Introduction to Colorimetry

Total Page:16

File Type:pdf, Size:1020Kb

Introduction to Colorimetry IES NY Issues in Color Seminar February 26, 2011 IntroductionIntroduction toto ColorimetryColorimetry JeanJean PaulPaul FreyssinierFreyssinier Lighting Research Center, Rensselaer Polytechnic Institute Troy, New York, U.S.A. sponsored by www.lrc.rpi.edu/programs/solidstate/assist 1 © 2010 Rensselaer Polytechnic Institute. All rights reserved. AcknowledgmentsAcknowledgments NYCNYC IESIES organizersorganizers ofof IssuesIssues inin ColorColor SeminarSeminar,, especiallyespecially ›› JasonJason Livingston,Livingston, WendyWendy LuedtkeLuedtke,, DanDan Rogers,Rogers, andand MegMeg SmithSmith LRCLRC faculty,faculty, staff,staff, andand studentsstudents SponsorsSponsors ofof ASSISTASSIST ProgramProgram 2 © 2010 Rensselaer Polytechnic Institute. All rights reserved. Radiometry 3 © 2010 Rensselaer Polytechnic Institute. All rights reserved. Radiometry Detection and measurement of electromagnetic energy › Purely physical – no consideration of how it stimulates the eye Unit of measurement: watt › The watt is a unit of power › Power is the rate of energy; energy per time • 1 watt = 1 joule/second 4 © 2010 Rensselaer Polytechnic Institute. All rights reserved. Radiometry: Geometry and units The geometry of how radiant energy is produced, emitted, propagating, defines the units of measurement Description Quantity Unit Energy per time Power W Incident on a surface Irradiance W/m2 Leaving a surface Exitance W/m2 5 © 2010 Rensselaer Polytechnic Institute. All rights reserved. Sources of radiance Sun Electroluminescent Approximate luminance, cd/m2 1.6x109 1.5x106 1.2x107 3.0x104 1.4x104 30 to to 1.0x109 3.9x107 High Medium Low 6 © 2010 Rensselaer Polytechnic Institute. All rights reserved. Spectrum: Radiation as a function of wavelength The electromagnetic spectrum can be divided into smaller and smaller bands, or expressed as a continuous function of wavelength (or frequency) Units: W/nm P P d area under curve total 0 Daylight 5700 K Incandescent 1.0 1.0 0.9 0.9 0.8 0.8 0.7 0.7 0.6 0.6 power power 0.5 0.5 0.4 0.4 Relative 0.3 Relative 0.3 0.2 0.2 0.1 0.1 0.0 0.0 360 410 460 510 560 610 660 710 760 360 410 460 510 560 610 660 710 760 Wavelength (nm) Wavelength (nm) 7 © 2010 Rensselaer Polytechnic Institute. All rights reserved. Spectra of typical light sources Incandescent Fluorescent High pressure sodium Light emitting diodes 1 1.2 1.2 6 1.0 1.0 5 0.8 0.8 4 energy 0.6 0.6 3 0.4 0.4 2 Relative Relative Energy Relative Energy 0.2 0.2 1 0 0.0 0.0 350 450 550 650 750 350 450 550 650 750 350 450 550 650 750 0 Wavelength(nm) Wavelength(nm) 390 440 490 540 590 640 690 740 Wavelength(nm) Wavelength (nm) 8 © 2010 Rensselaer Polytechnic Institute. All rights reserved. PhotometryPhotometry 9 © 2010 Rensselaer Polytechnic Institute. All rights reserved. What is photometry? A simple, mathematically precise system of measuring and specifying light agreed to by an international community involved with its commerce and specification 10 © 2010 Rensselaer Polytechnic Institute. All rights reserved. WhyWhy isis photometryphotometry important?important? PromotesPromotes internationalinternational tradetrade ProvidesProvides aa quantitativequantitative languagelanguage forfor communicatingcommunicating betweenbetween stakeholdersstakeholders 11 © 2010 Rensselaer Polytechnic Institute. All rights reserved. Light IESNA Definition: Radiant energy capable of exciting the retina and producing a visual sensation. The visible portion of the electromagnetic spectrum extends from about 380 to 780 nanometers. CIE defines it over 360 to 830 nm. Official (CIE) definition: radiant energy weighted by the photopic luminous efficiency function, V(). Based on flicker photometry. 