A New Metric for Memory Colour Preference Evaluation in Lighting Applications - Experiments, Mathematical Definition, and Comparison With Other Colour Rendering Indices

Abstract

Due to their potential use as an internal reference, memory colours have proven to provide an excellent conceptional approach for the colour rendition evaluation of white light sources in terms of predicting visual appreciation. However, there are still some major drawbacks that can be identified in the principal design of existing memory-based or memory-related colour quality metrics. For this reason, a new experiment was devised trying to overcome the shortcomings of these previous approaches. Based on the experimental output, the main goal of the current study consequently was to derive an improved version of a memory-based colour quality metric, which provides a superior tool for developers and manufacturers that can be used for the optimization of state-of-the-art lighting solutions in cases where visual appreciation and high user acceptability are more important than colour fidelity.

Impact of the adapted white point and the cultural background on memory color assessments

“…Profound knowledge of the underlying dependencies is required to develop—as a long‐term goal—an improved version of a memory‐based color quality metric. A first attempt of defining such a metric based on the results obtained for the two adapting field chromaticities considered here was presented elsewhere 72,76 . However, it should be stressed that the use of only these two adaptation conditions is certainly not enough for covering the huge variety of different lighting situations one is confronted with in real‐world applications.…”

“…In the present case of the color appearance rating experiments conducted at different CCTs of the ambient illumination, the simple model assumes that for each test object the variance of the entire rating data set obtained by pooling the respective results of both adaptation conditions can be explained by a single multivariate Gaussian function defined by seven fit parameters (cf Babilon et al 35,72 ). The more complex model, on the other hand, postulates a separate Gaussian for each adaptation condition to explain the total variance in the rating data leading to a total number of 14 fit parameters, that is, seven for each CCT.…”