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American Meteorological Society Early Online
AMERICAN METEOROLOGICAL SOCIETY Bulletin of the American Meteorological Society EARLY ONLINE RELEASE This is a preliminary PDF of the author-produced manuscript that has been peer-reviewed and accepted for publication. Since it is being posted so soon after acceptance, it has not yet been copyedited, formatted, or processed by AMS Publications. This preliminary version of the manuscript may be downloaded, distributed, and cited, but please be aware that there will be visual differences and possibly some content differences between this version and the final published version. The DOI for this manuscript is doi: 10.1175/BAMS-D-13-00155.1 The final published version of this manuscript will replace the preliminary version at the above DOI once it is available. © 2014 American Meteorological Society Generated using version 3.2 of the official AMS LATEX template 1 Somewhere over the rainbow: How to make effective use of colors 2 in meteorological visualizations ∗ 3 Reto Stauffer, Georg J. Mayr and Markus Dabernig Institute of Meteorology and Geophysics, University of Innsbruck, Innsbruck, Austria 4 Achim Zeileis Department of Statistics, Faculty of Economics and Statistics, University of Innsbruck, Innsbruck, Austria ∗Reto Stauffer, Institute of Meteorology and Geophysics, University of Innsbruck, Innrain 52, A{6020 Innsbruck E-mail: reto.stauff[email protected] 1 5 CAPSULE 6 Effective visualizations have a wide scope of challenges. The paper offers guidelines, a 7 perception-based color space alternative to the famous RGB color space and several tools to 8 more effectively convey graphical information to viewers. 9 ABSTRACT 10 Results of many atmospheric science applications are processed graphically. -
COLOR SPACE MODELS for VIDEO and CHROMA SUBSAMPLING
COLOR SPACE MODELS for VIDEO and CHROMA SUBSAMPLING Color space A color model is an abstract mathematical model describing the way colors can be represented as tuples of numbers, typically as three or four values or color components (e.g. RGB and CMYK are color models). However, a color model with no associated mapping function to an absolute color space is a more or less arbitrary color system with little connection to the requirements of any given application. Adding a certain mapping function between the color model and a certain reference color space results in a definite "footprint" within the reference color space. This "footprint" is known as a gamut, and, in combination with the color model, defines a new color space. For example, Adobe RGB and sRGB are two different absolute color spaces, both based on the RGB model. In the most generic sense of the definition above, color spaces can be defined without the use of a color model. These spaces, such as Pantone, are in effect a given set of names or numbers which are defined by the existence of a corresponding set of physical color swatches. This article focuses on the mathematical model concept. Understanding the concept Most people have heard that a wide range of colors can be created by the primary colors red, blue, and yellow, if working with paints. Those colors then define a color space. We can specify the amount of red color as the X axis, the amount of blue as the Y axis, and the amount of yellow as the Z axis, giving us a three-dimensional space, wherein every possible color has a unique position. -
Cielab Color Space
Gernot Hoffmann CIELab Color Space Contents . Introduction 2 2. Formulas 4 3. Primaries and Matrices 0 4. Gamut Restrictions and Tests 5. Inverse Gamma Correction 2 6. CIE L*=50 3 7. NTSC L*=50 4 8. sRGB L*=/0/.../90/99 5 9. AdobeRGB L*=0/.../90 26 0. ProPhotoRGB L*=0/.../90 35 . 