DOCSLIB.ORG

COLOR APPEARANCE MODELS Mehdi Rahimzadeh

Total Page：16

File Type：pdf, Size：1020Kb

Download full-text PDF Read full-text
COLOR APPEARANCE MODELS Mehdi Rahimzadeh COLOR APPEARANCE MODELS Mehdi Rahimzadeh 1 What is color ? • An Attribute of visual sensation • Cannot be defined without examples 2 Rayleigh Scattering • For small particles x<<1 • Scattered rays • Lower wavelength, higher scattering 3 Rayleigh Scattering 4 Color properties: Color • Attribute of visual perception consisting of any combination of chromatic and achromatic content 5 Color properties: Hue • An area appears to be similar to one of the perceived colors: • Red, Yellow, Green, Blue 6 Color properties: Brightness and Lightness • Brightness: An area appears to emit more or less light • Lightness: The brightness of an area judged relative 퐵푟푔ℎ푡푛푒푠푠 퐿푔ℎ푡푛푒푠푠 = to brightness of a similarly 퐵푟푔ℎ푡푛푒푠푠 (푊ℎ푡푒) illuminated white area 7 Color properties: Colorfulness and Chroma • Colorfulness: An area appears to be more or less chromatic • Chroma: The colorfulness of an area judged as a 퐶표푙표푟푓푢푙푛푒푠푠 퐶ℎ푟표푚푎 = proportion of the brightness 퐵푟푔ℎ푡푛푒푠푠 (푊ℎ푡푒) of similarly illuminated white 8 Color properties: Saturation • Colorfulness of an area judged to its brightness 퐶ℎ푟표푚푎 푆푎푡푢푟푎푡표푛 = 퐿푔ℎ푡푛푒푠푠 퐶표푙표푟푓푢푙푛푒푠푠 퐵푟푔ℎ푡푛푒푠푠 (푊ℎ푡푒) 푆푎푡푢푟푎푡표푛 = × 퐵푟푔ℎ푡푛푒푠푠 (푊ℎ푡푒) 퐵푟푔ℎ푡푛푒푠푠 퐶표푙표푓푢푙푛푒푠푠 푆푎푡푢푟푎푡표푛 = 퐵푟푔ℎ푡푛푒푠푠 9 Color properties: • Brightness • Lightness • Colorfulness • Chroma • Hue 10 Human Color Perception: • Color spectrum • Cone responses (L, M, S) 11 Opponent Color Theory: • Primary colors are encoded before sending to brain 12 US Flag afterimage: 13 US Flag afterimage: 14 CIE 1937 XYZ matching functions: 15 Color Appearance Phenomena: Simultaneous Contrast • Darker background cause the gray ribbon appear lighter 16 Color Appearance Phenomena: Simultaneous Contrast • Robertson (1996) • Darker background cause the gray ribbon appear lighter 17 Color Appearance Phenomena: Crispening • Increase in perceived magnitude of color differences when the background is similar in color 18 Color Appearance Phenomena: Bezold-Brucke Hue Shift • Hue changes with luminance • Wavelength shift required to maintain constant hue across a 10X reduction in luminance 19 Color Appearance Phenomena: • Hunt Effect: Colorfulness increases with luminance • Stevens Effect: Contrast increases with luminance 20 Color Appearance Phenomena: Helson-Judd effect • Under strongly monochromatic illumination • Samples lighter than the background exhibited Chroma of the same hue as the source • Samples darker than background exhibited Chroma of the hue of the source’s complement 21 Color Appearance Phenomena: Discounting-the-Illuminant • Cognitive ability of observers to interpret the colors of objects based on illuminated environment in which the are viewed • White objects appear white under tungsten light, fluorescent light, and daylight. 