1 V(V()) -- PhotopicPhotopic V() - Scotopic 0.8 0.6 0.4 Luminous efficiency efficiency Luminous Luminous 0.2 0 300 350 400 450 500 550 600 650 700 750 800 Wavelength, nm 12 © 2010 Rensselaer Polytechnic Institute. All rights reserved. What does flicker photometry mean? Related to response of photoreceptors in central fovea › L and M cones Cone Fundamentals and V( ) › 2L + 1M V() 1 0.8 0.6 photopic L cone M cone 0.4 S cone relative value relative 0.2 0 400 500 600 700 wavelength (nm) 13 © 2010 Rensselaer Polytechnic Institute. All rights reserved. Light: Calculation of luminous flux Fluorescent lamp, 4100 K (F32T8/841) 1.0 0.9 0.8 0.7 0.6 power 0.5 0.4 Relative 0.3 0.2 0.1 0.0 360 410 460 510 560 610 660 710 760 Wavelength (nm) 14 © 2010 Rensselaer Polytechnic Institute. All rights reserved. Light: Calculation of luminous flux Fluorescent lamp, 4100 K (F32T8/841) 1.0 0.9 0.8 0.7 0.6 powerpower 0.5 0.4 Relative Relative 0.3 0.2 0.1 0.0 360 410 460 510 560 610 660 710 760 Wavelength (nm) 15 © 2010 Rensselaer Polytechnic Institute. All rights reserved. Light: Calculation of luminous flux Fluorescent lamp, 4100 K (F32T8/841) 1.0 0.9 0.8 0.7 0.6 powerpower 0.5 0.4 Relative Relative 0.3 0.2 0.1 0.0 360 410 460 510 560 610 660 710 760 Wavelength (nm) 16 © 2010 Rensselaer Polytechnic Institute. All rights reserved. Light: Calculation of luminous flux Fluorescent lamp, 4100 K (F32T8/841) 1.0 0.9 0.8 0.7 0.6 powerpower 0.5 0.4 Relative Relative 0.3 0.2 0.1 0.0 360 410 460 510 560 610 660 710 760 Wavelength (nm) 17 © 2010 Rensselaer Polytechnic Institute. All rights reserved. Light: Calculation of luminous flux 830nm k P V d 380nm lm k 683 W P Power W V Photopic luminous efficiency function Luminous Flux 18 © 2010 Rensselaer Polytechnic Institute. All rights reserved. Photometry Description Quantity Unit Light Luminous flux Lumen Amount incident per Illuminance Lumen/m2 surface area Amount leaving per (Luminous) Exitance Lumen/m2 surface area In a particular (Luminous) Intensity Lumen/sr direction (range of cd directions) In a direction, the Luminance Lumen/(m2 sr) amount emitted per cd/m2, nit surface area 19 © 2010 Rensselaer Polytechnic Institute. All rights reserved. Photocell and photopic response CIE Photopic Luminous415 0.0022 Efficiency Function 420 0.393194 and Silicon 3.85E+02 4.0 Photocell420 Spectral 0.004 Response 430 0.407356 3.90E+02 4.3 1.0 425 0.0073 440 0.419748 3.95E+02 4.7 430 0.0116 450 0.434304 4.00E+02 5.0 435 0.0168 460 0.446499 4.05E+02 5.3 0.8 440 0.023 470 0.463415 4.10E+02 5.5 445 0.0298 480 0.476397 4.15E+02 5.7 0.6 450 0.038 490 0.487215 4.20E+02 5.9 455 0.048 500 0.500787 4.25E+02 6.1 0.4 460 0.06 510 0.512982 4.30E+02 6.3 465 0.0739 520 0.52439 4.35E+02 6.5 470 0.091 530CIE 0.536389 Photopic 4.40E+02 6.7 0.2 475 0.1126 540Silicon 0.548387 4.45E+02 7.0 480 0.139 550 0.560386 4.50E+02 7.2 0.0 485 0.1693 560 0.571597 4.55E+02 7.4 350 450 550490 650 0.208 750 570 850 0.583202 950 4.60E+02 1050 7.6 495Wavelength 0.2586 (nm) 580 0.594611 4.65E+02 7.8 500 0 323 590 0 606412 4 70E+02 81 20 © 2010 Rensselaer Polytechnic Institute. All rights reserved. Errors applying V() Filters work well for broadband, “white” light sources, but not for narrowband sources Illuminance Meter 0.3 CIE Photopic Illuminance Meter 0.25 CIE Photopic 1 0.2 Blue LED 0.8 0.15 Relative response Relative 0.1 0.6 0.05 0.4 Relative response Relative 0 440 450 460 470 480 490 500 Wavelength (nm) 0.2 0 400 450 500 550 600 650 700 Wavelength (nm) 21 © 2010 Rensselaer Polytechnic Institute. All rights reserved. ColorimetryColorimetry 22 © 2010 Rensselaer Polytechnic Institute. All rights reserved. WhatWhat isis color?color? PerceptionPerception –– opponentopponent colorcolor theorytheory › Red vs. green › Blue vs. yellow › Hue › Saturation (chroma) › Lightness (brightness) ColorColor matchingmatching –– trichromatictrichromatic colorcolor theorytheory › Any light can be perfectly matched with a combination