White point

Abstract: The CIE Technical Committee 8-01, color appearance models for color management applications, has recently proposed a single set of revisions to the CIECAM97s color appearance model This new model, called CIECAM02, is based on CIECAM97s but includes many revisions1-4 and some simplifications A partial list of revisions includes a linear chromatic adaptation transform, a new non-linear response compression function and modifications to the calculations for the perceptual attribute correlates The format of this paper is an annotated description of the forward equations for the model Introduction The CIECAM02 color appearance model builds upon the basic structure and form of the CIECAM97s5,6 color appearance model This document describes the single set of revisions to the CIECAM97s model that make up the CIECAM02 color appearance model There were many, often conflicting, considerations such as compatibility with CIECAM97s, prediction performance, computational complexity, invertibility and other factors The format for this paper will differ from previous papers introducing a color appearance model Often a general description of the model is provided, then discussion about its performance and finally the forward and inverse equations are listed separately in an appendix Performance of the CIECAM02 model will be described elsewhere7 and for the purposes of brevity this paper will focus on the forward model Specifically, this paper will attempt to document the decisions that went into the design of CIECAM02 For a complete description of the forward and inverse equations, as well as usage guidelines, interested readers are urged to refer to the TC 8-01 web site8 or to the CIE for the latest draft or final copy of the technical report This paper is not intended to provide a definitive reference for implementing CIECAM02 but as an introduction to the model and a summary of its structure Data Sets The CIECAM02 model, like CIECAM97s, is based primarily on a set corresponding colors experiments and a collection of color appearance experiments The corresponding color data sets9,10 were used for the optimization of the chromatic adaptation transform and the D factor The LUTCHI color appearance data11,12 was the basis for optimization of the perceptual attribute correlates Other data sets and spaces were also considered The NCS system was a reference for the e and hue fitting The chroma scaling was also compared to the Munsell Book of Color Finally, the saturation equation was based heavily on recent experimental data13 Summary of Forward Model A color appearance model14,15 provides a viewing condition specific means for transforming tristimulus values to or from perceptual attribute correlates The two major pieces of this model are a chromatic adaptation transform and equations for computing correlates of perceptual attributes, such as brightness, lightness, chroma, saturation, colorfulness and hue The chromatic adaptation transform takes into account changes in the chromaticity of the adopted white point In addition, the luminance of the adopted white point can influence the degree to which an observer adapts to that white point The degree of adaptation or D factor is therefore another aspect of the chromatic adaptation transform Generally, between the chromatic adaptation transform and computing perceptual attributes correlates there is also a non-linear response compression The chromatic adaptation transform and D factor was derived based on experimental data from corresponding colors data sets The non-linear response compression was derived based on physiological data and other considerations The perceptual attribute correlates was derived by comparing predictions to magnitude estimation experiments, such as various phases of the LUTCHI data, and other data sets, such as the Munsell Book of Color Finally the entire structure of the model is generally constrained to be invertible in closed form and to take into account a sub-set of color appearance phenomena Viewing Condition Parameters It is convenient to begin by computing viewing condition dependent constants First the surround is selected and then values for F, c and Nc can be read from Table 1 For intermediate surrounds these values can be linearly interpolated2 Table 1 Viewing condition parameters for different surrounds Surround F c Nc Average 10 069 10 Dim 09 059 095 Dark 08 0525 08 The value of FL can be computed using equations 1 and 2, where LA is the luminance of the adapting field in cd/m2 Note that this two piece formula quickly goes to very small values for mesopic and scotopic levels and while it may resemble a cube-root function there are considerable differences between this two-piece function and a cube-root as the luminance of the adapting field gets very small ! k =1/ 5L A +1 ( ) (1) ! F L = 02k 4 5L A ( ) + 01 1" k4 ( ) 2 5L A ( ) 1/ 3 (2) The value n is a function of the luminance factor of the background and provides a very limited model of spatial color appearance The value of n ranges from 0 for a background luminance factor of zero to 1 for a background luminance factor equal to the luminance factor of the adopted white point The n value can then be used to compute Nbb, Ncb and z, which are then used during the computation of several of the perceptual attribute correlates These calculations can be performed once for a given viewing condition

Abstract: A method for transforming three color input signals (R, G, B) corresponding to three gamut defining color primaries to four color output signals (R′, G′, B′, W) corresponding to the gamut defining color primaries and one additional color primary W for driving a display having a white point different from W includes the steps of: normalizing the color input signals (R,G,B) such that a combination of equal amounts in each signal produces a color having XYZ tristimulus values identical to those of the additional color primary to produce normalized color signals (Rn,Gn,Bn); calculating a common signal S that is a function F1 of the three normalized color signals (Rn,Gn,Bn); calculating a function F2 of the common signal S and adding it to each of the three normalized color signals (Rn,Gn,Bn) to provide three color signals (Rn′,Gn′,Bn′); normalizing the three color signals (Rn′,Gn′,Bn′) such that a combination of equal amounts in each signal produces a color having XYZ tristimulus values identical to those of the display white point to produce three of the four color output signals (R′,G′,B′); and calculating a function F3 of the common signal S and assigning it to the fourth color output signal W.

Abstract: An LED light source for LCD backlighting is described that recalibrates itself over time so that color and brightness uniformity across the backlight is maintained over the life of the backlight. The backlight contains clusters of red, green, and blue LEDs, each cluster generating a white point. In one embodiment, each color in a cluster has its own controllable driver so that the brightness of each color is a cluster is separately controllable. One or more optical sensors are arranged in the backlight, and the sensor signals are detected by processing circuitry to sense the light output of any LEDs that are energized in a single cluster. The measured white point and flux are compared to a stored target white point value and flux for that cluster. The currents to the RGB LEDs are then automatically adjusted to achieve the target level for each cluster. This process is applied to each cluster in sequence until the recalibration is complete. The recalibration takes place at various times over the lifetime of the backlight to offset the effects of LED degradation over time. Variations of this technique are also described.

Abstract: White organic light emitting diodes harvesting triplet excitons from the fluorescent blue emitter N,N′-di-1-naphthalenyl-N,N′-diphenyl-[1,1′:4′,1″:4″,1‴-quaterphenyl]-4,4‴-diamine (4P-NPD) are presented. Direct doping of the phosphorescent orange iridium(III)bis(2-methyldibenzo-[f,h]quinoxaline)(acetylacetonate) into 4P-NPD results in a strongly reduced efficiency roll-off as compared to separate emission layers, and yields 49.3lmW−1 total external power efficiency (24.1% quantum efficiency) at a luminance of 1000cdm−2 [CIE 1931 chromaticity coordinates (0.49,0.41)], measured in an integrating sphere. Introduction of an exciton balancing interlayer improves the chromaticity (0.43,0.43) toward the CIE illuminant A warm white point and keeps a high efficiency of 40.7lmW−1, 20.3%.