DOCSLIB.ORG

Introduction to Color Appearance Models Outline

Total Page：16

File Type：pdf, Size：1020Kb

Download full-text PDF Read full-text

Load more

Introduction to Color Appearance Models Arto Kaarna Lappeenranta University of Technology Department of Information Technology P.O. Box 20 FIN-53851 Lappeenranta, Finland [email protected] Basics of CAM … 1/81 Outline 1. Definitions 3 2. Color Appearance Phenomena 15 3. Chromatic Adaptation 44 4. Color appearance models 59 5. CIECAM02 65 Literature 80 Basics of CAM … 2/81 1 Definitions Needed for uniform and universal description Precise for mathematical manipulation International Lighting Vocabulary (CIE, 1987) Within color science Color Hue Brightness Lightness Colorfulness Chroma Saturation Unrelated and Related Colors Basics of CAM … 3/81 Definitions Color Visual perception of chromatic or achromatic content •Yellow, blue, red, etc •White, gray, black, etc. •Dark, dim, bright, light, etc. NOTE: perceived color depends on the spectral distribution of the stimulus, size, shape, structure, and surround •Also state of adaptation of the observer Both physical, physiological, psyckological and cognitive variables New attempt: visual stimulus without spatial or temporal variations More detailed definition needed using more parameters for numeric expression Basics of CAM … 4/81 2 Definitions Hue Visual sensation similar to one of the perceived colors: red, yellow, green, and blue or their combination Chromatic color: posessing a hue, achromatic no hue Munsell book of color No hue with zero value Achromatic colors Basics of CAM … 5/81 Definitions Brightness A visual sensation according to which an area emits more or less light An absolute level of perception Lightness The brightness of an area judged relative to the brightness of a similarly illuminated area that is white or highly transmitting, thus a relative value For example: A page from a book Office •Brightness: some value •Lightness: high value Sunny day: •Higher brightness value •Roughly the same lightness as in office Basics of CAM … 6/81 3 Definitions Basics of CAM … 7/81 Definitions Colorfulness A visual sensation in which an area appears to be more or less chromatic Defines the intensitivity of the hue of a given stimulus Chroma Colorfulness of an area as a proportion of the brightness of a similarly illuminated area of white or high transmittance The third dimension of 3D color vision (hue+brightness/lightness+colorfulness/chroma) Colorfulness and chroma in similar relationship as brightness and lightness Zero colorfulness or chroma: achromatic colors Basics of CAM … 8/81 4 Definitions Increase in lightness Increase in colorfulness Basics of CAM … 9/81 Definitions Saturation Colorfulness of an area in proportion to its own brightness Relative colorfulness Traffic lights at night Increase in saturation Increase in lightness Increase in colorfulness Basics of CAM … 10/81 5 Definitions Unrelated Colors Seen in isolation of other colors Many visual experiments with unrelated colors Related Colors Seen in relation to other colors Color appearance applications deal with related colors Colors like gray and brown Gray has lower lightness than white Brown is an orange color with low lightness Gray/brown light in dark environment? Basics of CAM … 11/81 Definitions Unrelated Colors Perceptual attributes are hue, brightness, colorfulness, and saturation No attributes related to similarly illuminated white Related colors Perceptual attributes are hue, brightness, lightness, colorfulness, chroma, and saturation Test: White/orange illuminant in dark environment surrounded with an increasing white illuminant? White/orange illuminant in highly illuminated environment? What is the color of the white/orange illuminant? Basics of CAM … 12/81 6 Definitions Finally we get the equations Colorfulness Colorfulness Chroma = Saturation = Brightness(white) Brightness Brightness Lightness = Brightness(white) Chroma Saturation = Lightness Colorfulness Brightness(white) = × Brightness(white) Brightness Five perceptual dimensions for color appearance Brightness, lightness, colorfulness, chroma, hue, saturation is redundant Basics of CAM … 13/81 Definitions Brightness-colorfulness vs. lightness-chroma Example Object in sunny day: hue(red), high brightness, lightness, colorfulness, chroma Object in office: hue, lightness and chroma same, brightness and colorfulness lower Basics of CAM … 14/81 7 Color Appearance Phenomena Relationships in viewing conditions and color appearance, examples Simultaneous Contrast Josef Albers Complex Spatial Structures Hunt Effect Stevens Effect Bartleson-Breneman Surround Effect Basics of CAM … 15/81 Color Appearance Phenomena Equal (XYZ)1 = (XYZ)2 appear similar Similar viewing conditions for both samples: surrounds, backgrouds, size, shape, surface, illumination, etc. Differences can be computed from (XYZ) pairs