of just 3 standard lights SpecificationSpecification ofof thethe lightlight stimulusstimulus › Color matching functions › Equivalent to photometry 23 © 2010 Rensselaer Polytechnic Institute. All rights reserved. Human color perception Trichromatic vision › 3 cone photoreceptors › Overlapping spectral sensitivity › A lot of not completely understood neural processing both at the retina and within the visual cortex of the brain 24 © 2010 Rensselaer Polytechnic Institute. All rights reserved. Opponent color encoding 25 © 2010 Rensselaer Polytechnic Institute. All rights reserved. Metamers Lights of the same color appearance can be made up of different spectral power distributions as seen in the diagram at the right. Sources with the same color appearance, but different spectral power distributions will render colors differently. Broad spectral power distributions are more likely to produce better color rendering These three spectra can produce the same color perception 26 © 2010 Rensselaer Polytechnic Institute. All rights reserved. Metamers -4 x 10 1 0.9 Yellow-filteredYellow LED incandescent 0.8 YellowYellow-filtered LED white source 0.7 0.6 0.5 0.4 0.3 Spectral Power, W/nm Power, Spectral 0.2 0.1 0 400 450 500 550 600 650 700 Wavelength, nm 27 © 2010 Rensselaer Polytechnic Institute. All rights reserved. CIE colorimetric system Based on color matching – not color perception Principle of univariance › Once a photon is absorbed by a photoreceptor all wavelength information is lost › Photoreceptor response is determined by the number of photons absorbed › Color information is contained in the relative strength of the signals from each type of photoreceptor Matching done under very particular and controlled conditions › 2° observer and 10° observer › Bipartite field Reference Matching field field 28 © 2010 Rensselaer Polytechnic Institute.
Recommended publications
  • A Guide to Colorimetry
    A Guide to Colorimetry Sherwood Scientific Ltd 1 The Paddocks Cherry Hinton Road Cambridge CB1 8DH England www.sherwood-scientific.com Tel: +44 (0)1223 243444 Fax: +44 (0)1223 243300 Registered in England and Wales Registration Number 2329039 Reg. Office as above Group I II III IV V VI VII VIII Period 1A 8A 1 2 1 H 2A 3A 4A 5A 6A 7A He 1.008 4.003 3 4 5 6 7 8 9 10 2 Li Be B C N O F Ne 6.939 9.0122 10.811 12.011 14.007 15.999 18.998 20.183 11 12 13 14 15 16 17 18 3 Na Mg 3B 4B 5B 6B 7B [---------------8B-------------] 1B 2B Al Si P S Cl Ar 22.99 24.312 26.982 28.086 30.974 32.064 35.453 39.948 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 4 K Ca Sc Ti V Cr Mn Fe Co Ni Cu Zn Ga Ge As Se Br Kr 39.102 40.08 44.956 47.9 50.942 51.996 54.938 55.847 58.933 58.71 63.546 65.37 69.72 72.59 74.922 78.96 79.904 83.8 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 5 Rb Sr Y Zr Nb Mo Tc Ru Rh Pd Ag Cd In Sn Sb Te I Xe 85.47 87.52 88.905 91.22 92.906 95.94 [97] 101.07 102.91 106.4 107.87 112.4 114.82 118.69 121.75 127.6 126.9 131.3 55 56 57* 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 6 Cs Ba La Hf Ta W Re Os Ir Pt Au Hg Ti Pb Bi Po At Rn 132.91 137.34 138.91 178.49 180.95 183.85 186.2 190.2 192.2 195.09 196.97 200.59 204.37 207.19 208.98 209 210 222 87 88 89** 104 105 106 107 108 109 110 111 112 114 116 7 Fr Ra Ac Rt Db Sg Bh Hs Mt 215 226.03 227.03 [261] [262] [266] [264] [269] [268] [271] [272] [277] [289] [289] 58 59 60 61 62 63 64 65 66 67 68 69 70 71 * Lanthanides Ce Pr Nd Pm Sm Eu Gd Tb Dy Ho Er Tm Yb Lu 140.12 140.91 144.24 145 150.35 151.96 157.25 158.92 152.5 164.93 167.26 168.93 173.04 174.97 90 91 92 93 94 95 96 97 98 99 100 101 102 103 ** Actinides Th Pa U Np Pu Am Cm Bk Cf Es Fm Md No Lr 232.04 231 238 237.05 239.05 241.06 244.06 249.08 251 252.08 257.1 258.1 259.1 262.11 The ability to analyse and quantify colour in aqueous solutions and liquids using a colorimeter is something today’s analyst takes for granted.