22 Color Appearance Model: • Any model that includes predictors of at least the relative color-appearance attributes of lightness, chroma, and hue • It must include at least some form of a chromatic-adaptation transform 23 Color Appearance Model: • Transform of XYZ tri-stimulus values to cone responses • Viewing environment (tri-stimulus of adaptation stimulus) 24 CIELAB: • Stimulus XYZ • Reference White 푋푛, 푌푛, 푍푛 • Results in L (luminance), a, and b (chrominance) parameters 25 CIELAB: • 푎: red-green • 푏: yellow-blue • 퐿 = 116 푌 푌푛 − 16 • 푎 = 500[푓(푋 푋푛) − 푓(푌 푌푛)] • 푏 = 200[푓(푌 푌푛) − 푓(푍 푍푛) 26 CIELAB: 휔1/3 • 푓 휔 = 휔>0.008856 7.787휔 + 16/116 휔≤0.008856 27 CIELAB: 2 2 • Chroma: 퐶푎푏 = 푎 + 푏 −1 푏 • Hue: ℎ푎푏 = tan ( 푎) 28 CIELAB: • Contours of constant Chroma and Hue • Lines: constant Hue • Circles: constant Chroma 29 CIELAB: Pros and Cons • Not accurate von Kries adaptation transformation • Incapable of predicting luminance-depending effects (Hunt and Stevens effect) • Incorporate no background or surround dependency • No modeling for discounting-the-illuminant 30 Nayatani et al: • Luminance factor of achromatic background (푌0) • Color of the illumination (푥0, 푦0) • Test stimulus in (푥, 푦, 푌) • Absolute luminance of the stimulus (퐸0) • Normalizing illuminance 퐸표푟 • Noise term (푛) 31 Nayatani et al: • Converting XYZ to intermediate values • 휉 = (0.48105푥0 + 0.78841푦0 − 0.08081)/푦0 • 휂 = (0.27200푥0 + 1.11962푦0 − 0.04570)/푦0 • 휁 = 0.91822(1 − 푥0 − 푦0)/푦0 32 Nayatani et al: adaptation model 33 Nayatani et al: Opponent color dimension Achromatic response Chromatic response 34 Nayatani et al: Pros and Cones • Accounts for change in color appearance due to chromatic adaptation and luminance level (Stevens and Hunt effect) • Predicts Helsun-Judd effect • No chromatic or cognitive adaptation (von Kries and discounting-the-illuminant) • Not incorporate rod vision 35 Hunt Model: • Heavily depends on surround relative luminance • Transparencies projected in a dark room • Television displays in a dim surround 36 Hunt Model: • Chromaticity coordinates of the illuminant and the adapting field • Chromaticity and luminance factors of the background, reference white and test sample 37 Hunt Model: adaptation model • 퐹퐿: Luminance-level adaptation factor • 퐹휌, 퐹훾, and 퐹훽 are chromatic adaptation factors • 퐷, 훾퐷, and 훽퐷 are Helson-Judd effect factors • 퐵휌, 퐵훾, and 퐵훽 are cone bleach factors 38 Hunt Model: adaptation model 39 Hunt Model: Opponent color dimension A: Achromatic post-adaptation signal 40 Hunt Model: Pros and Cons • Designed to predict the appearance of stimuli in a variety background and surround • Predicts Hunt effect, Hue shift, Helson-Judd effect, Simultaneous constant, Stevens effect • Predicts chromatic adaptation due to discounting-the-illuminant 41 Hunt Model: Pros and Cons • It is very complex model • Extreme poor result on incomplete input data • Cannot be easily converted • Expensive and difficult to implement 42 RLAB: • XYZ of the test object • 푋푛, 푌푛, 푍푛 of the white point • Absolute luminance of the white object in 푐푑/푚2 • Relative luminance of the surround • Is discounting-the-illuminant taking place? 43 RLAB: • M: Cone Response Transformation • A: von Kries adaptation • R: 퐴−1푀−1for reference viewing conditions 44 RLAB: Opponent color dimension • Analogous to CIELAB • Average surround = 1/2.3 • Dim surround = 1/2.7 • Dark surround = 1/3.5 45 RLAB: • Adaptation to dark surround 46 RLAB: Pros and Cons • Predicts discounting-the-illuminant • Includes variable exponent for better contrast in different surroundings • Not exhaustive in predicting color-appearance phenomena (Hunt, Helson-Judd, Stevens) • Not over a wide range of luminance 47.
