Assessing Color Discrimination
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An Evaluation of Color Differences Across Different Devices Craitishia Lewis Clemson University, [email protected]
Clemson University TigerPrints All Theses Theses 12-2013 What You See Isn't Always What You Get: An Evaluation of Color Differences Across Different Devices Craitishia Lewis Clemson University, [email protected] Follow this and additional works at: https://tigerprints.clemson.edu/all_theses Part of the Communication Commons Recommended Citation Lewis, Craitishia, "What You See Isn't Always What You Get: An Evaluation of Color Differences Across Different Devices" (2013). All Theses. 1808. https://tigerprints.clemson.edu/all_theses/1808 This Thesis is brought to you for free and open access by the Theses at TigerPrints. It has been accepted for inclusion in All Theses by an authorized administrator of TigerPrints. For more information, please contact [email protected]. What You See Isn’t Always What You Get: An Evaluation of Color Differences Across Different Devices A Thesis Presented to The Graduate School of Clemson University In Partial Fulfillment Of the Requirements for the Degree Master of Science Graphic Communications By Craitishia Lewis December 2013 Accepted by: Dr. Samuel Ingram, Committee Chair Kern Cox Dr. Eric Weisenmiller Dr. Russell Purvis ABSTRACT The objective of this thesis was to examine color differences between different digital devices such as, phones, tablets, and monitors. New technology has always been the catalyst for growth and change within the printing industry. With gadgets like the iPhone and the iPad becoming increasingly more popular in the recent years, printers have yet another technological advancement to consider. Soft proofing strategies use color management technology that allows the client to view their proof on a monitor as a duplication of how the finished product will appear on a printed piece of paper. -
Package 'Colorscience'
Package ‘colorscience’ October 29, 2019 Type Package Title Color Science Methods and Data Version 1.0.8 Encoding UTF-8 Date 2019-10-29 Maintainer Glenn Davis <[email protected]> Description Methods and data for color science - color conversions by observer, illuminant, and gamma. Color matching functions and chromaticity diagrams. Color indices, color differences, and spectral data conversion/analysis. License GPL (>= 3) Depends R (>= 2.10), Hmisc, pracma, sp Enhances png LazyData yes Author Jose Gama [aut], Glenn Davis [aut, cre] Repository CRAN NeedsCompilation no Date/Publication 2019-10-29 18:40:02 UTC R topics documented: ASTM.D1925.YellownessIndex . .5 ASTM.E313.Whiteness . .6 ASTM.E313.YellownessIndex . .7 Berger59.Whiteness . .7 BVR2XYZ . .8 cccie31 . .9 cccie64 . 10 CCT2XYZ . 11 CentralsISCCNBS . 11 CheckColorLookup . 12 1 2 R topics documented: ChromaticAdaptation . 13 chromaticity.diagram . 14 chromaticity.diagram.color . 14 CIE.Whiteness . 15 CIE1931xy2CIE1960uv . 16 CIE1931xy2CIE1976uv . 17 CIE1931XYZ2CIE1931xyz . 18 CIE1931XYZ2CIE1960uv . 19 CIE1931XYZ2CIE1976uv . 20 CIE1960UCS2CIE1964 . 21 CIE1960UCS2xy . 22 CIE1976chroma . 23 CIE1976hueangle . 23 CIE1976uv2CIE1931xy . 24 CIE1976uv2CIE1960uv . 25 CIE1976uvSaturation . 26 CIELabtoDIN99 . 27 CIEluminanceY2NCSblackness . 28 CIETint . 28 ciexyz31 . 29 ciexyz64 . 30 CMY2CMYK . 31 CMY2RGB . 32 CMYK2CMY . 32 ColorBlockFromMunsell . 33 compuphaseDifferenceRGB . 34 conversionIlluminance . 35 conversionLuminance . 36 createIsoTempLinesTable . 37 daylightcomponents . 38 deltaE1976 -
Photometric Modelling for Efficient Lighting and Display Technologies
