In the world of lighting engineering, one of the most active areas of research and industrial application is in the definition of the color rendering properties of light sources. There is a current international standard, and several new methods have been proposed over the last decade. Ordinary consumers are frequently left with little or no knowledge of how to interpret the numerical data produced by any of these systems. This situation has been exacerbated with the advent of LED light sources with widely differing properties. Certain LEDs yield very different results depending on the particular metric in use. We have designed a color graphical system that allows a user to pick a set of (typically) 16 surface color samples, and to be given a realistic comparison of the colors when illuminated by two different light sources, shown on a side-by-side display on a color monitor. This provides a visual analogy to the computations built into the above-mentioned metrics, all of which are based on comparison techniques. This chapter will provide an insight into the design and operation of our lighting computer graphics visualization system. Mention will also be made of similar systems that may be found in the published literature.
Part of the book: Computer Graphics and Imaging
The advent of light-emitting diode (LED) light sources has led to a new freedom in the design of light-source spectra, and it is now possible to optimise for different source performance parameters, which is the principal aim of the authors’ work. LEDs and lasers are real or potential light sources, and are inherently monochromatic, that is, narrow-band sources, with typical optical bandwidths in the range 20–40 nm (nanometres) for LEDs and 1–5 nm for diode lasers. Mixtures of three or more can be used to produce nominally white light of the type acceptable for general purpose lighting. It is a characteristic of all types of sources that there is a trade-off between good colour properties and high efficiencies, and the methods described here are directed towards an optimum combination of such parameters. This chapter will explain the use of differential evolution (DE) as a highly effective heuristic approach to optimisation, and proceeds to explain the structure and operation of a DE algorithm designed as an optimisation tool for such purposes.