Analyzing a decade of Colors of the Year

The Color of the Year was first introduced by Pantone in 2000, and recently (the last decade) we saw the trend of introducing a Color of the Year being picked up by more and more companies. Paints and coatings companies typically select their colors of the year by extensive research by designers and trend experts, resulting in a plurality of colors being introduced as Color of the Year, every year. In this article, we collated colors of the year of 15 different paints and coatings companies published in the past decade and we show that most colors of the year can be described as neutral or off‐white color (ie, the median value for NCS Chromaticness is low, 20%) although occasionally colors of the year have high NCS Chromaticness. We demonstrate that the distribution of colors of the year follow a certain narrative from year to year: The average Lightness and Chroma (averaged over all companies, per year) appear to follow a wavelike pattern, where the average Lightness appears to repeat itself every ~8 years and the average Chroma approximately every 4.5 years. Similarly, we can see a cyclic pattern in the hue: From mostly yellowish red or greenish blue in 2015, towards predominantly blue in 2017, to a wide variation in hues in 2020 suggesting a fragmentation in colors of the year preferences. In addition, we demonstrate that the colors of the year differ significantly from what can be expected if the colors would have been selected randomly. This could reflect the fact that paint companies use similar raw data to identify their color trends

Improving Color Accuracy of Colorimetric Sensors

Accurate measurements of reflectance and color require spectrophotometers with prices often exceeding $3000. Recently, new “color instruments” became available with much lower prices, thanks to the availability of inexpensive colorimetric sensors. We investigated the Node+ChromaPro and the Color Muse, launched in 2015 and 2016 by Variable Inc. Both instruments are colorimeters, combining a colorimetric sensor with LED lighting. We investigated color accuracy compared to a high-end spectrophotometer from BYK Gardner. With different sets of samples we find for the Node an average value of dECMC (1:1) = 1.50, and a maximum of 7.86, when comparing with the 45° geometry of the spectrophotometer. Utilizing measurement data on the Spectral Power Distributions of the LEDs, we developed three methods to improve color accuracy as compared to the spectrophotometer data. We used these methods on different sets of samples with various degrees of gloss, both for training the models underlying the methods and for independent tests of model accuracy. Average color accuracy of the Node+ChromaPro improves from dECMC (1:1) = 1.82 to 1.16 with respect to spectrophotometer data. The percentage of samples with dECMC (1:1) < 1.0 increases from 30.9% (uncorrected) to 64%. With the improved color accuracy, these sensors become useful for many more applications

Real-time accurate rendering of color and texture of car coatings

Burlingame, California USA, 13 - 17 January, 2019The digital representation of three dimensional objects with different materials has become common not only in the games and movie industry, but also in designer software, e-commerce and other applications. Although the rendered images often seem to be realistic, a closer look reveals that their color accuracy is often insufficient for critical applications. Storage of the angle-dependent color properties of metallic coatings and other gonio-apparent materials demands large amounts of data. Apart from that, also rendering sparkle, gloss and other texture phenomena is still a subject of active research. Current approaches are computationally very demanding, and require manual ad-hoc setting of many model parameters. In this paper, we describe a new approach to solve these problems. We combine a physics-based approach to make BRDF representation more efficient. We also account for the common loss in color accuracy due to the varying technical specifications of displays., and we correct for the influence from ambient lighting. The rendering framework presented here is shown to be capable of rendering sparkle and gloss as well, based on objective measurement of these properties. This takes out the subjective phase of manual fine-tuning of model parameters that is characteristic for many current rendering approaches. A feasibility test with the new rendering pipeline shows that is indeed able to produce realistic rendering of color, sparkle, gloss and other texture aspects. The computation time is small enough to make the rendering real-time on an iPad 2017, i.e. with low memory footprint and without high demands on graphic card or data storage