    [Show full text]
  • 1 Human Color Vision
    CAMC01 9/30/04 3:13 PM Page 1 1 Human Color Vision Color appearance models aim to extend basic colorimetry to the level of speci- fying the perceived color of stimuli in a wide variety of viewing conditions. To fully appreciate the formulation, implementation, and application of color appearance models, several fundamental topics in color science must first be understood. These are the topics of the first few chapters of this book. Since color appearance represents several of the dimensions of our visual experience, any system designed to predict correlates to these experiences must be based, to some degree, on the form and function of the human visual system. All of the color appearance models described in this book are derived with human visual function in mind. It becomes much simpler to understand the formulations of the various models if the basic anatomy, physiology, and performance of the visual system is understood. Thus, this book begins with a treatment of the human visual system. As necessitated by the limited scope available in a single chapter, this treatment of the visual system is an overview of the topics most important for an appreciation of color appearance modeling. The field of vision science is immense and fascinating. Readers are encouraged to explore the liter- ature and the many useful texts on human vision in order to gain further insight and details. Of particular note are the review paper on the mechan- isms of color vision by Lennie and D’Zmura (1988), the text on human color vision by Kaiser and Boynton (1996), the more general text on the founda- tions of vision by Wandell (1995), the comprehensive treatment by Palmer (1999), and edited collections on color vision by Backhaus et al.
    [Show full text]
  • Calculating Correlated Color Temperatures Across the Entire Gamut of Daylight and Skylight Chromaticities
    Calculating correlated color temperatures across the entire gamut of daylight and skylight chromaticities Javier Herna´ ndez-Andre´ s, Raymond L. Lee, Jr., and Javier Romero Natural outdoor illumination daily undergoes large changes in its correlated color temperature ͑CCT͒, yet existing equations for calculating CCT from chromaticity coordinates span only part of this range. To improve both the gamut and accuracy of these CCT calculations, we use chromaticities calculated from our measurements of nearly 7000 daylight and skylight spectra to test an equation that accurately maps CIE 1931 chromaticities x and y into CCT. We extend the work of McCamy ͓Color Res. Appl. 12, 285–287 ͑1992͔͒ by using a chromaticity epicenter for CCT and the inverse slope of the line that connects it to x and y. With two epicenters for different CCT ranges, our simple equation is accurate across wide chromaticity and CCT ranges ͑3000–106 K͒ spanned by daylight and skylight. © 1999 Optical Society of America OCIS codes: 010.1290, 330.1710, 330.1730. 1. Introduction term correlated color temperature ͑CCT͒ instead of A colorimetric landmark often included in the Com- color temperature to describe its appearance. Sup- mission Internationale de l’Eclairage ͑CIE͒ 1931 pose that x1, y1 is the chromaticity of such an off-locus chromaticity diagram is the locus of chromaticity co- light source. By definition, the CCT of x1, y1 is the ordinates defined by blackbody radiators ͑see Fig. 1, temperature of the Planckian radiator whose chro- inset͒. One can calculate this Planckian ͑or black- maticity is nearest to x1, y1. The colorimetric body͒ locus by colorimetrically integrating the Planck minimum-distance calculations that determine CCT function at many different temperatures, with each must be done within the color space of the CIE 1960 temperature specifying a unique pair of 1931 x, y uniformity chromaticity scale ͑UCS͒ diagram.