Load more

Recommended publications
  • Color Models
    Color Models Jian Huang CS456 Main Color Spaces • CIE XYZ, xyY • RGB, CMYK • HSV (Munsell, HSL, IHS) • Lab, UVW, YUV, YCrCb, Luv, Differences in Color Spaces • What is the use? For display, editing, computation, compression, …? • Several key (very often conflicting) features may be sought after: – Additive (RGB) or subtractive (CMYK) – Separation of luminance and chromaticity – Equal distance between colors are equally perceivable CIE Standard • CIE: International Commission on Illumination (Comission Internationale de l’Eclairage). • Human perception based standard (1931), established with color matching experiment • Standard observer: a composite of a group of 15 to 20 people CIE Experiment CIE Experiment Result • Three pure light source: R = 700 nm, G = 546 nm, B = 436 nm. CIE Color Space • 3 hypothetical light sources, X, Y, and Z, which yield positive matching curves • Y: roughly corresponds to luminous efficiency characteristic of human eye CIE Color Space CIE xyY Space • Irregular 3D volume shape is difficult to understand • Chromaticity diagram (the same color of the varying intensity, Y, should all end up at the same point) Color Gamut • The range of color representation of a display device RGB (monitors) • The de facto standard The RGB Cube • RGB color space is perceptually non-linear • RGB space is a subset of the colors human can perceive • Con: what is ‘bloody red’ in RGB? CMY(K): printing • Cyan, Magenta, Yellow (Black) – CMY(K) • A subtractive color model dye color absorbs reflects cyan red blue and green magenta green blue and red yellow blue red and green black all none RGB and CMY • Converting between RGB and CMY RGB and CMY HSV • This color model is based on polar coordinates, not Cartesian coordinates.
    [Show full text]
  • Know the Color Wheel Primary Color
    Introduction to Color/Hue Theory With Marlene Oaks Color affects us psychologically in nature, clothing, quilts, art and in decorating. The color choices we make create varying responses. Being able to use colors consciously and harmoniously can help us create spectacular results. Know the Color Wheel Primary color Primary color Primary color The color wheel is the basic tool for combining colors. The first circular color diagram was designed by Sir Isaac Newton in 1666. Primary, Secondary and Tertiary Colors Color theory in regards to light says that all colors are within white light—think prism, and black is devoid of color. In pigment theory, white is the absence of color & black contains all colors. We will be discussing pigment theory here. The primary colors are red, yellow and blue and most other colors can be made by various combinations of them along with the neutrals. The three secondary colors (green, orange and purple) are created by mixing two primary colors. Another six tertiary colors are created by mixing primary and secondary colors adjacent to each other. The above illustration shows the color circle with the primary, secondary and tertiary colors. 1 Warm and cool colors The color circle can be divided into warm and cool colors. Warm colors are energizing and appear to come forward. Cool colors give an impression of calm, and appear to recede. White, black and gray are considered to be neutral. Tints - adding white to a pure hue: Terms about Shades - adding black to a pure hue: hue also known as color Tones - adding gray to a pure hue: Test for color blindness NOTE: Color theory is vast.
    [Show full text]
  • An Improved SPSIM Index for Image Quality Assessment
    S S symmetry Article An Improved SPSIM Index for Image Quality Assessment Mariusz Frackiewicz * , Grzegorz Szolc and Henryk Palus Department of Data Science and Engineering, Silesian University of Technology, Akademicka 16, 44-100 Gliwice, Poland; [email protected] (G.S.); [email protected] (H.P.) * Correspondence: [email protected]; Tel.: +48-32-2371066 Abstract: Objective image quality assessment (IQA) measures are playing an increasingly important role in the evaluation of digital image quality. New IQA indices are expected to be strongly correlated with subjective observer evaluations expressed by Mean Opinion Score (MOS) or Difference Mean Opinion Score (DMOS). One such recently proposed index is the SuperPixel-based SIMilarity (SPSIM) index, which uses superpixel patches instead of a rectangular pixel grid. The authors of this paper have proposed three modifications to the SPSIM index. For this purpose, the color space used by SPSIM was changed and the way SPSIM determines similarity maps was modified using methods derived from an algorithm for computing the Mean Deviation Similarity Index (MDSI). The third modification was a combination of the first two. These three new quality indices were used in the assessment process. The experimental results obtained for many color images from five image databases demonstrated the advantages of the proposed SPSIM modifications. Keywords: image quality assessment; image databases; superpixels; color image; color space; image quality measures Citation: Frackiewicz, M.; Szolc, G.; Palus, H. An Improved SPSIM Index 1. Introduction for Image Quality Assessment. Quantitative domination of acquired color images over gray level images results in Symmetry 2021, 13, 518. https:// the development not only of color image processing methods but also of Image Quality doi.org/10.3390/sym13030518 Assessment (IQA) methods.
