Comparing Color Appearance Models Using Pictorial Images Taek Gyu Kim
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The Art of Digital Black & White by Jeff Schewe There's Just Something
The Art of Digital Black & White By Jeff Schewe There’s just something magical about watching an image develop on a piece of photo paper in the developer tray…to see the paper go from being just a blank white piece of paper to becoming a photograph is what many photographers think of when they think of Black & White photography. That process of watching the image develop is what got me hooked on photography over 30 years ago and Black & White is where my heart really lives even though I’ve done more color work professionally. I used to have the brown stains on my fingers like any good darkroom tech, but commercially, I turned toward color photography. Later, when going digital, I basically gave up being able to ever achieve what used to be commonplace from the darkroom–until just recently. At about the same time Kodak announced it was going to stop making Black & White photo paper, Epson announced their new line of digital ink jet printers and a new ink, Ultrachrome K3 (3 Blacks- hence the K3), that has given me hope of returning to darkroom quality prints but with a digital printer instead of working in a smelly darkroom environment. Combine the new printers with the power of digital image processing in Adobe Photoshop and the capabilities of recent digital cameras and I think you’ll see a strong trend towards photographers going digital to get the best Black & White prints possible. Making the optimal Black & White print digitally is not simply a click of the shutter and push button printing. -
Fast and Stable Color Balancing for Images and Augmented Reality
Fast and Stable Color Balancing for Images and Augmented Reality Thomas Oskam 1,2 Alexander Hornung 1 Robert W. Sumner 1 Markus Gross 1,2 1 Disney Research Zurich 2 ETH Zurich Abstract This paper addresses the problem of globally balanc- ing colors between images. The input to our algorithm is a sparse set of desired color correspondences between a source and a target image. The global color space trans- formation problem is then solved by computing a smooth Source Image Target Image Color Balanced vector field in CIE Lab color space that maps the gamut of the source to that of the target. We employ normalized ra- dial basis functions for which we compute optimized shape parameters based on the input images, allowing for more faithful and flexible color matching compared to existing RBF-, regression- or histogram-based techniques. Further- more, we show how the basic per-image matching can be Rendered Objects efficiently and robustly extended to the temporal domain us- Tracked Colors balancing Augmented Image ing RANSAC-based correspondence classification. Besides Figure 1. Two applications of our color balancing algorithm. Top: interactive color balancing for images, these properties ren- an underexposed image is balanced using only three user selected der our method extremely useful for automatic, consistent correspondences to a target image. Bottom: our extension for embedding of synthetic graphics in video, as required by temporally stable color balancing enables seamless compositing applications such as augmented reality. in augmented reality applications by using known colors in the scene as constraints. 1. Introduction even for different scenes. With today’s tools this process re- quires considerable, cost-intensive manual efforts. -
Color Printing Techniques
4-H Photography Skill Guide Color Printing Techniques Enlarging Color Negatives Making your own color prints from Color Relations color negatives provides a whole new area of Before going ahead into this fascinating photography for you to enjoy. You can make subject of color printing, let’s make sure we prints nearly any size you want, from small ones understand some basic photographic color and to big enlargements. You can crop pictures for the visual relationships. composition that’s most pleasing to you. You can 1. White light (sunlight or the light from an control the lightness or darkness of the print, as enlarger lamp) is made up of three primary well as the color balance, and you can experiment colors: red, green, and blue. These colors are with control techniques to achieve just the effect known as additive primary colors. When you’re looking for. The possibilities for creating added together in approximately equal beautiful color prints are as great as your own amounts, they produce white light. imagination. You can print color negatives on conventional 2. Color‑negative film has a separate light‑ color printing paper. It’s the kind of paper your sensitive layer to correspond with each photofinisher uses. It requires precise processing of these three additive primary colors. in two or three chemical solutions and several Images recorded on these layers appear as washes in water. It can be processed in trays or a complementary (opposite) colors. drum processor. • A red subject records on the red‑sensitive layer as cyan (blue‑green). • A green subject records on the green‑ sensitive layer as magenta (blue‑red). -
Simplest Color Balance
