (2014)
http://www.opticsinfobase.org/oe/abstract.cfm?URI=oe-22-3-3458


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References

  1. M. Melgosa, A. Trémeau, and G. Cui, Advanced Color Image Processing and Analysis (Springer, 2013), Chap. 3.
  2. W. V. Longley, “Automotive color certification,” Color Res. Appl. 20(1), 50–54 (1995). [CrossRef]
  3. E. J. J. Kirchner, J. Ravi, “Setting tolerances on color and texture for automotive coatings,” Color Res. Appl., 39(1), 88–98 (2012).
  4. M. Melgosa, “Testing CIELAB-based color-difference formulas,” Color Res. Appl. 25(1), 49–55 (2000). [CrossRef]
  5. Commission Internationale de l’Éclairage (CIE), Improvement to industrial colour-difference evaluation, CIE Publication 142–2001 (CIE Central Bureau, Vienna, 2001).
  6. Commission Internationale de l’Éclairage (CIE), Colorimetry - Part 6: CIEDE2000 colour-difference formula, CIE S 014–6 E:2013 (CIE Central Bureau, Vienna, 2013).
  7. R. G. Kuehni, “CIEDE2000, milestone or final answer?” Color Res. Appl. 27(2), 126–127 (2002). [CrossRef]
  8. S. Wen, “A color difference metric based on the chromaticity discrimination ellipses,” Opt. Express 20(24), 26441–26447 (2012), http://www.opticsinfobase.org/oe/abstract.cfm?uri=oe-20-24-26441 . [CrossRef] [PubMed]
  9. F. J. J. Clarke, R. McDonald, B. Rigg, “Modification to the JPC79 colour-difference formula,” J. Soc. Dyers Colour. 100(4), 128–132 (1984). [CrossRef]
  10. Commission Internationale de l’Éclairage (CIE), Industrial Colour-difference evaluation, CIE Publication 116–1995 (CIE Central Bureau, Vienna, 1995).
  11. G. Cui, M. R. Luo, B. Rigg, G. Roesler, K. Witt, “Uniform colour spaces based on the DIN99 colour-difference formula,” Color Res. Appl. 27(4), 282–290 (2002). [CrossRef]
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