Lighting Spectra for the Maximum Colorfulness
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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. -
Calculation of CCT and Duv and Practical Conversion Formulae
CORM 2011 Conference, Gaithersburg, MD, May 3-5, 2011 Calculation of CCT and Duv and Practical Conversion Formulae Yoshi Ohno Group Leader, NIST Fellow Optical Technology Division National Institute of Standards and Technology Gaithersburg, Maryland USA CORM 2011 1 White Light Chromaticity Duv CCT CORM 2011 2 Duv often missing Lighting Facts Label CCT and CRI do not tell the whole story of color quality CORM 2011 3 CCT and CRI do not tell the whole story Triphosphor FL simulation Not acceptable Not preferred Neodymium (Duv=-0.005) Duv is another important dimension of chromaticity. CORM 2011 4 Duv defined in ANSI standard Closest distance from the Planckian locus on the (u', 2/3 v') diagram, with + sign for above and - sign for below the Planckian locus. (ANSI C78.377-2008) Symbol: Duv CCT and Duv can specify + Duv the chromaticity of light sources just like (x, y). - Duv The two numbers (CCT, Duv) provides color information intuitively. (x, y) does not. Duv needs to be defined by CIE. CORM 2011 5 ANSI C78.377-2008 Specifications for the chromaticity of SSL products CORM 2011 6 CCT- Duv chart 5000 K 3000 K 4000 K 6500 K 2700 K 3500 K 4500 K 5700 K ANSI 7-step MacAdam C78.377 ellipses quadrangles (CCT in log scale) CORM 2011 7 Correlated Color Temperature (CCT) Temperature [K] of a Planckian radiator whose chromaticity is closest to that of a given stimulus on the CIE (u’,2/3 v’) coordinate. (CIE 15:2004) CCT is based on the CIE 1960 (u, v) diagram, which is now obsolete. -
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. -
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. -
Cooledge Light Quality Metrics Fabricated
COOLEDGE LIGHT QUALITY METRICS: FABRICATED LUMINAIRES - 3500K NOTES ABOUT LIGHT QUALITY METRICS DATA: ― Values shown are TYPICAL – actual performance of individual units may vary ― The data presented has been generated in accordance with LM-79-08 ― A complete summary of LM-79-08 data is provided for nominal 4x8 (1200x2400) FABRICated Luminaire models only; however, spectral and color rendering data is applicable to all luminaire sizes, models, and flux levels including: ― Spectral Power Distribution (SPD) ― Nominal CCT ― Chromaticity ― Rf and Rg (TM-30-15) ― CRI (Ra) and R-values ― Duv SELECTED DEFINITIONS ― Candlepower: As presented in this document it is the same as “candela” the SI unit of measurement for light intensity. ― CRI (Ra): The general Color Rendering Index based on 8 CIE reference pastel color samples. ― Duv: The American National Standards Institute (ANSI) references Duv, a metric based on the CIE 1976 color space that quantifies the distance between the chromaticity of a given light source and a blackbody radiator of equal CCT. A negative Duv indicates that the source is “below” the Planckian locus (blackbody curve), potentially having a red/ blue tint, whereas a positive Duv indicates that the source is “above” the curve, potentially exhibiting a green tint. ― Nominal CCT Quadrangles: ANSI has defined acceptable chromaticity quadrangles for LED binning in relation to the blackbody curve within CIE color space. The data presented shows the typical chromaticity coordinate within the relevant quadrangle. ― R-value (Ri): The R-value is a mathematical calculation measuring how similar a light source renders a particular color compared to a reference blackbody source of the same CCT. -
A Correlated Color Temperature for Illuminants
. (R P 365) A CORRELATED COLOR TEMPERATURE FOR ILLUMINANTS By Raymond Davis ABSTRACT As has long been known, most of the artificial and natural illuminants do not match exactly any one of the Planckian colors. Therefore, strictly speaking, they can not be assigned a color temperature. A color of this type may, however, be correlated with a representative Planckian color. The method of determining correlated color temperature described in this paper consists in comparing the relative luminosities of each of the three primary red, green, and blue components of the source with similar values for the Planckian series. With such a comparison three component temperatures are obtained; that is, the red component of the source corresponds with that of the Planckian radiator at one temperature, its green component with that of the Planckian radiator at a second temperature, and its blue component with that of the Planckian radiator at a third temperature. The average of these three component temperatures is designated as the correlated color temperature of the source. The mean devia- tion of the component temperatures from the average temperature is used as a basis for specifying the color (chromaticity) departure of the source from that of the Planckian radiator at the correlated color temperature. The conjunctive wave length indicates the kind of color departure. CONTENTS Page I. Introduction 659 II. The proposed method 662 III. Procedure 665 1. The Planckian radiator evaluated in terms of relative lumi- nosity of the primary components 665 2. Computation of the correlated color temperature 670 3. Calculation of color departure in terms of sensation steps 672 4. -
Calculating Color Temperature and Illuminance Using the TAOS TCS3414CS Digital Color Sensor Contributed by Joe Smith February 27, 2009 Rev C
