Thanks for sticking with us—this is the sixth and final post about the NCCA Color Project experiment we conducted at METALCON. In the previous five posts, we presented our analyses of the 28 observers’ ratings to see how discerning and consistent they were. We concluded that human observers see color differences differently; some see a lot of difference, some just a little. This was not unexpected. Finally, it’s time to look at the observed color differences plotted against the machine readings for color difference.
Let’s quickly review the previous two posts on NCCA’s “The Color Project”:
- In Part Three, we showed that about 20% of our observers fell into an “extremes” category (i.e., they were either notably far less critical or far more critical); but the majority—80%—of the observers were more or less in agreement.
- In Part Four, we concluded that most of the people, most of the time, were not fooled by the identical-pair panels. Only 9% of the time were there notable color differences declared, and half of the observers saw no difference at all. If you expect to see a color difference, by golly, you will!
In the last post, Part One, we left off with two facts: We depend on a numerical description of color and color difference rather than judging a sample vs. a standard visually; and NCCA began to investigate ΔE2000 to determine how well it might work in the coil industry.
Let’s start Part Two with a short discussion on ΔE2000. It is way more than the usual ΔE with a little “2000” as a subscript. (If only it were that easy.) Our current ΔE is a straightforward root-mean-squared calculation, as shown here:
ΔEHunter = [(L2-L1)2 + (a2-a1)2 + (b2-b1)2]1/2
NCCA has been investigating an alternative method for color measurement for the coil coating industry. As part of this investigation, NCCA is coordinating a visual assessment experiment. In a nutshell, we are attempting to assess the human response to slight color differences between pairs of panels and to correlate that response to a color instrument’s reading. Of course, people see color and color differences differently, and color instruments have a host of setup options from which to choose, so this is hardly a straightforward experiment. But if it were simple, it would have been done decades ago. Continue reading