Sinan G Sinan ENÇ PHOTOMETRIC MODELLING FOR PHOTOMETRIC MODELLING FOR EFFICIENT LIGHTING LIGHTING EFFICIENT FOR MODELLING PHOTOMETRIC EFFICIENT LIGHTING AND DISPLAY TECHNOLOGIES AND DISPLAY TECHNOLOGIES DISPLAY AND A MASTER’S THESIS By Sinan GENÇ December 2016 AGU 2016 ABDULLAH GÜL UNIVERSITY PHOTOMETRIC MODELLING FOR EFFICIENT LIGHTING AND DISPLAY TECHNOLOGIES A THESIS SUBMITTED TO THE DEPARTMENT OF ELECTRICAL AND COMPUTER ENGINEERING AND THE GRADUATE SCHOOL OF ENGINEERING AND SCIENCE OF ABDULLAH GÜL UNIVERSITY IN PARTIAL FULFILLMENT OF THE REQUIREMENTS FOR THE DEGREE OF MASTER OF SCIENCE By Sinan GENÇ December 2016 i SCIENTIFIC ETHICS COMPLIANCE I hereby declare that all information in this document has been obtained in accordance with academic rules and ethical conduct. I also declare that, as required by these rules and conduct, I have fully cited and referenced all materials and results that are not original to this work. Sinan GENÇ ii REGULATORY COMPLIANCE M.Sc. thesis titled “PHOTOMETRIC MODELLING FOR EFFICIENT LIGHTING AND DISPLAY TECHNOLOGIES” has been prepared in accordance with the Thesis Writing Guidelines of the Abdullah Gül University, Graduate School of Engineering & Science. Prepared By Advisor Sinan GENÇ Asst. Prof. Evren MUTLUGÜN Head of the Electrical and Computer Engineering Program Assoc. Prof. Vehbi Çağrı GÜNGÖR iii ACCEPTANCE AND APPROVAL M.Sc. thesis titled “PHOTOMETRIC MODELLING FOR EFFICIENT LIGHTING AND DISPLAY TECHNOLOGIES” and prepared by Sinan GENÇ has been accepted by the jury in the Electrical and Computer Engineering Graduate Program at Abdullah Gül University, Graduate School of Engineering & Science. 26/12/2016 (26/12/2016) JURY: Prof. Bülent YILMAZ :………………………………. Assoc. Prof. M. Serdar ÖNSES :……………………………… Asst. -
Color Difference Delta E - a Survey
See discussions, stats, and author profiles for this publication at: https://www.researchgate.net/publication/236023905 Color difference Delta E - A survey Article in Machine Graphics and Vision · April 2011 CITATIONS READS 12 8,785 2 authors: Wojciech Mokrzycki Maciej Tatol Cardinal Stefan Wyszynski University in Warsaw University of Warmia and Mazury in Olsztyn 157 PUBLICATIONS 177 CITATIONS 5 PUBLICATIONS 27 CITATIONS SEE PROFILE SEE PROFILE All content following this page was uploaded by Wojciech Mokrzycki on 08 August 2017. The user has requested enhancement of the downloaded file. Colour difference ∆E - A survey Mokrzycki W.S., Tatol M. {mokrzycki,mtatol}@matman.uwm.edu.pl Faculty of Mathematics and Informatics University of Warmia and Mazury, Sloneczna 54, Olsztyn, Poland Preprint submitted to Machine Graphic & Vision, 08:10:2012 1 Contents 1. Introduction 4 2. The concept of color difference and its tolerance 4 2.1. Determinants of color perception . 4 2.2. Difference in color and tolerance for color of product . 5 3. An early period in ∆E formalization 6 3.1. JND units and the ∆EDN formula . 6 3.2. Judd NBS units, Judd ∆EJ and Judd-Hunter ∆ENBS formulas . 6 3.3. Adams chromatic valence color space and the ∆EA formula . 6 3.4. MacAdam ellipses and the ∆EFMCII formula . 8 4. The ANLab model and ∆E formulas 10 4.1. The ANLab model . 10 4.2. The ∆EAN formula . 10 4.3. McLaren ∆EMcL and McDonald ∆EJPC79 formulas . 10 4.4. The Hunter color system and the ∆EH formula . 11 5. ∆E formulas in uniform color spaces 11 5.1. -
Accurately Reproducing Pantone Colors on Digital Presses
Accurately Reproducing Pantone Colors on Digital Presses By Anne Howard Graphic Communication Department College of Liberal Arts California Polytechnic State University June 2012 Abstract Anne Howard Graphic Communication Department, June 2012 Advisor: Dr. Xiaoying Rong The purpose of this study was to find out how accurately digital presses reproduce Pantone spot colors. The Pantone Matching System is a printing industry standard for spot colors. Because digital printing is becoming more popular, this study was intended to help designers decide on whether they should print Pantone colors on digital presses and expect to see similar colors on paper as they do on a computer monitor. This study investigated how a Xerox DocuColor 2060, Ricoh Pro C900s, and a Konica Minolta bizhub Press C8000 with default settings could print 45 Pantone colors from the Uncoated Solid color book with only the use of cyan, magenta, yellow and black toner. After creating a profile with a GRACoL target sheet, the 45 colors were printed again, measured and compared to the original Pantone Swatch book. Results from this study showed that the profile helped correct the DocuColor color output, however, the Konica Minolta and Ricoh color outputs generally produced the same as they did without the profile. The Konica Minolta and Ricoh have much newer versions of the EFI Fiery RIPs than the DocuColor so they are more likely to interpret Pantone colors the same way as when a profile is used. If printers are using newer presses, they should expect to see consistent color output of Pantone colors with or without profiles when using default settings. -