    [Show full text]
  • Nondestructive Colorimetric Method to Determine the Oxygen Diffusion Rate Through Closures Used in Winemaking
    J. Agric. Food Chem. 2005, 53, 6967−6973 6967 Nondestructive Colorimetric Method To Determine the Oxygen Diffusion Rate through Closures Used in Winemaking PAULO LOPES,CEÄ DRIC SAUCIER,* AND YVES GLORIES Faculte´ d’Oenologie de Bordeaux, Universite´ Victor Segalen Bordeaux 2 UMR 1219 INRA, 351 Cours de la libe´ration, 33405 Talence Cedex, France Oxygen is one of the most important factors determining the aging potential of bottled wine, and oxygen diffusion into bottled wine is extremely dependent on the sealing effectiveness of the closure. A nondestructive colorimetric method was developed to measure oxygen diffusion from 1 to 9.8 mg/L during the postbottling period. This method was used to study oxygen diffusion through different closures available on the market. After 365 days of horizontal storage, only the control bottle was impermeable to atmospheric oxygen; all other closures exhibited variable rates of oxygen diffusion, which were much greater in the first month than in the following months. It was shown that the rate of diffusion was clearly influenced by the type of closure material used. KEYWORDS: Indigo carmine; sodium dithionite; oxygen diffusion; closures; bottles INTRODUCTION of total oxygen in bottled beer using an oximeter, based on The contact between wine and oxygen is critical for wine- Henry’s law (16), and measurement of gas composition in the making and bottle-aging processes. Some degree of oxygenation headspace of a bottle by gas chromatography (9, 17) or even may be beneficial for red wine development as the oxidative for white wines by simple absorbance measurement at 420 nm phenolic reactions will lead to color enhancement and the (10).
    [Show full text]
  • Color Appearance Models Second Edition
    Color Appearance Models Second Edition Mark D. Fairchild Munsell Color Science Laboratory Rochester Institute of Technology, USA Color Appearance Models Wiley–IS&T Series in Imaging Science and Technology Series Editor: Michael A. Kriss Formerly of the Eastman Kodak Research Laboratories and the University of Rochester The Reproduction of Colour (6th Edition) R. W. G. Hunt Color Appearance Models (2nd Edition) Mark D. Fairchild Published in Association with the Society for Imaging Science and Technology Color Appearance Models Second Edition Mark D. Fairchild Munsell Color Science Laboratory Rochester Institute of Technology, USA Copyright © 2005 John Wiley & Sons Ltd, The Atrium, Southern Gate, Chichester, West Sussex PO19 8SQ, England Telephone (+44) 1243 779777 This book was previously publisher by Pearson Education, Inc Email (for orders and customer service enquiries): [email protected] Visit our Home Page on www.wileyeurope.com or www.wiley.com All Rights Reserved. No part of this publication may be reproduced, stored in a retrieval system or transmitted in any form or by any means, electronic, mechanical, photocopying, recording, scanning or otherwise, except under the terms of the Copyright, Designs and Patents Act 1988 or under the terms of a licence issued by the Copyright Licensing Agency Ltd, 90 Tottenham Court Road, London W1T 4LP, UK, without the permission in writing of the Publisher. Requests to the Publisher should be addressed to the Permissions Department, John Wiley & Sons Ltd, The Atrium, Southern Gate, Chichester, West Sussex PO19 8SQ, England, or emailed to [email protected], or faxed to (+44) 1243 770571. This publication is designed to offer Authors the opportunity to publish accurate and authoritative information in regard to the subject matter covered.