Basics of CAM … 16/81 8 Color Appearance Phenomena If two samples with (XYZ)1 = (XYZ)2 look different then there is a change in viewing conditions Basics of CAM … 17/81 Color Appearance Phenomena Basics of CAM … 18/81 9 Color Appearance Phenomena Cognition through vision Basics of CAM … 19/81 Hermann Grid Basics of CAM … 20/81 10 Scintillation Effect Basics of CAM … 21/81 Basics of CAM … 22/81 11 Basics of CAM … 23/81 Simultaneous Contrast The background influences the apparent color of the sample Bg change Sample change Darker Lighter Lighter Darker Red Green Green Red Yellow Blue Blue Yellow Basics of CAM … 24/81 12 Simultaneous Contrast Constant gray-level of a sample Basics of CAM … 25/81 Simultaneous Contrast Constant color of a sample Robertson (1996): yellow stripe ĺ square gets bluer Basics of CAM … 26/81 13 Josef Albers Studies on simulteneous contrast Josef Albers: Interaction of Color, 1963 Basics of CAM … 27/81 Crispening Similar gray-level at different backgrounds Differences between squares change with different backgrounds Basics of CAM … 28/81 14 Spreading High-frequency sample close to background Left: more greenish, right: more reddish Basics of CAM … 29/81 Bezold-Brucke Hue cannot be determined directly from wavelength Hue shift: hue changes with luminance Largest change in red-yellow area 650nm ĺ 620 nm when luminance is 1/10th of the original Basics of CAM … 30/81 15 Abney effect Mixing of monocromatic light with white light does not preserve constant hue Y 0.8 0.6 Contours at constant hue Constant hue produced by various wavelengths 0.4 0.2 0.2 0.4 0.6 0.8 x Basics of CAM … 31/81 Helmholtz-Kohlrausch Brightness depends on luminance and chromaticity Correction factor: F = 0.256 - 0.184 y - 2.527 xy + 4.656 x 3 y + 4.657 xy 4 Equal brightness: log( L1 ) + F1 = log( L 2 ) + F2 Y 0.8 0.6 1.2 Contours at constant brightness- 1.3 1.1 to-luminance ratio 1.4 Constant luminance ĺ perceived 0.4 1.0 brightness increases when 1.5 stimulus becomes more chromatic 0.2 0.2 0.4 0.6 0.8 x Basics of CAM … 32/81 16 Hunt Effect When luminance level changes the color appearance of objects changes Objects vivid and contrasty in summer afternoon Subdued in dusk Y 10000 0.8 1000 For a constant chromaticity, perceived colorfulness increases 100 0.6 with luminance 10 With luminance increase, a lower colorimetric purity is needed to 0.4 1 match with the reference stimulus 10000 1000 100 10 1 0.2 0.2 0.4 0.6 0.8 x Basics of CAM … 33/81 Hunt Effect At low level of illumination, the colorfulness is low In brighter viewing environment, the elements will be more colorful Absolute luminance level is needed in an appearance model Basics of CAM … 34/81 17 Stevens Effect Contrast increases with luminance: perceived contrast increases in with increasing luminance Lower luminance Luminance level increases: light colors become lighter, dark colors become darker Higher luminance 0.1 Relative0.1 Brightness 1.0 0.1 Relative luminance 1.0 Basics of CAM … 35/81 Stevens Effect Black-and-white image: white is more white and black is more black in higher level luminance conditions Basics of CAM … 36/81 18 Bartleson-Breneman Complex stimuli while varying luminance level and surround Percieved contrast of images increased when the surround was changed from dark to dim to light 1.0 Dark Dark background makes dark colors darker without affecting to light colors Dark background lowers and light background adds contrast Average 0.0 Lightness 0.0 0.0 Relative luminance 1.0 Basics of CAM … 37/81 Bartleson-Breneman Basics of CAM … 38/81 19 Spatial Interactions Spatial variables influence appearance Surround effects Basics of CAM … 39/81 Spatial Interactions Basics of CAM … 40/81 20 Dale Purves, Brown Brown patch under changing illumination See demonstration: http://www.purveslab.net/main/ Basics of CAM … 41/81 Viewing Conditions Color appearance depends on the simulus itself and other stimuli nearby Spatial Temporal Laboratory vs. practical applications and measurements Stimulus Proximal field Background Surround Basics of CAM … 42/81 21 Viewing Conditions Stimulus The area for which the color appearance is desired 2 degree angle (CIE 1931) Color in the angle vs. color of the object Proximal Field Immediate field around stimuli in all directions Local contrast effects: crispening, spreading Nearest pixels in digital images In many cases same as background Background About 10 degree field For simultaneous contrast definition Surround Area outside background Basics of CAM … 43/81 Chromatic Adaptation Light adaptation Decrease in visual sensitivity upon increase in overall illumination level Switch the lights on when you wake up Dark adaptation Increase in visual sensitivity upon decrease in illumination level From sunlight to dark room Chromatic adaptation Adaptation to changes in chromatic
Recommended publications
  • When Red Lights Look Yellow