    [Show full text]
  • 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.
    [Show full text]
  • Urban Land Grab Or Fair Urbanization?
    Urban land grab or fair urbanization? Compulsory land acquisition and sustainable livelihoods in Hue, Vietnam Stedelijke landroof of eerlijke verstedelijking? Landonteigenlng en duurzaam levensonderhoud in Hue, Vietnam (met een samenvatting in het Nederlands) Chiếm đoạt đất đai đô thị hay đô thị hoá công bằng? Thu hồi đất đai cưỡng chế và sinh kế bền vững ở Huế, Việt Nam (với một phần tóm tắt bằng tiếng Việt) Proefschrift ter verkrijging van de graad van doctor aan de Universiteit Utrecht op gezag van de rector magnificus, prof.dr. G.J. van der Zwaan, ingevolge het besluit van het college voor promoties in het openbaar te verdedigen op maandag 21 december 2015 des middags te 12.45 uur door Nguyen Quang Phuc geboren op 10 december 1980 te Thua Thien Hue, Vietnam Promotor: Prof. dr. E.B. Zoomers Copromotor: Dr. A.C.M. van Westen This thesis was accomplished with financial support from Vietnam International Education Development (VIED), Ministry of Education and Training, and LANDac programme (the IS Academy on Land Governance for Equitable and Sustainable Development). ISBN 978-94-6301-026-9 Uitgeverij Eburon Postbus 2867 2601 CW Delft Tel.: 015-2131484 [email protected]/ www.eburon.nl Cover design and pictures: Nguyen Quang Phuc Cartography and design figures: Nguyen Quang Phuc © 2015 Nguyen Quang Phuc. 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, or otherwise, without the prior permission in writing from the proprietor. © 2015 Nguyen Quang Phuc.
    [Show full text]
  • Several Color Appearance Phenomena in Color Reproduction
    2nd International Conference on Electronic & Mechanical Engineering and Information Technology (EMEIT-2012) Several Color Appearance Phenomena in Color Reproduction Qin-ling Dai1,a, Xiao-zhou Li2*,b, Ai Xu2 1 School of Materials Engineering (Southwest Forestry University), Kunming, China 650224 2 Key Laboratory of Pulp & Paper Science and Technology (Shandong Polytechnic University), Ministry of Education, Ji’nan, China, 250353 [email protected], bcorresponding author: [email protected] Keywords: color reproduction, color appearance, color appearance phenomena Abstract. Color perceived performance was influenced by various color appearance phenomena caused by varying viewing conditions in color reproduction process. It is necessary to do some research on the color appearance phenomena to represent the color appearance models qualitatively and quantitatively and accurate color reproduction easily. Only the phenomena were studied thoroughly, could the color transmission and reproduction be well performed. The color appearance and common color appearance phenomena of color reproduction were analyzed in this paper. And the basic theory of color appearance in color reproduction was also studied. Introduction High fidelity digital printing plays an important role in high fidelity color transmission and reproduction and it is one of the most important techniques to perform high fidelity color reproduction. High fidelity digital printing helps to perform accurate color reproduction of the original which can’t be performed because of paper and ink in traditional printing process [1]. In color printing, the color difference caused by paper, ink and viewing conditions is various. The difference is not only colorimetric difference but also different color appearance phenomena. While the different color appearance phenomenon is the leading factor to influence the color vision perceived.
    [Show full text]
  • Prediction of Munsell Appearance Scales Using Various Color-Appearance Models
    Prediction of Munsell Appearance Scales Using Various Color- Appearance Models David R. Wyble,* Mark D. Fairchild Munsell Color Science Laboratory, Rochester Institute of Technology, 54 Lomb Memorial Dr., Rochester, New York 14623-5605 Received 1 April 1999; accepted 10 July 1999 Abstract: The chromaticities of the Munsell Renotation predict the color of the objects accurately in these examples, Dataset were applied to eight color-appearance models. a color-appearance model is required. Modern color-appear- Models used were: CIELAB, Hunt, Nayatani, RLAB, LLAB, ance models should, therefore, be able to account for CIECAM97s, ZLAB, and IPT. Models were used to predict changes in illumination, surround, observer state of adapta- three appearance correlates of lightness, chroma, and hue. tion, and, in some cases, media changes. This definition is Model output of these appearance correlates were evalu- slightly relaxed for the purposes of this article, so simpler ated for their uniformity, in light of the constant perceptual models such as CIELAB can be included in the analysis. nature of the Munsell Renotation data. Some background is This study compares several modern color-appearance provided on the experimental derivation of the Renotation models with respect to their ability to predict uniformly the Data, including the specific tasks performed by observers to dimensions (appearance scales) of the Munsell Renotation evaluate a sample hue leaf for chroma uniformity. No par- Data,1 hereafter referred to as the Munsell data. Input to all ticular model excelled at all metrics. In general, as might be models is the chromaticities of the Munsell data, and is expected, models derived from the Munsell System per- more fully described below.