3D Views 44 2. Linear and Standard Nonlinear CIELab 47 3. Human Gamut in CIELab 48 4. Low Chromaticity 49 5. sRGB L*=50 with RGB Numbers 50 6. PostScript Kernels 5 7. Mapping CIELab to xyY 56 8. Number of Different Colors 59 9. HLS-Hue for sRGB in CIELab 60 20. References 62 1.1 Introduction CIE XYZ is an absolute color space (not device dependent). Each visible color has non-negative coordinates X,Y,Z. CIE xyY, the horseshoe diagram as shown below, is a perspective projection of XYZ coordinates onto a plane xy. The luminance is missing. CIELab is a nonlinear transformation of XYZ into coordinates L*,a*,b*. The gamut for any RGB color system is a triangle in the CIE xyY chromaticity diagram, here shown for the CIE primaries, the NTSC primaries, the Rec.709 primaries (which are also valid for sRGB and therefore for many PC monitors) and the non-physical working space ProPhotoRGB. The white points are individually defined for the color spaces. The CIELab color space was intended for equal perceptual differences for equal chan- ges in the coordinates L*,a* and b*. Color differences deltaE are defined as Euclidian distances in CIELab. This document shows color charts in CIELab for several RGB color spaces. -
Color Space Conversion
L Technical Color Space Information Conversion The role of color space conversion in the process of scanning and reproduc- ing a color image begins the moment an image is captured by a scanner and continues through the point at which it is output on film. For the purpose of this article we will discuss conversions between three types of color systems: RGB, CIE, and CMYK. The RGB (Red, Green, & Blue) and CMYK (Cyan, Magenta, Yellow, & Black) color models have been described in greater detail in the Linotype-Hell Technical information piece entitled Color in Printing. For some background information on the CIE (Commission Internationale de l’Eclairage), please refer to Color Spaces and PostScript Level 2. CIE as a reference color space Most scanners acquire color in the form of RGB data. The majority of non- photographic printing methods employ CMYK inks or toners. In a closed sys- tem, where the characteristics of both the scanner and the printing method are well-defined, conversions may be made from RGB to CMYK through tables that maintain a reasonable level of color consistency. The process becomes somewhat more difficult as you add monitors, other scanners, proofing devices or printing processes that have different characteristics. It becomes critical to have a consistent yardstick, if you will, a means of con- verting between different color systems (and back again) without a loss in color fidelity. This is what CIE provides. Let’s start with some background on CIE, and then we will look at how it can be applied in an open system. Measuring color Light reflected off of, or transmitted through a colored object can be mea- sured by the wavelengths of light that are reflected or transmitted. -
Package 'Colorspace'
Package ‘colorspace’ February 15, 2013 Version 1.2-1 Date 2013-01-24 Title Color Space Manipulation Description Carries out mapping between assorted color spaces including RGB, HSV, HLS, CIEXYZ, CIELUV, HCL (polar CIELUV),CIELAB and po- lar CIELAB. Qualitative, sequential, and diverging color palettes based on HCL colors are provided. Depends R (>= 2.10.0), methods Suggests KernSmooth, MASS, kernlab, mvtnorm, vcd, tcltk, dichromat License BSD LazyData yes Author Ross Ihaka [aut], Paul Murrell [aut], Kurt Hornik [aut], Jason C. Fisher [aut], Achim Zeileis [aut, cre] Maintainer Achim Zeileis <[email protected]> Repository CRAN Date/Publication 2013-01-24 14:59:08 NeedsCompilation yes R topics documented: choose_palette . .2 color-class . .3 coords . .4 desaturate . .5 hex..............................................6 hex2RGB . .7 HLS.............................................8 1 2 choose_palette HSV.............................................9 LAB............................................. 10 LUV............................................. 11 mixcolor . 12 polarLAB . 13 polarLUV . 14 rainbow_hcl . 15 readhex . 18 readRGB . 19 RGB............................................. 20 sRGB ............................................ 21 USSouthPolygon . 22 writehex . 22 XYZ............................................. 23 Index 25 choose_palette Graphical User Interface for Choosing HCL Color Palettes Description A graphical user interface (GUI) for viewing, manipulating, and choosing HCL color palettes. Usage choose_palette(pal -
Verfahren Zur Farbanpassung F ¨Ur Electronic Publishing-Systeme
