We report on some recent advances in industrial color-difference evaluation focused in three main fields: Development of reliable experimental visual datasets; proposal of new color spaces and color-difference formulas; tools to evaluate the merits of color-difference formulas. The use of fuzzy techniques to assign consistency degrees to color pairs in combined visual datasets is described. The CIE/ISO joint proposal of the CIEDE2000 color-difference formula as a standard will facilitate the communication among companies and users. The CIE recommendation of the STRESS index to assess observers’ variability and relative merits of different color-difference formulas is reported. Power functions are an efficient method to improve the performance of modern color-difference formulas. We need of advanced color-difference formulas accounting for new materials with different kind of textures and gonioapparent effects.
Academic results depend strongly on the individual circumstances of students: background, motivation and aptitude. We think that academic activities conducted to increase motivation must be tuned to the special situation of the students. Main goal of this work is analyze the students in the first year of the Degree in Optics and Optometry in the University of Granada and the suitability of an activity designed for those students. Initial data were obtained from a survey inquiring about the reasons to choose this degree, their knowledge of it, and previous academic backgrounds. Results show that: 1) the group is quite heterogeneous, since students have very different background. 2) Reasons to choose the Degree in Optics and Optometry are also very different, and in many cases were selected as a second option. 3) Knowledge and motivations about the Degree are in general quite low. Trying to increase the motivation of the students we designed an academic activity in which we show different topics studied in the Degree. Results show that students that have been involved in this activity are the most motivated and most satisfied with their election of the degree.
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.