    [Show full text]
  • Illumination and Distance
    PHYS 1400: Physical Science Laboratory Manual ILLUMINATION AND DISTANCE INTRODUCTION How bright is that light? You know, from experience, that a 100W light bulb is brighter than a 60W bulb. The wattage measures the energy used by the bulb, which depends on the bulb, not on where the person observing it is located. But you also know that how bright the light looks does depend on how far away it is. That 100W bulb is still emitting the same amount of energy every second, but if you are farther away from it, the energy is spread out over a greater area. You receive less energy, and perceive the light as less bright. But because the light energy is spread out over an area, it’s not a linear relationship. When you double the distance, the energy is spread out over four times as much area. If you triple the distance, the area is nine Twice the distance, ¼ as bright. Triple the distance? 11% as bright. times as great, meaning that you receive only 1/9 (or 11%) as much energy from the light source. To quantify the amount of light, we will use units called lux. The idea is simple: energy emitted per second (Watts), spread out over an area (square meters). However, a lux is not a W/m2! A lux is a lumen per m2. So, what is a lumen? Technically, it’s one candela emitted uniformly across a solid angle of 1 steradian. That’s not helping, is it? Examine the figure above. The source emits light (energy) in all directions simultaneously.
    [Show full text]
  • "He" Had Me at Blue: Color Theory and Visual
    Downloaded from http://www.mitpressjournals.org/doi/pdf/10.1162/LEON_a_00677 by guest on 30 September 2021 general article “He” Had Me at Blue: Color Theory and Visual Art Barbara L. Miller a b s t r a c t Schopenhauer and Goethe argued that colors are danger- ous: When philosophers speak Blue is the colour of your yellow hair of colors, they often begin Red is the whirl of your green wheels to rant and rave. This essay addresses the confusing and ing effects. It can leave an intolera- —Kurt Schwitters treacherous history of color the- ble and “powerful impression” and ory and perception. An overview result in a type of visual incapaci- of philosophers and scientists Color Mad tation that, he suggests, “may last associated with developing for hours” [3]. Exposure to blazing theories leads into a discussion of contemporary perspectives: A friend and colleague once confided that she hated yellow light—“red” or “white” light, as the flowers: “I can’t,” she blustered, “have them in my garden.” Taussig’s notion of a “combus- fictional character cries—in real tible mixture” and “total bodily “You sound like a scene from a Hitchcock movie!” I teased, life can result in blinding after- activity” and Massumi’s idea of and Tippi Hedren as Marnie flashed before my eyes. effects; for example, walking out an “ingressive activity” are used of a dark corridor into a bright, sun- as turning points in a discussion Marnie: “First there are three taps.” of Roger Hiorns’s Seizure—an Thunder claps. Marnie swoons, wailing: “Needles .