Verfahren zur Farbanpassung f ¨ur Electronic Publishing-Systeme Von dem Fachbereich Elektrotechnik und Informationstechnik der Universit¨atHannover zur Erlangung des akademischen Grades Doktor-Ingenieur genehmigte Dissertation von Dipl.-Ing. Wolfgang W¨olker geb. am 7. Juli 1957, in Herford 1999 Referent: Prof. Dr.-Ing. C.-E. Liedtke Korreferent: Prof. Dr.-Ing. K. Jobmann Tag der Promotion: 18.01.1999 Kurzfassung Zuk ¨unftigePublikationssysteme ben¨otigenleistungsstarke Verfahren zur Farb- bildbearbeitung, um den hohen Durchsatz insbesondere der elektronischen Me- dien bew¨altigenzu k¨onnen. Dieser Beitrag beschreibt ein System f ¨urdie automatisierte Farbmanipulation von Einzelbildern. Die derzeit vorwiegend manuell ausgef ¨uhrtenAktionen wer- den durch hochsprachliche Vorgaben ersetzt, die vom System interpretiert und ausgef ¨uhrtwerden. Basierend auf einem hier vorgeschlagenen Grundwortschatz zur Farbmanipulation sind Modifikationen und Erweiterungen des Wortschat- zes durch neue abstrakte Begriffe m¨oglich.Die Kombination mehrerer bekannter Begriffe zu einem neuen abstrakten Begriff f ¨uhrtdabei zu funktionserweitern- den, komplexen Aktionen. Dar ¨uberhinaus pr¨agendiese Erg¨anzungenden in- dividuellen Wortschatz des jeweiligen Anwenders. Durch die hochsprachliche Schnittstelle findet eine Entkopplung der Benutzervorgaben von der technischen Umsetzung statt. Die farbverarbeitenden Methoden lassen sich so im Hinblick auf die verwendeten Farbmodelle optimieren. Statt der bisher ¨ublichenmedien- und ger¨atetechnischbedingten Farbmodelle kann nun z.B. das visuell adaptierte CIE(1976)-L*a*b*-Modell genutzt werden. Die damit m¨oglichenfarbverarbeiten- den Methoden erlauben umfangreiche und wirksame Eingriffe in die Farbdar- stellung des Bildes. Zielsetzung des Verfahrens ist es, unter Verwendung der vorgeschlagenen Be- nutzerschnittstelle, die teilweise wenig anschauliche Parametrisierung bestimm- ter Farbmodelle, durch einen hochsprachlichen Zugang zu ersetzen, der den An- wender bei der Farbbearbeitung unterst ¨utztund den Experten entlastet. -
Psychovisual Evaluation of the Effect of Color Spaces and Color Quantification in Jpeg2000 Image Compression
PSYCHOVISUAL EVALUATION OF THE EFFECT OF COLOR SPACES AND COLOR QUANTIFICATION IN JPEG2000 IMAGE COMPRESSION Mohamed-Chaker Larabi, Christine Fernandez-Maloigne and Noel¨ Richard IRCOM-SIC Laboratory, University of Poitiers BP 30170 - 86962 Futuroscope cedex FRANCE Email : [email protected] ABSTRACT 4]. Figure 1 shows the fundamental building blocks of a typical JPEG2000 is an emerging standard for still image compres- JPEG2000 encoder as described by Rabbani[4]. sion. It is not only intended to provide rate-distortion and subjective image quality performance superior to existing standards, but also to provide features and additional func- tionalities that current standards can not address sufficiently such as lossless and lossy compression, progressive trans- mission by pixel accuracy and by resolution, etc. Currently the JPEG2000 standard is set up for use with the sRGB three-component color space.the aim of this research is to Fig. 1. JPEG2000 fundamental building blocks. determine thanks to psychovisual experiences whether or Color management in JPEG2000 was an important topic in not the color space selected will significantly improve the the development of the standard and the issue of present- image compression. The RGB, XYZ, CIELAB, CIELUV, ing color properly is becoming more and more important as YIQ, YCrCb and YUV color spaces were examined and com- systems get better and as a wider range of systems are doing pared. In addition, we started a psychovisual evaluation similar things. In the past, color has been targeted as an area on the effect of color quantification on JPEG2000 image of least concern with the overall presentation. -
Color Appearance Models Today's Topic
Color Appearance Models Arjun Satish Mitsunobu Sugimoto 1 Today's topic Color Appearance Models CIELAB The Nayatani et al. Model The Hunt Model The RLAB Model 2 1 Terminology recap Color Hue Brightness/Lightness Colorfulness/Chroma Saturation 3 Color Attribute of visual perception consisting of any combination of chromatic and achromatic content. Chromatic name Achromatic name others 4 2 Hue Attribute of a visual sensation according to which an area appears to be similar to one of the perceived colors Often refers red, green, blue, and yellow 5 Brightness Attribute of a visual sensation according to which an area appears to emit more or less light. Absolute level of the perception 6 3 Lightness