    When Red Lights Look Yellow

    Publications 11-2005 When Red Lights Look Yellow Joanne M. Wood Queensland University of Technology David A. Atchison Queensland University of Technology Alex Chaparro Wichita State University, [email protected] Follow this and additional works at: https://commons.erau.edu/publication Part of the Cognition and Perception Commons, Musculoskeletal, Neural, and Ocular Physiology Commons, and the Ophthalmology Commons Scholarly Commons Citation Wood, J. M., Atchison, D. A., & Chaparro, A. (2005). When Red Lights Look Yellow. Investigative Ophthalmology & Visual Science, 46(11). https://doi.org/10.1167/iovs.04-1513 This Article is brought to you for free and open access by Scholarly Commons. It has been accepted for inclusion in Publications by an authorized administrator of Scholarly Commons. For more information, please contact [email protected]. When Red Lights Look Yellow Joanne M. Wood,1 David A. Atchison,1 and Alex Chaparro2 PURPOSE. Red signals are typically used to signify danger. This observer’s distance spectacle prescription. It did not occur study was conducted to investigate a situation identified by with lens powers in excess of ϩ1.00 D, as the signals became train drivers in which red signals appear yellow when viewed too blurred for the viewer to distinguish the color. A compre- at long distances (ϳ900 m) through progressive-addition hensive eye and vision examination of the train driver who had lenses. originally reported the color misperception revealed that his METHODS. A laboratory study was conducted to investigate the corrected vision was normal. The train driver was also shown effects of defocus, target size, ambient illumination, and sur- to have normal color vision, as assessed by the Ishihara, Farns- round characteristics on the extent of the color misperception worth Lantern, and Farnsworth D15 tests.
  • Urban Land Grab Or Fair Urbanization?

    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.
  • Several Color Appearance Phenomena in Color Reproduction

    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.
  • Report Template V3.0

    Report Template V3.0

    Ministry of Agriculture and Rural Development Forest Carbon Partnership Facility (FCPF) Carbon Fund Emission Reductions Program Document (ER-PD) Draft Version 1.2 Annex 1 ER Program Name and Country: Viet Nam Date of Submission or Revision: June 2016 Version 1.1 FCPF Room 403, 4th floor, 14 Thuy Khue Street Tha Ho District Hanoi Viet Nam Tel +84 4 3728 6495 Fax +84 4 3728 6496 www.Viet Nam-redd.org Contents Amendment Record This report has been issued and amended as follows: Issue Revision Description Date Approved by Table 1.1 Summary of the financial plan .................................................... 6 Table 1.2 Results framework .......................................................................... 7 Table 2.1 Summary of the monitoring plan .............................................12 Table 3.1 List of protected area in ER-P region with biodiversity significance ..................................................................................................14 Table 3.2 Protected areas in the NCC with the highest numbers of critical and endangered species .........................................................15 Table 3.3 Critically endangered mammal species ................................15 Table 3.4 Examples of protected biodiversity recently confirmed by SUF Management Boards (review of selected records 2012- 16 on-going work) .....................................................................................16 Table 4.1 Districts and provinces in the ER-P ........................................18 Table 4.2
  • On Trend: 2020 Color of the Year Classic Blue Inspirations, a Nod to Pantone’S Color of the Year