    [Show full text]
  • Color Filter Array Image Analysis for Joint Denoising and Demosaicking Keigo Hirakawa University of Dayton, [email protected]
    University of Dayton eCommons Electrical and Computer Engineering Faculty Department of Electrical and Computer Publications Engineering 2008 Color Filter Array Image Analysis for Joint Denoising and Demosaicking Keigo Hirakawa University of Dayton, [email protected] Follow this and additional works at: http://ecommons.udayton.edu/ece_fac_pub Part of the Electrical and Electronics Commons, Optics Commons, Other Physics Commons, and the Signal Processing Commons eCommons Citation Hirakawa, Keigo, "Color Filter Array Image Analysis for Joint Denoising and Demosaicking" (2008). Electrical and Computer Engineering Faculty Publications. Paper 90. http://ecommons.udayton.edu/ece_fac_pub/90 This Book Chapter is brought to you for free and open access by the Department of Electrical and Computer Engineering at eCommons. It has been accepted for inclusion in Electrical and Computer Engineering Faculty Publications by an authorized administrator of eCommons. For more information, please contact [email protected], [email protected]. p 9 Color Filter Array Image Analysis for Joint Demosaicking and Denoising Keigo Hirakawa 9.1 Introduction . 239 9.1.1 A Comment About Model Assumptions . .. .. 242 9.1.2 Terminologies and Notational Conventions . 242 9.2 Noise Model . 244 9.3 Spectral Analysis of CFA Image . 246 9.4 Wavelet Analysis of CFA Image . 249 9.5 Constrained Filtering . .. .. .. .. .. .. 254 9.6 Missing Data . 257 9.7 Filterbank Coefficient Estimation . .. .. 259 9.8 Conclusion . 261 Acknowledgments . 261 References . 264 9.1 Introduction Noise is among the worst artifacts that affect the perceptual quality of the output from a digital camera (see Chapter 1). While cost-effective and popular, single-sensor solutions to camera architectures are not adept at noise suppression.
    [Show full text]
  • Colorimeter a  K  C (Beer’S Law) (Order Code COL-BTA) Where K Is a Proportionality Constant
    with a constant cell width, one can assume and b to be constant. This leads to the equation Colorimeter A k C (Beer’s law) (Order Code COL-BTA) where k is a proportionality constant. This equation shows absorbance to be related directly to concentration and represents a mathematical statement of Beer’s law. The Vernier Colorimeter is designed to determine the Beer’s law is discussed in more detail below. concentration of a solution by analyzing its color In this guide and in some of our computer programs, transmittance is expressed as intensity. The color of a solution may be inherent or percent transmittance or %T. Since T=%T/100, the formula can be rewritten as derived by adding another reagent to it. The A log(100 / %T ) or A 2 log%T Colorimeter measures the amount of light transmitted through a sample at a user-selectable wavelength. Beer’s Law Using the front panel arrow keys, you may choose from four wavelengths: 430 nm, In general, absorbance is important 470 nm, 565 nm, and 635 nm. Features such as automatic sensor identification and because of its direct relationship with one-step calibration make this sensor easy to use. concentration according to Beer’s law. Many experiments in chemistry How the Colorimeter Works and biology are based on this Light from a LED light source passes through a Source Detector concept. To obtain a Beer’s law cuvette containing a solution sample, as shown in curve, several standards (solutions of Figure 1. Some of the incoming light is absorbed LED Transmitted known concentration) are prepared light by the solution.
    [Show full text]
  • Standard Illuminant
    Standard illuminant From Wikipedia, the free encyclopedia A standard illuminant is a profile or spectrum of visible light which is published in order to allow images or colors recorded under different lighting to be compared. Contents 1 CIE illuminants 1.1 Illuminant A 1.2 Illuminants B and C 1.3 Illuminant series D 1.4 Illuminant E Relative spectral power distributions (SPDs) of CIE 1.5 Illuminant series F illuminants A, B, and C from 380nm to 780nm. 2 White point 2.1 White points of standard illuminants 3 References 4 External links CIE illuminants The International Commission on Illumination (usually abbreviated CIE for its French name) is the body responsible for publishing all of the well-known standard illuminants. Each of these is known by a letter or by a letter-number combination. Illuminants A, B, and C were introduced in 1931, with the intention of respectively representing average incandescent light, direct sunlight, and average daylight. Illuminants D represent phases of daylight, Illuminant E is the equal-energy illuminant, while Illuminants F represent fluorescent lamps of various composition. There are instructions on how to experimentally produce light sources ("standard sources") corresponding to the older illuminants. For the relatively newer ones (such as series D), experimenters are left to measure to profiles of their sources and compare them to the published spectra:[1] At present no artificial source is recommended to realize CIE standard illuminant D65 or any other illuminant D of different CCT. It is hoped that new developments in light sources and filters will eventually offer sufficient basis for a CIE recommendation.
