Colour-difference formulas are tools employed in colour industries for objective pass/fail decisions of manufactured products. These objective decisions are based on instrumental colour measurements which must reliably predict the subjective colour-difference evaluations performed by observers’ panels. In a previous paper we have tested the performance of different colour-difference formulas using the datasets employed at the development of the last CIErecommended colour-difference formula CIEDE2000, and we found that the AUDI2000 colour-difference formula for solid (homogeneous) colours performed reasonably well, despite the colour pairs in these datasets were not similar to those typically employed in the automotive industry (CIE Publication x038:2013, 465-469). Here we have tested again AUDI2000 together with 11 advanced colour-difference formulas (CIELUV, CIELAB, CMC, BFD, CIE94, CIEDE2000, CAM02-UCS, CAM02-SCD, DIN99d, DIN99b, OSA-GP-Euclidean) for three visual datasets we may consider particularly useful to the automotive industry because of different reasons: 1) 828 metallic colour pairs used to develop the highly reliable RIT-DuPont dataset (Color Res. Appl. 35, 274-283, 2010); 2) printed samples conforming 893 colour pairs with threshold colour differences (J. Opt. Soc. Am. A 29, 883-891, 2012); 3) 150 colour pairs in a tolerance dataset proposed by AUDI. To measure the relative merits of the different tested colour-difference formulas, we employed the STRESS index (J. Opt. Soc. Am. A 24, 1823-1829, 2007), assuming a 95% confidence level. For datasets 1) and 2), AUDI2000 was in the group of the best colour-difference formulas with no significant differences with respect to CIE94, CIEDE2000, CAM02-UCS, DIN99b and DIN99d formulas. For dataset 3) AUDI2000 provided the best results, being statistically significantly better than all other tested colour-difference formulas.
An experiment with EIZO CG 19, DELL 19, IBM 19 and HP 19 LCD was designed and carried out to test the
interaction between RGB channels, and then to test the spectral additive property of LCDs. The results show that the
interaction between channels is very weak and spectral additivity is held well. This result indicates that the manufacture
technology of LCDs is improved greatly. But the computation results of tristimuli addition are not very accurate. A new
calculation method based on spectral additivity, in which gamma is fitted by a cubic polynomial in each piece of
wavelength, is proposed and discussed. The proposed method is proved simple and very few samples need to measure
while the computation precision is very high.
Neugebauer equations are basic formulas to calculate colors of print. But there is obvious deviation between calculation and measurement due to the complexity of printing process. Some methods of improving its precision are reviewed and an improved spectral Neugebauer method is proposed in this paper, which is based on spectral dot gain correction. There are two kinds of dot gains, one is mechanical dot gain and another is optical dot gain. Both of them are the key factors affecting calculated results of Neugebauer equations. It is found that the optical dot gain is a function of wavelength, so that the correction should also be a function of wavelength. After measuring spectral curves of paper substrate, primaries and their mixture, spectral dot gain can be calculated and Neugebauer equations can be corrected with it. The forwards color lookup table is got directly from the improved Neugebauer equations, the reverse color lookup table is calculated by interpolation from forwards color lookup table (LUT).