Color Appearance Study under Two Lightings Having Different Illuminance Levels

Department of Human Factors EngineeringColor appearances of the objects are changing, depending on light sources. In everyday life, it is common to see a scene having two or more light sources together, or to look at an object that is shadowed by other objects. However, these situations might not be interpreted by current color appearance models, which are based on a single light source. With increasing interest in color reproduction of high dynamic scenes, color appearance research that can explain these multi illumination situations is necessary. In this research, it was intended to explain color appearance phenomena in the context where observers alternately saw two light sources having largely different illuminance levels (7005 lux and 376 lux, respectively) being present at the same time. This study also attempted to identify the observer's state of adaptation in the presence of multiple lightings, by exploring how color appearance in terms of hue, brightness and colorfulness changes in complex multiple lighting conditions, as opposed to single lighting conditions. Psychophysical experiment based on magnitude estimation technique was conducted to estimate color appearance and was composed of four sessions according to 1) illuminance of lighting either high or low, and 2) observer's adaptation to the lighting conditions for either single lighting or multiple lightings. Seven observers who were skillfully trained for color appearance estimation participated in the experiment and evaluated the color appearance of 50 color patches in terms of hue, colorfulness and brightness throughout the four sessions. As for the analyses of the results, human color perception data regarding hue, brightness and colorfulness of all observers were averaged and compared across sessions, based on the illuminance of lighting and the observer???s adaptation to the lighting conditions. Also, the color appearance model CIECAM02 performance was evaluated in terms of hue, brightness and colorfulness by comparing model prediction data with color perception data. As a result, through the color appearance study under two lightings with different illuminance levels, it turned out that hue appearance was not affected by the illuminance level of lighting and the observer???s adaptation to the lighting conditions. Perceived brightness and colorfulness were increased under higher illuminance level, but not affected by the observer's adaptation to the lighting conditions, explaining that observers locally adapted to the lighting where the color was directly shown. It was also found that the CIECAM02 H adeptly predicted hue appearance regardless of the illuminance level of lighting and the observer's adaptation to the lighting conditions. However, the CIECAM02 Q and the CIECAM02 M were overestimated under high illuminance lighting. The modification of the luminance-level adaptation factor, FL, by lowering the value from 1.20 to 0.67, helped the model not to overestimate Q and M. These results are based on color appearance perception when there are only two lightings having 19 times the illuminance difference. Consequently, it cannot be firmly concluded that these phenomena are common color appearance in multiple lighting environments. Therefore, it is necessary to conduct additional color appearance estimation research in multiple lighting conditions having more diverse illuminance level differences or having different configurations of lightings.clos

The LLAB model for quantifying colour appearance

A reliable colour appearance model is desired by industry to achieve high colour fidelity between images produced using a range of different imaging devices. The aim of this study was to derive a reliable colour appearance model capable of predicting the change of perceived attributes of colour appearance under a wide range of media/viewing conditions. The research was divided into three parts: characterising imaging devices, conducting a psychophysical experiment, and developing a colour appearance model. Various imaging devices were characterised including a graphic art scanner, a Cromalin proofing system, an IRIS ink jet printer, and a Barco Calibrator. For the former three devices, each colour is described by four primaries: cyan (C), magenta (M), yellow (Y), and black (K). Three set of characterisation samples (120 and 31 black printer, and cube data sets) were produced and measured for deriving and testing the printing characterisation models. Four black printer algorithms (BPA), were derived. Each included both forward and reverse processes. A 2nd BPA printing model taking into account additivity failure, grey component replacement (GCR) algorithm gave the most accurate prediction to the characterisation data set than the other BPA models. The PLCC (Piecewise Linear interpolation assuming Constant Chromaticity coordinates) monitor model was also implemented to characterise the Barco monitor. The psychophysical experiment was conducted to compare Cromalin hardcopy images viewed in a viewing cabinet and softcopy images presented on a monitor under a wide range of illuminants (white points) including: D93, D65, D50 and A. Two scaling methods: category judgement and paired comparison, were employed by viewing a pair of images. Three classes of colour models were evaluated: uniform colour spaces, colour appearance models and chromatic adaptation transforms. Six images were selected and processed via each colour model. The results indicated that the BFD chromatic transform gave the most accurate predictions of the visual results. Finally, a colour appearance model, LLAB, was developed. It is a combination of the BFD chromatic transform and a modified version of CIELAB uniform colour space to fit the LUTCRI Colour Appearance Data previously accumulated. The form of the LLAB model is much simpler and its performance is more precise to fit experimental data than those of the other models