Predictability of Spot Color Overprints
Predictability of Spot Color Overprints Robert Chung, Michael Riordan, and Sri Prakhya Rochester Institute of Technology School of Print Media 69 Lomb Memorial Drive, Rochester, NY 14623, USA emails: [email protected], [email protected], [email protected] Keywords spot color, overprint, color management, portability, predictability Abstract Pre-media software packages, e.g., Adobe Illustrator, do amazing things. They give designers endless choices of how line, area, color, and transparency can interact with one another while providing the display that simulates printed results. Most prepress practitioners are thrilled with pre-media software when working with process colors. This research encountered a color management gap in pre-media software’s ability to predict spot color overprint accurately between display and print. In order to understand the problem, this paper (1) describes the concepts of color portability and color predictability in the context of color management, (2) describes an experimental set-up whereby display and print are viewed under bright viewing surround, (3) conducts display-to-print comparison of process color patches, (4) conducts display-to-print comparison of spot color solids, and, finally, (5) conducts display-to-print comparison of spot color overprints. In doing so, this research points out why the display-to-print match works for process colors, and fails for spot color overprints. Like Genie out of the bottle, there is no turning back nor quick fix to reconcile the problem with predictability of spot color overprints in pre-media software for some time to come. 1. Introduction Color portability is a key concept in ICC color management. -
Measuring Perceived Color Difference Using YIQ Color Space
Programación Matemática y Software (2010) Vol. 2. No 2. ISSN: 2007-3283 Recibido: 17 de Agosto de 2010 Aceptado: 25 de Noviembre de 2010 Publicado en línea: 30 de Diciembre de 2010 Measuring perceived color difference using YIQ NTSC transmission color space in mobile applications Yuriy Kotsarenko, Fernando Ramos TECNOLOGICO DE DE MONTERREY, CAMPUS CUERNAVACA. Resumen: En este trabajo varias fórmulas 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 fórmulas 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 fórmulas, mientras el segundo grupo de experimentos permite determinar el desempeño de cada una de las fórmulas 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. -
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 -
The Applicability of Total Color Difference E for Determining The
applied sciences Article The Applicability of Total Color Difference DE for Determining the Blooming Time in Longissimus Lumborum and Semimembranosus Muscles from Holstein-Friesian Bulls at Different Ageing Times Katarzyna Tkacz 1,* , Monika Modzelewska-Kapituła 1 , Adam Wi˛ek 1 and Zenon Nogalski 2 1 Department of Meat Technology and Chemistry, Faculty of Food Sciences, University of Warmia and Mazury in Olsztyn, Plac Cieszy´nski1, 10-719 Olsztyn, Poland; [email protected] (M.M.