TAOS Inc. is now ams AG The technical content of this TAOS document is still valid. Contact information: Headquarters: ams AG Tobelbader Strasse 30 8141 Premstaetten, Austria Tel: +43 (0) 3136 500 0 e-Mail: [email protected] Please visit our website at www.ams.com NUMBER 25 INTELLIGENT OPTO SENSOR DESIGNER’S NOTEBOOK Calculating Color Temperature and Illuminance using the TAOS TCS3414CS Digital Color Sensor contributed by Joe Smith February 27, 2009 Rev C ABSTRACT The Color Temperature and Illuminance of a broad band light source can be determined with the TAOS TCS3414CS red, green and blue digital color sensor with IR blocking filter built in to the package. This paper will examine Color Temperature and discuss how to calculate the Color Temperature and Illuminance of a given light source. Color Temperature information could be useful in feedback control and quality control systems. COLOR TEMPERATURE Color temperature has long been used as a metric to characterize broad band light sources. It is a means to characterize the spectral properties of a near-white light source. Color temperature, measured in degrees Kelvin (K), refers to the temperature to which one would have to heat a blackbody (or planckian) radiator to produce light of a particular color. A blackbody radiator is defined as a theoretical object that is a perfect radiator of visible light. As the blackbody radiator is heated it radiates energy first in the infrared spectrum and then in the visible spectrum as red, orange, white and finally bluish white light. Incandescent lights are good models of blackbody radiators, because most of the light emitted from them is due to the heating of their filaments. -
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
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. -
Chroma and Hue Variation in Color Images of Natural Scenes
Naturalness and Image Quality: Chroma and Hue Variation in Color Images of Natural Scenes Huib de Ridder and Frans J.J. Blommaert Institute for Perception Research, Eindhoven, The Netherlands; Elena A. Fedorovskaya, Department of Psychophysiology, Moscow State University, Mokhovaya street 8, 103009 Moscow, Russia Abstract these parameters (e.g. blur, periodic structure, noise) it proved possible to derive explicit expressions for their The relation between perceptual image quality and natural- relation with the corresponding image attributes.3,4 ness was investigated by varying the colorfulness and hue Scaling experiments using multiply impaired images of color images of natural scenes. These variations were have shown that image attributes can be represented by a set created by digitizing the images, subsequently determining of orthogonal vectors in a Euclidean space.3,5-8 Accord- their color point distributions in the CIELUV color space ingly, the attributes can be said to be the orthogonal dimen- and finally multiplying either the chroma value or the hue- sions of a multidimensional psychological space underlying angle of each pixel by a constant. During the chroma/hue- image quality. The sensorial image is represented in this angle transformation the lightness and hue-angle/chroma space by a point with the perceived strengths of the at- value of each pixel were kept constant. Ten subjects rated tributes as coordinates. Image quality has sometimes been quality and naturalness on numerical scales. The results identified as a direction in this space, with the angle to a show that both quality and naturalness deteriorate as soon as dimension indicating how relevant that attribute is for the hues start to deviate from the ones in the original image. -
Estimation of Illuminants from Projections on the Planckian Locus Baptiste Mazin, Julie Delon, Yann Gousseau
Estimation of Illuminants From Projections on the Planckian Locus Baptiste Mazin, Julie Delon, Yann Gousseau To cite this version: Baptiste Mazin, Julie Delon, Yann Gousseau. Estimation of Illuminants From Projections on the Planckian Locus. IEEE Transactions on Image Processing, Institute of Electrical and Electronics Engineers, 2015, 24 (6), pp. 1944 - 1955. 10.1109/TIP.2015.2405414. hal-00915853v3 HAL Id: hal-00915853 https://hal.archives-ouvertes.fr/hal-00915853v3 Submitted on 9 Oct 2014 HAL is a multi-disciplinary open access L’archive ouverte pluridisciplinaire HAL, est archive for the deposit and dissemination of sci- destinée au dépôt et à la diffusion de documents entific research documents, whether they are pub- scientifiques de niveau recherche, publiés ou non, lished or not. The documents may come from émanant des établissements d’enseignement et de teaching and research institutions in France or recherche français ou étrangers, des laboratoires abroad, or from public or private research centers. publics ou privés. 1 Estimation of Illuminants From Projections on the Planckian Locus Baptiste Mazin, Julie Delon and Yann Gousseau Abstract—This paper introduces a new approach for the have been proposed, restraining the hypothesis to well chosen automatic estimation of illuminants in a digital color image. surfaces of the scene, that are assumed to be grey [41]. Let The method relies on two assumptions. First, the image is us mention the work [54], which makes use of an invariant supposed to contain at least a small set of achromatic pixels. The second assumption is physical and concerns the set of color coordinate [20] that depends only on surface reflectance possible illuminants, assumed to be well approximated by black and not on the scene illuminant. -
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.