Published in Image Processing On Line on 2011{10{24. Submitted on 2011{00{00, accepted on 2011{00{00. ISSN 2105{1232 c 2011 IPOL & the authors CC{BY{NC{SA This article is available online with supplementary materials, software, datasets and online demo at http://dx.doi.org/10.5201/ipol.2011.llmps-scb 2014/07/01 v0.5 IPOL article class Simplest Color Balance Nicolas Limare1, Jose-Luis Lisani2, Jean-Michel Morel1, Ana Bel´enPetro2, Catalina Sbert2 1 CMLA, ENS Cachan, France ([email protected], [email protected]) 2 TAMI, Universitat Illes Balears, Spain (fjoseluis.lisani, anabelen.petro, [email protected]) Abstract In this paper we present the simplest possible color balance algorithm. The assumption under- lying this algorithm is that the highest values of R, G, B observed in the image must correspond to white, and the lowest values to obscurity. The algorithm simply stretches, as much as it can, the values of the three channels Red, Green, Blue (R, G, B), so that they occupy the maximal possible range [0, 255] by applying an affine transform ax+b to each channel. Since many images contain a few aberrant pixels that already occupy the 0 and 255 values, the proposed method saturates a small percentage of the pixels with the highest values to 255 and a small percentage of the pixels with the lowest values to 0, before applying the affine transform. Source Code The source code (ANSI C), its documentation, and the online demo are accessible at the IPOL web page of this article1. -
ARC Laboratory Handbook. Vol. 5 Colour: Specification and Measurement
Andrea Urland CONSERVATION OF ARCHITECTURAL HERITAGE, OFARCHITECTURALHERITAGE, CONSERVATION Colour Specification andmeasurement HISTORIC STRUCTURESANDMATERIALS UNESCO ICCROM WHC VOLUME ARC 5 /99 LABORATCOROY HLANODBOUOKR The ICCROM ARC Laboratory Handbook is intended to assist professionals working in the field of conserva- tion of architectural heritage and historic structures. It has been prepared mainly for architects and engineers, but may also be relevant for conservator-restorers or archaeologists. It aims to: - offer an overview of each problem area combined with laboratory practicals and case studies; - describe some of the most widely used practices and illustrate the various approaches to the analysis of materials and their deterioration; - facilitate interdisciplinary teamwork among scientists and other professionals involved in the conservation process. The Handbook has evolved from lecture and laboratory handouts that have been developed for the ICCROM training programmes. It has been devised within the framework of the current courses, principally the International Refresher Course on Conservation of Architectural Heritage and Historic Structures (ARC). The general layout of each volume is as follows: introductory information, explanations of scientific termi- nology, the most common problems met, types of analysis, laboratory tests, case studies and bibliography. The concept behind the Handbook is modular and it has been purposely structured as a series of independent volumes to allow: - authors to periodically update the -
Color Balance
TECHNOLOGY 3.2 – ADOBE® PHOTOSHOP® MINUTE7 STARTER Color Balance OBJECTIVES STEP 1 | LEARN By reviewing the Color Balance tutorial, students will learn how to modify the color balance of a photograph using Adobe® Photoshop®. STEP 2 | PRACTICE Students will use the Color Balance tutorial as a reference as they change the color balance of a photograph. Note: A practice photo is provided in the tutorial folder. STEP 3 | USE Students will apply knowledge when editing and reviewing photographs being published. 21ST CENTURY SKILLS Employment in the 21st century requires the ability to learn and use technology appropriately and effectively. Adobe Photoshop is an industry-standard software that allows for creative thinking. COMMON CORE ISTE STANDARDS ISTE STATE STANDARDS 1B: Create original works. ELA-Literacy.SL.9-12.5, CCRA.SL.5 6A: Understand and use technology systems. Make strategic use of digital media to 6B: Select and use applications effectively enhance understanding. and productively. 6C: Troubleshoot systems and applications. Do you have an idea for a 7-Minute Starter? Email us at [email protected] 14-0610 Color Balance Sometimes an image may have a slight color cast, either due to White Balance issues with the camera or simply because of artificial lighting indoors where the photo was taken. Color can be quickly neutralized by using Adobe® Photoshop’s® Auto Color feature located under the Image Tab. Auto Color adjusts the contrast and color of an image by searching the image to identify shadows, mid-tones and highlights. If the Auto Color tool doesn’t do the trick — the following method is an easy way to help remove the color cast from a photo using the Neutral Color Picker and may produce better results. -
Color Management Guide Printing with Epson Premium ICC Profiles Copyright Notice All Rights Reserved
Epson Professional Imaging Color Management Guide Printing With Epson Premium ICC Profiles Copyright Notice 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 written permission of Seiko Epson Corporation. The information contained herein is designed only for use with this Epson product. Epson is not responsible for any use of this information as applied to other equipment. Neither Seiko Epson Corporation nor its affiliates shall be liable to the purchaser of this product or third parties for damages, losses, costs, or expenses incurred by purchaser or third parties as a result of: accident, misuse, or abuse of this product or unauthorized modifications, repairs, or alterations to this product, or (excluding the U.S.) failure to strictly comply with Seiko Epson Corporation’s operating and maintenance instructions. Seiko Epson Corporation shall not be liable for any damages or problems arising from the use of any options or any consumable products other than those designated as Original Epson Products or Epson Approved Products by Seiko Epson Corporation. Responsible Use of Copyrighted Materials Epson encourages each user to be responsible and respectful of the copyright laws when using any Epson product. While some countries’ laws permit limited copying or reuse of copyrighted material in certain circumstances, those circumstances may not be as broad as some people assume. Contact your legal advisor for any questions regarding copyright law. Trademarks Epson and Epson Stylus are registered trademarks, and Epson Exceed Your Vision is a registered logomark of Seiko Epson Corporation. -