The brightness of an area judged as a ratio to the brightness of a similarly illuminated area that appears to be white Relative amount of light reflected, or relative brightness normalized for changes in the illumination and view conditions 7 Colorfulness Attribute of a visual sensation according to which the perceived color of an area appears to be more or less chromatic 8 4 Chroma Colorfulness of an area judged as a ratio of the brightness of a similarly illuminated area that appears white Relationship between colorfulness and chroma is similar to relationship between brightness and lightness 9 Saturation Colorfulness of an area judged as a ratio to its brightness Chroma – ratio to white Saturation – ratio to its brightness 10 5 Definition of Color Appearance Model so much description of color such as: wavelength, cone response, tristimulus values, chromaticity coordinates, color spaces, … it is difficult to distinguish them correctly We need a model which makes them straightforward 11 Definition of Color Appearance Model CIE Technical Committee 1-34 (TC1-34) (Comission Internationale de l'Eclairage) They agreed on the following definition: A color appearance model is any model that includes predictors of at least the relative color-appearance attributes of lightness, chroma, and hue. -
ARC Laboratory Handbook. Vol. 5 Colour: Specification and Measurement
Andrea Urland CONSERVATION OF ARCHITECTURAL HERITAGE, OFARCHITECTURALHERITAGE, CONSERVATION Colour Specification andmeasurement HISTORIC STRUCTURESANDMATERIALS UNESCO ICCROM WHC VOLUME ARC 5 /99 LABORATCOROY HLANODBOUOKR The ICCROM ARC Laboratory Handbook is intended to assist professionals working in the field of conserva- tion of architectural heritage and historic structures. It has been prepared mainly for architects and engineers, but may also be relevant for conservator-restorers or archaeologists. It aims to: - offer an overview of each problem area combined with laboratory practicals and case studies; - describe some of the most widely used practices and illustrate the various approaches to the analysis of materials and their deterioration; - facilitate interdisciplinary teamwork among scientists and other professionals involved in the conservation process. The Handbook has evolved from lecture and laboratory handouts that have been developed for the ICCROM training programmes. It has been devised within the framework of the current courses, principally the International Refresher Course on Conservation of Architectural Heritage and Historic Structures (ARC). The general layout of each volume is as follows: introductory information, explanations of scientific termi- nology, the most common problems met, types of analysis, laboratory tests, case studies and bibliography. The concept behind the Handbook is modular and it has been purposely structured as a series of independent volumes to allow: - authors to periodically update the -
Measuring Perceived Color Difference Using YIQ NTSC Transmission Color Space in Mobile Applications
Programación Matemática y Software (2010) Vol.2. Num. 2. Dirección de Reservas de Derecho: 04-2009-011611475800-102 Measuring perceived color difference using YIQ NTSC transmission color space in mobile applications Yuriy Kotsarenko, Fernando Ramos TECNOLÓGICO DE DE MONTERREY, CAMPUS CUERNAVACA. Resumen. En este trabajo varias formulas están introducidas que permiten calcular la medir la diferencia entre colores de forma perceptible, utilizando el espacio de colores YIQ. Las formulas clásicas y sus derivados que utilizan los espacios CIELAB y CIELUV requieren muchas transformaciones aritméticas de valores entrantes definidos comúnmente con los componentes de rojo, verde y azul, y por lo tanto son muy pesadas para su implementación en dispositivos móviles. Las formulas alternativas propuestas en este trabajo basadas en espacio de colores YIQ son sencillas y se calculan rápidamente, incluso en tiempo real. La comparación está incluida en este trabajo entre las formulas clásicas y las propuestas utilizando dos diferentes grupos de experimentos. El primer grupo de experimentos se enfoca en evaluar la diferencia perceptible utilizando diferentes formulas, mientras el segundo grupo de experimentos permite determinar el desempeño de cada una de las formulas para determinar su velocidad cuando se procesan imágenes. Los resultados experimentales indican que las formulas propuestas en este trabajo son muy cercanas en términos perceptibles a las de CIELAB y CIELUV, pero son significativamente más rápidas, lo que los hace buenos candidatos para la medición de las diferencias de colores en dispositivos móviles y aplicaciones en tiempo real. Abstract. An alternative color difference formulas are presented for measuring the perceived difference between two color samples defined in YIQ color space. -