Quantification of metamerism and colour constancy

Reliable colour constancy by industry for colour conducted to quantify metamerism. and metamerism indices are highly desired quality control. Two experiments were the degree of colour constancy and In the colour constancy experiment, 240 wool samples were prepared and scaled using a magnitude estimation method by a panel of 5 experienced observers under sources D65, A and TL84. 2 corresponding data sets derived from the experimental results were used to test various chromatic adaptation transforms. The results clearly show that the BFD transform gave the most precise prediction than the other transforms. Attempts were also made to derive 4 new transforms from four independent data sets. These gave similar performance as that of the BFD, but overcome the BFO's problem (incapable of predicting some of the high saturated colours). Hence, these transforms should be used with confidence for predicting the degree of colour constancy. This experimental results were also used to test various uniform colour spaces and colour appearance models. The Hunt94 model gave the most precise prediction to the colourfulness and hue results. Modification was made to its lightness scale for improving the fit. In the metamerism experiment, 76 pairs of wool samples were prepared and assessed with 20 observations using a grey scale under 7 sources: D65, A, TL84, TL83, P27, W and WW. The experimental results were used to test 3 types of illuminant metamerism indices derived here. It was found that calculating colour difference using 3 colour difference formulae, i.e. CMC, BFD and CIE94 gave the most precise prediction to the visual results. The degree of precision is quite satisfactory in comparison with typical observer precision. A new standard deviate observer (SDO) was also derived. This together with the CIE SDO and 1964 Observer were tested using the author's and the Obande's data. The results showed that the new SDO predicted results more accurate than those from the other two CIE Observers. An Observer Metamerism Index (OMI) was also derived to indicate the degree of metamerism based upon the new SDO. The results showed that the new SDO was more suitable for indicating the degree of observer metamerism

Quantifying the colour appearance of displays.

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Color difference evaluation for digital pictorial images under various surround conditions

Department of Human and Systems EngineeringNowadays pictorial images are more often shown on displays than on paper. Therefore, displaymanufacturers have been trying to improve the image quality of their displays to increase theirmarket share. To improve the image quality, not only good hardware technology and imageprocessing algorithms but also good color difference equations are needed to predict the overallcolor difference between pictorial images shown on different panels or manipulated using differentimage processing algorithms etc. Color difference equations are also developed for the use ofindustrial purposes. However they were developed in limits of surround condition and applications. The purposes of this research is to clarify the effect of surround condition and magnitude of colordifference on perceptual color difference for complex image. The experiment under four surroundconditions was carried out to achieve these purposes. The collected data by psychophysicalexperiment was used to develop image color difference metric under various surround condition.Before conducting the main experiment, pilot test was conducted to investigate the effect of colordifference magnitude on perceptual image difference. Pilot experiment tested the performance of each conventional color difference equation such asCIE ??E*ab, CMC(l:c) and CIEDE2000 while the psychophysical experiment including wide rangeof color difference stimuli was conducted to evaluate perceptual color difference between originalimage and manipulated image. Twenty observers were participated in the experiment and 195stimuli were used for the magnitude estimation. Main Experiment was investigated how perceptual color difference is shifted by changing surroundluminance level and magnitude of color difference. There are four surround conditions, dark, dim,average and bright. 996 stimuli were prepared for the experiment. 500 randomly selected stimuliwere used for each surround condition. Twenty-three observers were participated in the mainexperiment. They were asked to evaluate the perceived color difference between original andmanipulated images with magnitude estimation method.ope