    On Trend: 2020 Color of the Year Classic Blue Inspirations, a Nod to Pantone’S Color of the Year

    Rowley On Trend On Trend: 2020 Color of the Year Classic Blue inspirations, a nod to Pantone’s color of the year. Color of the Year PANTONE of the YEAR 2020 Classic Blue Instilling calm, confidence, and PANTONE 19-4052 connection, this enduring blue hue Classic Blue highlights our desire for a dependable and stable foundation on which to build as we cross the threshold into a new era. A timeless and enduring blue hue, PANTONE 19-4052 Classic Blue is elegant in its simplicity. Suggestive of the sky at dusk, the reassuring qualities of the thought-provoking PANTONE 19-4052 Classic Blue highlight our desire for a dependable and stable foundation on which to build as we cross the threshold into a new era. Imprinted in our psyches as a restful color, PANTONE 19-4052 Classic Blue brings a sense of peace and tranquility to the human spirit, offering refuge. Aiding concentration and bringing laser like clarity, PANTONE 19- 4052 Classic Blue re-centers our thoughts. A reflective blue tone, Classic Blue fosters resilience. Information found at Pantone.com. | ©2020 Rowley Company LLC | All rights reserved. 1 Color Palette: We’ve paired Classic Blue (2020 Color of the Year) with subtle hues of green and blue to create our Modern Swag Roomscape. This corner treatment features finials from our 1 ⅛" Atelier Collection in Satin Gold finish, from our AriA® Metal Hardware collection, dressed in a watercolor floral drapery pattern with swag accents and one-of-a-kind pillows. Explore other Palettes for the 2020 Color of the Year. Color Palettes found on Pantone.com.
  • Colornet--Estimating Colorfulness in Natural Images

    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.
  • The Helmholtz-Kohlrausch Effect

    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.
  • RAL-Product-2019-SC-1.Pdf

    RAL-Product-2019-SC-1.Pdf

    INHALT / PRODUKTÜBERSICHT / RAL FARBEN PRODUCT OVERVIEW RAL COLOURS – Innovation and reliability. Worldwide. RAL FARBEN / PRODUKTÜBERSICHT / INHALT RAL CLASSIC THE WORLD‘S LEADING INDUSTRIAL COLOUR COLLECTION The RAL CLASSIC colour collection has for 90 years been indispensable in the clear communication of colours and a guarantee for obtaining exactly the same colours – worldwide. APPLICATION EXAMPLES Steel sculpture by world famous sculptor Anish Kapoor and star architect Cecil Balmond is London’s Olympic landmark. The ArcelorMittal Orbit glows in RAL 3003 Ruby Red. Allmilmö – a leading premium brand manufacturer of high-quality kitchen furnishings – produces these kitchen models in RAL 1023 traffic yellow. A design classic that is available in various colours. The picture shows a model in RAL 1004 Golden yellow. Emergency exit signs have the colour RAL 6002 Leaf green. Thonet produces the S 43 cantilever chair by Mart Stam in 11 RAL colours. RAL CLASSIC / PRODUCT OVERVIEW / RAL COLOURS RAL 840-HR Primary standards with 213 RAL CLASSIC colours Semi matt A5-sized 14.8 x 21.0 cm Colour illustration A6-sized 10.5 x 14.8 cm Binding colour samples for colour matching and quality control RAL 840-HR | 841-GL Including XYZ-values, colour distance from the original standard and reflectance curve Single cards available RAL 841-GL Primary standards with 196 RAL CLASSIC colours High gloss Allmilmö – a leading premium brand manufacturer of high-quality kitchen furnishings – A5-sized 14.8 x 21.0 cm produces these kitchen models in RAL 1023 traffic yellow. Colour illustration A6-sized 10.5 x 14.8 cm Binding colour samples for colour matching and quality control Including XYZ-values, colour distance from the original standard and reflectance curve Single cards available 07 Thonet produces the S 43 cantilever chair by Mart Stam in 11 RAL colours.
  • Color Appearance Models Today's Topic