Skin Color Measurements in Terms of CIELAB Color Space Values
Skin Color Measurements in Terms of CIELAB Color Space Values Ian L. Weatherall and Bernard D. Coombs School of Consumer and Applied Sciences (IL W) and Department of Preventative and Social Medicine (BDC), University of Otago, Dunedin, New Zealand The principles of color measurement established by the terms of one, two, or all three color-space parameters. Be Commission International d'Eclairage have been applied to cause the method is quantitative and the principles interna skin and the results expressed in terms of color space L *, hue tionally recognized, these color-space parameters are pro angle, and chroma values. The distribution of these values for posed for the unambiguous communication of skin-color the ventral forearm skin of a sample of healthy volunteers is information that relates directly to visual observations of presented. The skin-color characteristics of a European sub clinical importance or scientific interest. ] Invest Dermato199: group is summarized and briefly compared with others. 468-473, 1992 Color differences between individuals were identified in he appearance of human skin is a major descriptive that differ systematically from those based on scanning spectro parameter in both clinical and scientific evaluations. photometry [16,17] . There appear to have been no reports of the Skin color may be associated with susceptibility to the evaluation of the color of skin by applying reflectance spectroscopy development of skin cancer [1,2]' changes in color according to the principles laid down by the Commission Interna characterize erythema [3-6], and the observation of tional d'Eclairage (CIE) for the measurement of the color Tcolor variegation is an important clinical criterion for differentiat of surfaces with the results given in terms of CIE 1976 L * a* b* ing between cutaneous malignant melanoma and benign nevi [7]. -
Color Spaces YCH and Ysch for Color Specification and Image Processing in Multi-Core Computing and Mobile Systems
Programación Matemática y Software (2012) Vol. 4. No 2. ISSN: 2007-3283 Recibido: 14 de septiembre del 2011 Aceptado: 3 de enero del 2012 Publicado en línea: 8 de enero del 2013 Color spaces YCH and YScH for color specification and image processing in multi-core computing and mobile systems Yuriy Kotsarenko, Fernando Ramos Tecnológico de Monterrey, Campus Cuernavaca [email protected], [email protected] Resumen. En este trabajo dos nuevos espacios de color se describen para especificación de colores y procesamiento de imágenes utilizando la forma cilíndrica del espacio de color YIQ. Los espacios de colores clásicos tales como HSL y HSV no toman en cuenta la visión humana y son perceptualmente inexactos. Los espacios de colores perceptualmente uniformes como CIELAB y CIELUV son muy costosos computacionalmente para aplicaciones interactivas de tiempo real y son difíciles de implementar. Las alternativas propuestas, por otro lado, tienen un balance entre uniformidad perceptual, desempeño y simplicidad de cálculo. Estos espacios modelan colores de forma más exacta y son rápidos de calcular. Los resultados experimentales en este trabajo comparan espacios de colores clásicos con los propuestos en términos de uniformidad, riqueza de colores y desempeño, incluyendo numerosas pruebas de rapidez en procesadores de varios núcleos y sistemas móviles tales como ultra portátiles y los tablets tipo iPad. Los resultados evidencian que los espacios de colores propuestos son mejores alternativas para la industria de computación donde actualmente se utilicen los espacios de colores clásicos. Abstract. Two novel color spaces are described for color specification and image processing using cylindrical variants of YIQ color space.