    [Show full text]
  • Color Appearance Models Second Edition
    Color Appearance Models Second Edition Mark D. Fairchild Munsell Color Science Laboratoiy Rochester Institute of Technology, USA John Wiley & Sons, Ltd Contents Series Preface xiii Preface XV Introduction xix 1 Human Color Vision 1 1.1 Optics of the Eye 1 1.2 The Retina 6 1.3 Visual Signal Processing 12 1.4 Mechanisms of Color Vision 17 1.5 Spatial and Temporal Properties of Color Vision 26 1.6 Color Vision Deficiencies 30 1.7 Key Features for Color Appearance Modeling 34 2 Psychophysics 35 2.1 Psychophysics Defined 36 2.2 Historical Context 37 2.3 Hierarchy of Scales 40 2.4 Threshold Techniques 42 2.5 Matching Techniques 45 2.6 One-Dimensional Scaling 46 2.7 Multidimensional Scaling 49 2.8 Design of Psychophysical Experiments 50 2.9 Importance in Color Appearance Modeling 52 3 Colorimetry 53 3.1 Basic and Advanced Colorimetry 53 3.2 Whyis Color? 54 3.3 Light Sources and Illuminants 55 3.4 Colored Materials 59 3.5 The Human Visual Response 66 3.6 Tristimulus Values and Color Matching Functions 70 3.7 Chromaticity Diagrams 77 3.8 CIE Color Spaces 78 3.9 Color Difference Specification 80 3.10 The Next Step , 82 viii CONTENTS 4 Color Appearance Terminology 83 4.1 Importance of Definitions 83 4.2 Color 84 4.3 Hue 85 4.4 Brightness and Lightness 86 4.5 Colorfulness and Chroma 87 4.6 Saturation 88 4.7 Unrelated and Related Colors 88 4.8 Definitions in Equations 90 4.9 Brightness-Colorfulness vs Lightness-Chroma 91 5 Color Order Systems 94 5.1 Overvlew and Requirements 94 5.2 The Munsell Book of Color 96 5.3 The Swedish Natural Color System (NCS)
    [Show full text]
  • Sub-Micrometer Nanostructure-Based RGB Filters for CMOS Image Sensors
    Sub-micrometer Nanostructure-based RGB Filters for CMOS Image Sensors Jonas Berzinš,1,2,* Stefan Fasold,1 Thomas Pertsch,1,3 Stefan M. B. Bäumer,2 Frank Setzpfandt1 1Institute of Applied Physics, Friedrich Schiller University Jena, Albert-Einstein-Str. 15, 07745 Jena, Germany 2TNO Optics Department, Stieltjesweg 1, 2628CK Delft, The Netherlands 3Fraunhofer Institute for Applied Optics and Precision Engineering, Albert-Einstein-Str. 7, 07745 Jena, Germany *E-mail: [email protected] Abstract. Digital color imaging relies on spectral filters on top of a pixelated sensor, such as a CMOS image sensor. An important parameter of imaging devices is their resolution, which depends on the size of the pixels. For many applications, a high resolution is desirable, consequently requiring small spectral filters. Dielectric nanostructures, due to their resonant behavior and its tunability, offer the possibility to be assembled into flexible and miniature spectral filters, which could potentially replace conventional pigmented and dye-based color filters. In this paper, we demonstrate the generation of transmissive structural colors based on uniform-height amorphous silicon nanostructures. We optimize the structures for the primary RGB colors and report the construction of sub-micrometer RGB filter arrays for a pixel size down to 0.5 μm. Keywords: sub-micrometer pixel, dielectric nanostructures, color filter, RGB, image sensor In the past decades we have observed a rapid growth of the spatial resolution of color imaging devices1–8. This technological outburst was mainly driven by daily-use hand-held devices, such as mobile phones and compact optical cameras, but a high resolution also serves a role in industrial and environmental imaging9.