-K.); [email protected] (A.W.) 2 Department of Cattle Breeding and Milk Evaluation, Faculty of Animal Bioengineering, University of Warmia and Mazury in Olsztyn, Oczapowskiego 5, 10-719 Olsztyn, Poland; [email protected] * Correspondence: [email protected]; Tel.: +488-9523-5284 Received: 30 October 2020; Accepted: 18 November 2020; Published: 20 November 2020 Featured Application: The results of this study may have practical applications because an increase in the values of color parameters during beef ageing and the color of beef after blooming will be perceived by consumers as more attractive. Abstract: This study was conducted to determine the optimal blooming time in beef muscles based on DE, and to analyze the effects of muscle type and ageing time on beef color and blooming. Beef color was determined on freshly cut longissimus lumborum (LL, n = 8) and semimembranosus (SM, n = 8) muscles on days 1, 9, and 14 of ageing during 60 min blooming at 5 min intervals. It was found that DE0, representing the difference in color between freshly cut muscles and subsequently analyzed samples, supported the determination of the optimal blooming time, which varied across ageing times (15, 20, 25 min for the LL muscle, and 10, 15, 20 min for the SM muscle on days 1, 9, and 14 of ageing, respectively). -
Análise Sensorial (Sensory Analysis) 29-02-2012 by Goreti Botelho 1
Análise Sensorial (Sensory analysis) 29-02-2012 INSTITUTO POLITÉCNICO DE COIMBRA INSTITUTO POLITÉCNICO DE COIMBRA ESCOLA SUPERIOR AGRÁRIA ESCOLA SUPERIOR AGRÁRIA LEAL LEAL Análise Sensorial Sensory analysis AULA T/P Nº 3 Lesson T/P Nº 3 SUMÁRIO: Summary Parte expositiva: Sistemas de medição de cor: diagrama de cromaticidade CIE, sistema de Theoretical part: Hunter e sistema de Munsell. Color Measurement Systems: CIE chromaticity diagram, Hunter system Parte prática: and Munsell system. Determinação de cores problema utilizando o diagrama de cromaticidade Practical part: CIE. Determination of a color problem by using the CIE chromaticity diagram. Utilização do colorímetro de refletância para determinação da cor de frutos. Use of the reflectance colorimeter to determine the color of fruits. Prova sensorial de dois sumos para compreensão da cor de um produto na Sensory taste of two juices to understand the color effect of a product in percepção sensorial. sensory perception. Goreti Botelho 1 Goreti Botelho 2 Why do we need devices to replace the human vision in the food industry? Limitações do olho humano • a) não é reprodutível – o mesmo alimento apresentado a vários provadores ou ao mesmo provador em momentos diferentes pode merecer qualificações diferentes. Este último fenómeno deve-se ao facto de que, em oposição à grande capacidade humana de apreciar diferenças, o homem não tem uma boa “memória da cor”, ou seja, é difícil recordar uma cor quando não a está a ver. • b) a nomenclatura é pouco concreta e até confusa. As expressões “verde muito claro” ou “amarelo intenso” não são suficientes para definir uma cor e muito menos para a reproduzir ou compará-la com outras quando não se dispõe do objecto que tem essa cor. -
AMSA Meat Color Measurement Guidelines
AMSA Meat Color Measurement Guidelines Revised December 2012 American Meat Science Association http://www.m eatscience.org AMSA Meat Color Measurement Guidelines Revised December 2012 American Meat Science Association 201 West Springfi eld Avenue, Suite 1202 Champaign, Illinois USA 61820 800-517-2672 [email protected] http://www.m eatscience.org iii CONTENTS Technical Writing Committee .................................................................................................................... v Preface ..............................................................................................................................................................vi Section I: Introduction ................................................................................................................................. 1 Section II: Myoglobin Chemistry ............................................................................................................... 3 A. Fundamental Myoglobin Chemistry ................................................................................................................ 3 B. Dynamics of Myoglobin Redox Form Interconversions ........................................................................... 3 C. Visual, Practical Meat Color Versus Actual Pigment Chemistry ........................................................... 5 D. Factors Affecting Meat Color ............................................................................................................................... 6 E. Muscle