    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.
  • Control of Chromatic Adaptation: Signals from Separate Cone Classes Interact

    Control of Chromatic Adaptation: Signals from Separate Cone Classes Interact

    Vision Research 40 (2000) 2885–2903 www.elsevier.com/locate/visres Control of chromatic adaptation: signals from separate cone classes interact Peter B. Delahunt *, David H. Brainard Department of Psychology, Uni6ersity of California, Santa Barbara, Santa Barbara, CA 93106, USA Received 7 December 1999; received in revised form 11 April 2000 Abstract Match stimuli presented on one side of a contextual image were adjusted to have the same appearance as test stimuli presented on the other side. Both full color and isochromatic contextual images were used. Contextual image pairs were constructed that had identical S-cone image planes, while their L- and M-cone image planes differed. The data show that the S-cone component of the matches depends on the L- and M-cone planes of the contextual image. This dependence means that matches obtained using isochromatic stimuli (lightness matches) may not be used directly to predict full color matches. © 2000 Elsevier Science Ltd. All rights reserved. Keywords: Colour; Constancy; Adaptation; Appearance; Lightness 1. Introduction studies, judgments are made of a single attribute of color appearance, lightness (or sometimes brightness). Color appearance depends on context. A classic ex- Restricting the stimuli in this way simplifies ample is simultaneous color contrast, where the imme- stimulus specification and control and therefore allows diate surround of an image region affects its color easier exploration of spatially rich contexts. We refer to appearance (see for example Evans, 1948). The immedi- studies where the stimuli are restricted to be isochro- ate surround, however, is not the only relevant contex- matic as lightness experiments (e.g.
  • Colornet - Estimating Colorfulness in Natural Images

    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.
  • Graphic Standard Guidelinesview

    Graphic Standard Guidelinesview

    Maricopa County Graphic Standard Guidelines basic standards The updated Maricopa County seal is the basic building block of our new visual image. It is a symbol of many things our County represents. The goal is to establish an image that is credible, “ownable” and that with proper use will promote the County as a well-integrated organization. This graphic standards manual was prepared to ensure that we speak to all with a common “voice,” projecting a distinctive and relevant image of Maricopa County, while allowing the necessary flexibility for individual departmental messages. These guidelines provide an objective set of boundaries to ensure consistent quality in the application of the seal and safeguard against potential problems that could dilute efforts to build the Maricopa County identity. addendum: typefaces Garamond (replaces Minion Regular) abcdefghijklmnopqrstuvwxyz 0123456789 ABCDEFGHIJKLMNOPQRSTUVWXYZ Garamond Italic (replaces Minion Italic) abcdefghijklmnopqrstuvwxyz 0123456789 ABCDEFGHIJKLMNOPQRSTUVWXYZ Garamond Bold (replaces Minion Semibold and Bold) abcdefghijklmnopqrstuvwxyz 0123456789 ABCDEFGHIJKLMNOPQRSTUVWXYZ Note: Do not artificially italicize Garamond Bold. Microsoft Garamond has only three faces included in its set (roman, italic and bold). This face is licensed from the AGFA/Monotype corporation. An additional two weights (Monotype Alternate Italic and Bold Italic) are available from the AGFA/Monotype web site (www.fonts.com). Please check with your department before purchasing. Tahoma (replaces Avenir Book) abcdefghijklmnopqrstuvwxyz 0123456789 ABCDEFGHIJKLMNOPQRSTUVWXYZ Tahoma Bold (replaces Avenir Medium and Heavy) abcdefghijklmnopqrstuvwxyz 0123456789 ABCDEFGHIJKLMNOPQRSTUVWXYZ Note: Tahoma does not have italic faces in its family. Do not artificially italicize this face. a.1 typeface update:It has come to the attention of the Public Information Office that the typefaces specified for use on Maricopa County materials are not widely available throughout the County computer network, and are cost prohibitive to purchase.