    [Show full text]
  • Color Vision and Colorimetry THEORY and APPLICATIONS SECOND EDITION
    Color Vision and Colorimetry THEORY and APPLICATIONS SECOND EDITION Daniel Malacara SPIE PRESS Bellingham, Washington USA Contents Preface to the Second Edition ix Preface to the First Edition xi Chapter 1 The Nature of Color 1 1.1 Introduction 1 1.2 Newton's Color Experiment 4 1.3 Theories and Experiments in Color Vision 4 1.4 Some Radiometric and Photometric Units 8 1.4.1 Radiometric units 8 1.4.2 Photometric units 10 1.5 Color Sensitivity of the Eye 14 1.6 How Materials are Colored or Modify Color 16 1.7 Absorption and Interference Filters 18 References 20 Chapter 2 Light Sources and llluminants 23 2.1 Introduction 23 2.2 Blackbody Radiation and Color Temperature 23 2.3 Tungsten Lamps 24 2.4 Gas Discharge and Fluorescent Lamps 26 2.5 Light-Emitting Diodes 28 2.6 Television and Computer Displays 31 2.7 Standard Light Sources and llluminants 33 2.8 Color-Rendering Index of Light Sources 35 References 38 Chapter 3 The Human Eye 41 3.1 Anatomy of the Eye 41 3.2 Eye Resolving Power and Eye Aberrations 44 3.3 Stiles-Crawford Effect 45 3.4 Eye Response to Pulsating Light 45 3.5 Visual Detectors in the Retina 47 v vi Contents 3.6 Observation of the Human Eye Retina 48 3.7 Cone Fundamentals 51 References 54 Chapter 4 Trichromatic Theory 59 4.1 Grassmann Laws 59 4.2 Maxwell's Triangle 59 4.3 Color-Matching Experiments 60 4.4 Color-Matching Functions г(Л), g(Ä), and Ъ{Л) 63 4.5 Tristimulus Values R, G, В 64 4.6 Chromaticity Coordinates r, g, b 68 References 72 Chapter 5 CIE Color Specification System 75 5.1 The CIE Color System 75 5.2 Color-Matching
    [Show full text]
  • Instrumental Colorimetry of Retrorefective Sign Materials
    NBSIR 74-518 Instrumental Colorimetry of Retroreflective Sign Materials I. Nimeroff, W. A. Hall Optical Radiation Section Heat Division Institute for Basic Standards August, 1974 Prepared for Federal Highway Administration Office of Research Washington, D. C. 20590 NBSIR 74-518 INSTRUMENTAL COLORIMETRY OF RETROREFLECTIVE SIGN MATERIALS I. Nimeroff, W. A. Hall Optical Radiation Section Heat Division Institute for Basic Standards August, 1974 Prepared for Federal Highway Administration Office of Research Washington, D. C. 20590 U. S. DEPARTMENT OF COMMERCE, Frederick B. Dent. Secretary NATIONAL BUREAU OF STANDARDS. Richard W. Roberts. Director PREFACE At the request of the Federal Highway Administration of the Department of U S Transportation, the National Bureau of Standards conducted a study to develop an instrumental procedure by which to specify and measure colors of retroreflective materials, as seen under nighttime illuminating and viewing conditions. The work included a study of the colorimetric properties of retroreflective materials used for highway signs and markings as evaluated under illuminating and viewing geometry, particularly those^hr^'^iv^^r'^^''^?''^that simulate nighttime use conditions. The opinions, findings, and conclusions this m report are those of the authors and are not necessarily those of the Federal Highway Administration. Certain commercially available equipment, instruments, or materials are identified this m report to specify adequately the experimental procedures used. In no case should such identification be inferred as recommendation or endorsement by the National Bureau of Standards, nor does the identification imply that the materials or are equipment necessarily the best available for the purpose i CONTENTS ' . Page PREFACE i I. Introduction <..... 1 1.1 Statement of Work 2 1.2 Partial Support by Federal Aviation Administration 3 2.
    [Show full text]