Color Ratios and Chromatic Adaptation

In this paper, the performance of chromatic adaptation transforms based on stable color ratios is investigated.It was found that for three different sets of reflectance data, their performance was not statistically different from CMCCAT2000,when applying the chromatic adaptation transforms to Lam’s corresponding color data set and using a perceptual error metric of CIE Delta E94.The sensors with the best color ratio stability are much sharper and more de-correlated than the CMCCAT2000 sensors, corresponding better to sensor responses found in other psychovisual studies.The new sensors also closely match those used by the sharp adaptation transform

Optimization for Hue Constant RGB Sensors

We present an optimization technique to find hue constant RGB sensors. The hue representation is based on a log RGB opponent color space that is invariant to brightness and gamma. While modeling the visual response did not derive the opponent space, the hue definition is similar to the ones found in CIE Lab and IPT. Finding hue constant RGB sensors through this optimization might be applicable in color engineering applications such as finding RGB sensors for color image encodings

Computing Chromatic Adaptation

Most of today’s chromatic adaptation transforms (CATs) are based on a modified form of the von Kries chromatic adaptation model, which states that chromatic adaptation is an independent gain regulation of the three photoreceptors in the human visual system. However, modern CATs apply the scaling not in cone space, but use “sharper” sensors, i.e. sensors that have a narrower shape than cones. The recommended transforms currently in use are derived by minimizing perceptual error over experimentally obtained corresponding color data sets. We show that these sensors are still not optimally sharp. Using different computational approaches, we obtain sensors that are even more narrowband. In a first experiment, we derive a CAT by using spectral sharpening on Lam’s corresponding color data set. The resulting Sharp CAT, which minimizes XYZ errors, performs as well as the current most popular CATs when tested on several corresponding color data sets and evaluating perceptual error. Designing a spherical sampling technique, we can indeed show that these CAT sensors are not unique, and that there exist a large number of sensors that perform just as well as CAT02, the chromatic adaptation transform used in CIECAM02 and the ICC color management framework. We speculate that in order to make a final decision on a single CAT, we should consider secondary factors, such as their applicability in a color imaging workflow. We show that sharp sensors are very appropriate for color encodings, as they provide excellent gamut coverage and hue constancy. Finally, we derive sensors for a CAT that provide stable color ratios over different illuminants, i.e. that only model physical responses, which still can predict experimentally obtained appearance data. The resulting sensors are sharp

Traitement logarithmique d'images couleur

Cette thèse de doctorat porte sur l'extension du modèle LIP (Logarithmic Image Processing) aux images en couleurs. Le modèle CoLIP (Color Logarithmic Image Processing) est défini, étudié et appliqué au traitement d'image dans ce manuscrit.Le modèle LIP est un cadre mathématique original complet, développé pour le traitement d'images à niveaux de gris, rigoureusement établi mathématiquement, compatible avec les lois physiques de formation d'image, et mieux adapté que l'approche classique pour modéliser la perception visuelle humaine de l'intensité de la lumière. Après une étude de la vision des couleurs et de la science des couleurs, le modèle CoLIP est construit en suivant les étapes de la perception humaine des couleurs, tout en intégrant le cadre mathématique du modèle LIP. Dans un premier temps, le CoLIP est construit en suivant les étapes de la photoréception, de la compression lumineuse et du codage antagoniste. Il est donc développé comme un espace couleur représentant une image couleur par un ensemble de trois fonctions de tons antagonistes, sur lesquelles sont définies les opérations CoLIP d'addition et de multiplication par un scalaire, qui confèrent à cet espace couleur la structure d'espace vectoriel couleur. Ensuite, l'espace couleur CoLIP étant un espace de type luminance-chrominance uniforme, les attributs relatifs et absolus de la perception humaine des couleurs (teinte, chroma, coloration, luminosité, clarté, et saturation) peuvent être définis. Cette construction fait du CoLIP à la fois un espace vectoriel couleur bien structuré mathématiquement, et un modèle d'apparence couleur. Dans un deuxième temps, un grand nombre de justifications physiques, mathématiques, et psychophysiques du modèle CoLIP sont proposées, notamment la comparaison des formes des ellipses de MacAdam dans l'espace de couleur uniforme CoLIP et dans d'autres modèles uniformes, sur des critères d'aire et d'excentricité des ellipses. Enfin, diverses applications utilisant la structure d'espace vectoriel couleur du modèle CoLIP sont proposées, telles que le rehaussement de contraste, le rehaussement d'image et la détection de contour. Des applications utilisant la structure de modèle d'apparence couleur, qui permet de travailler sur les notions de teinte, de luminosité et de saturation, sont également développées. Une application spécifique permettant de mesurer la viabilité des cellules sur des images de lames obtenues par cytocentrifugation et marquage couleur est également proposée.This doctoral thesis introduces the extension of the LIP (Logarithmic Image Processing) model to color images. The CoLIP (Color Logarithmic Image Processing) model is defined, studied and applied to image processing in this manuscript. The Logarithmic Image Processing (LIP) approach is a mathematical framework developed for the representation and processing of images valued in a bounded intensity range. The LIP theory is physically and psychophysically well justified since it is consistent with several laws of human brightness perception and with the multiplicative image formation model. Following a study of color vision and color science, the CoLIP model is constructed according to the human color perception stages, while integrating the mathematical framework of the LIP.Initially, the CoLIP is constructed by following the photoreception, non-linear cone compression, and opponent processing human color perception steps. It is developed as a color space representing a color image by a set of three antagonists tones functions, that can be combined by means of specific CoLIP operations: addition, scalar multiplication, and subtraction, which provide to the CoLIP framework a vector space structure. Then, as the CoLIP color space is a luminance-chrominance uniform color space, relative and absolute perception attributes (hue, chroma, colorfulness, brightness, lightness, and saturation) can be defined. Thus, the CoLIP framework combines advantages of a mathematically well structured vector space, and advantages of a color appearance model. In a second step, physical, mathematical, physiological and psychophysical justifications are proposed including a comparison of MacAdam ellipses shapes in the CoLIP uniform model, and in other uniform models, based on ellipses area and eccentricity criterions. Finally, various applications using the CoLIP vector space structure are proposed, such as contrast enhancement, image enhancement and edge detection. Applications using the CoLIP color appearance model structure, defined on hue, brightness and saturation criterions are also proposed. A specific application dedicated to the quantification of viable cells from samples obtained after cytocentrifugation process and coloration is also presented.ST ETIENNE-ENS des Mines (422182304) / SudocSudocFranceF

Color Ratios and Chromatic Adaptation

In this paper, the performance of chromatic adaptation transforms based on stable color ratios is investigated. It was found that for three different sets of reflectance data, their performance was not statistically different from CMCCAT2000, when applying the chromatic adaptation transforms to Lam’s corresponding color data set and using a perceptual error metric of CIE ?E94. The sensors with the best color ratio stability are much sharper and more decorrelated than the CMCCAT2000 sensors, corresponding better to sensor responses found in other psychovisual studies. The new sensors also closely match those used by the sharp adaptation transform