    [Show full text]
  • Colornet--Estimating Colorfulness in Natural Images
    COLORNET - ESTIMATING COLORFULNESS IN NATURAL IMAGES Emin Zerman∗, Aakanksha Rana∗, Aljosa Smolic V-SENSE, School of Computer Science, Trinity College Dublin, Dublin, Ireland ABSTRACT learning-based objective metric ‘ColorNet’ for the estimation of colorfulness in natural images. Based on a convolutional neural Measuring the colorfulness of a natural or virtual scene is critical network (CNN), our proposed ColorNet is a two-stage color rating for many applications in image processing field ranging from captur- model, where at stage I, a feature network extracts the characteristics ing to display. In this paper, we propose the first deep learning-based features from the natural images and at stage II, a rating network colorfulness estimation metric. For this purpose, we develop a color estimates the colorfulness rating. To design our feature network, rating model which simultaneously learns to extracts the pertinent we explore the designs of the popular high-level CNN based fea- characteristic color features and the mapping from feature space to ture models such as VGG [22], ResNet [23], and MobileNet [24] the ideal colorfulness scores for a variety of natural colored images. architectures which we finally alter and tune for our colorfulness Additionally, we propose to overcome the lack of adequate annotated metric problem at hand. We also propose a rating network which dataset problem by combining/aligning two publicly available color- is simultaneously learned to estimate the relationship between the fulness databases using the results of a new subjective test which characteristic features and ideal colorfulness scores. employs a common subset of both databases. Using the obtained In this paper, we additionally overcome the challenge of the subjectively annotated dataset with 180 colored images, we finally absence of a well-annotated dataset for training and validating Col- demonstrate the efficacy of our proposed model over the traditional orNet model in a supervised manner.
    [Show full text]
  • Sunlight Readability and Luminance Characteristics of Light
    SUNLIGHT READABILITY AND LUMINANCE CHARACTERISTICS OF LIGHT- EMITTING DIODE PUSH BUTTON SWITCHES Robert J. Fitch, B.S.E.E., M.B.A. Thesis Prepared for the Degree of MASTER OF SCIENCE UNIVERSITY OF NORTH TEXAS May 2004 APPROVED: Albert B. Grubbs, Jr., Major Professor and Chair of the Department of Engineering Technology Don W. Guthrie, Committee Member Michael R. Kozak, Committee Member Roman Stemprok, Committee Member Vijay Vaidyanathan, Committee Member Oscar N. Garcia, Dean of the College of Engineering Sandra L. Terrell, Interim Dean of the Robert B. Toulouse School of Graduate Studies Fitch, Robert J., Sunlight readability and luminance characteristics of light- emitting diode push button switches. Master of Science (Engineering Technology), May 2004, 69 pp., 7 tables, 9 illustrations, references, 22 titles. Lighted push button switches and indicators serve many purposes in cockpits, shipboard applications and military ground vehicles. The quality of lighting produced by switches is vital to operators’ understanding of the information displayed. Utilizing LED technology in lighted switches has challenges that can adversely affect lighting quality. Incomplete data exists to educate consumers about potential differences in LED switch performance between different manufacturers. LED switches from four different manufacturers were tested for six attributes of lighting quality: average luminance and power consumption at full voltage, sunlight readable contrast, luminance contrast under ambient sunlight, legend uniformity, and dual-color uniformity. Three of the four manufacturers have not developed LED push button switches that meet lighting quality standards established with incandescent technology. Copyright 2004 by Robert J. Fitch ii ACKNOWLEDGMENTS I thank Don Guthrie and John Dillow at Aerospace Optics, Fort Worth, Texas, for providing the test samples, lending the use of their laboratories, and providing tremendous support for this research.
    [Show full text]
  • The Helmholtz-Kohlrausch Effect
    Out of the Wood BY MIKE WOOD Lightness— The Helmholtz-Kohlrausch effect Don’t be put off by the strange name also strongly suggest looking at the online lightness most people see when looking at of this issue’s article. The Helmholtz- version of this article, as the images will be this image. Kohlrausch (HK) effect might sound displayed on your monitor and behave Note: Not everyone will see the differences esoteric, but it’s a human eye behavioral more like lights than the ink pigments in as strongly, particularly with a printed page effect with colored lighting that you are the printed copy. You can access Protocol like this. Just about everyone sees this effect undoubtedly already familiar with, if issues on-line at http://plasa.me/protocol with lights, but the strength of the effect varies perhaps not under its formal name. This or through the iPhone or iPad App at from individual to individual. In particular, effect refers to the human eye (or entoptic) http://plasa.me/protocolapp. if you are red-green color-blind, then you may phenomenon that colored light appears The simplest way to show the Helmholtz- see very little difference. brighter to us than white light of the same Kohlrausch effect is with an illustration. What I see—which will agree with what luminance. This is particularly relevant to The top half of Figure 1 shows seven most of you see—is that the red and pink the entertainment industry, as it is most differently colored patches against a grey patches look by far the brightest, while blue, obvious when using colored lights.
    [Show full text]