Color Management Scanner Digital Camera Mobile Phone
Graphic Arts Workflow Lecture 12 Color Management Scanner Digital Camera Mobile Phone Device independent Color Representation ICC Profiles Gamut Mapping Image retouching Page Layout DFE Proofer Digital Printers Press Color Management Images look different on each device. Original Image Scanner Image Printer Image Images look different on each device. Color Management Device Differences Variations are due to: • Spectral distribution of the device components Difference in Spatial Resolutions (phosphors, filters, sensors)’ • Viewing conditions (dark/light, indoor/outdoors, illumination spectra) Printers 300-1200 dpi 1-4 intensity bits • media CRT Pitch 0.27 µ, 72 ppi, (projected/reflected light or print). TV 480 lines (analog) LCD 100 ppi, 8 intensity bits Camera 2 Megapixel, 10 intensity bits Scanner 600 dpi, 12 intensity bits Solution: Define a transform to map colors from color space of one device (source) to color space of another device (destination). Device Differences Device Differences Difference in Contrast and Brightness Range Difference in White Point QImagingKodak Nikon Genoacolor Device Differences Device Differences Difference in Gamut Difference in Gamut Monitor Gamut Printer Gamut Film Monitor Device Differences Gamut Mismatch Difference in Gamut How does one print this color? 0.8 display printer Scanner 0.6 y 0.4 0.2 Monitor 0 0 0.2 0.4 0.6 0.8 x This color never needed? printer Gamut Mismatch Gamut Mismatch How does one print this color? 0.8 display printer 0.6 y 0.4 0.2 0 0 0.2 0.4 0.6 0.8 x How does one display this color? The problem is twofold: 1) Differences in device representation 2) Differences in Gamut Size and Shape Gamut Mapping Gamut Mapping - Example In order to transfer color information between a source device and a destination device, one must define a mapping between the source Gamut to destination the destination Gamut. -
Comparing Appearance Models Using Pictorial Images
Comparing Appearance Models Using Pictorial Images Taek Gyu Kim, Roy S. Berns, and Mark D. Fairchild Munsell Color Science Laboratory, Center for Imaging Science Rochester Institute of Technology, Rochester, New York Eight different color appearance models were tested using Appearance-Model Overview pictorial images. A psychophysical paired comparison ex- von Kries periment was performed where 30 color-normal observers ′ = ⋅ judged reference and test images via successive-Ganzfeld L kL L haploscopic viewing such that each eye maintained con- M′ = k ⋅ M stant chromatic adaptation and inter-ocular interactions M (1) ′ = ⋅ were minimized. It was found that models based on von S kS S Kries had best performance, specifically CIELAB, HUNT, RLAB, and von Kries. where L, M , and S represent the excitations of the long-, middle-, and short-wavelength sensitive cones, L′, M′, and ′ Introduction S represent the post-adaptation cone signals, and kL , kM , and kS are the multiplicative factors, generally taken to be Color appearance models are necessary to incorporate the inverse of the respective maximum cone excitations for into the color WYSIWYG chain when images are viewed the illuminating condition.3,4 The calculation of the cone under dissimilar conditions such as illumination spectral fundamentals is a linear transformation of CIE tristimulus power distribution and luminance, surround relative lu- values. In this case the Stiles-Estevez-Hunt-Pointer funda- minance, and media type where cognition is affected. mentals were used.2,9,17 (These are also used in the Hunt, These differing conditions often occur when compar- Nayatani, and RLAB models.) ing CRT and printed images, CRT and projected slides, or rear-illuminated transparencies and CRT or printed CIELAB images. -
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. -
PRECISE COLOR COMMUNICATION COLOR CONTROL from PERCEPTION to INSTRUMENTATION Knowing Color
PRECISE COLOR COMMUNICATION COLOR CONTROL FROM PERCEPTION TO INSTRUMENTATION Knowing color. Knowing by color. In any environment, color attracts attention. An infinite number of colors surround us in our everyday lives. We all take color pretty much for granted, but it has a wide range of roles in our daily lives: not only does it influence our tastes in food and other purchases, the color of a person’s face can also tell us about that person’s health. Even though colors affect us so much and their importance continues to grow, our knowledge of color and its control is often insufficient, leading to a variety of problems in deciding product color or in business transactions involving color. Since judgement is often performed according to a person’s impression or experience, it is impossible for everyone to visually control color accurately using common, uniform standards. Is there a way in which we can express a given color* accurately, describe that color to another person, and have that person correctly reproduce the color we perceive? How can color communication between all fields of industry and study be performed smoothly? Clearly, we need more information and knowledge about color. *In this booklet, color will be used as referring to the color of an object. Contents PART I Why does an apple look red? ········································································································4 Human beings can perceive specific wavelengths as colors. ························································6 What color is this apple ? ··············································································································8 Two red balls. How would you describe the differences between their colors to someone? ·······0 Hue. Lightness. Saturation. The world of color is a mixture of these three attributes.