Controlling Color in Displays: A discussion on Quality Jean-Baptiste Thomas Centre de Recherche et de Restauration des musées de France CNRS UMR 171 Jean-Baptiste.

Slides:

Advertisements

Similar presentations

J Schanda University Veszprém, Department of Image Processing and Neurocomputing, Hungary Characterizing illumination systems Colour rendering and beyond.

Advertisements

Multispectral Format from Perspective of Remote Sensing

Evaluating Color Descriptors for Object and Scene Recognition Koen E.A. van de Sande, Student Member, IEEE, Theo Gevers, Member, IEEE, and Cees G.M. Snoek,

Color Appearance Models The Nayatani et al. Model The Hunt 91 and 94 Model The RLAB Model Iris Zhao April 21, 2004.

Full Gamut Color Matching for Tiled Display Walls Grant Wallace, Han Chen, Kai Li Princeton University.

Wen-Hung Liao Department of Computer Science National Chengchi University November 27, 2008 Estimation of Skin Color Range Using Achromatic Features.

COLOR CATEGORIES IN VARIOUS COLOR SPACES Hirohisa Yaguchi Dept. of Information and Image Sciences Chiba University The 9th CIC (Nov. 8, 2001) Scottsdale,

Color & Light, Digitalization, Storage. Vision Rods work at low light levels and do not see color –That is, their response depends only on how many photons,

 Image Characteristics  Image Digitization Spatial domain Intensity domain 1.

Color spaces CIE - RGB space. HSV - space. CIE - XYZ space.

A Perceptual Heuristic for Shadow Computation in Photo-Realistic Images Wednesday, 2 August 2006 Peter VangorpOlivier DumontToon LenaertsPhilip Dutré.

Guillaume Lavoué Mohamed Chaker Larabi Libor Vasa Université de Lyon

The eyes have three different kinds of color receptors; One kind is most sensitive to short wavelengths, one to middle wavelengths, and one to long wavelengths.

Color Mixing There are two ways to control how much red, green, and blue light reaches the eye: “Additive Mixing” Starting with black, the right amount.

Apparent Greyscale: A Simple and Fast Conversion to Perceptually Accurate Images and Video Kaleigh SmithPierre-Edouard Landes Joelle Thollot Karol Myszkowski.

Perceptual Evaluation of Colour Gamut Mapping Algorithms Fabienne Dugay The Norwegian Color Research Laboratory Faculty of Computer Science and Media Technology.

1 Human Perception What you may or may not already know, want to know or even care about how you see.

I.1 ii.2 iii.3 iv.4 1+1=. i.1 ii.2 iii.3 iv.4 1+1=

Color Management Systems Problems –Solve gamut matching issues –Attempt uniform appearance Solutions –Image dependent manipulations (e.g. Stone) –Device.

Quantization If too few levels of gray, (e.g. decrease halftone spot size to increase spatial resolution), then boundaries between adjacent gray levels.

1 Computer Science 631 Lecture 6: Color Ramin Zabih Computer Science Department CORNELL UNIVERSITY.

Gamut Mapping First try: map black points and fill destination gamut.

I.1 ii.2 iii.3 iv.4 1+1=. i.1 ii.2 iii.3 iv.4 1+1=

EE465: Introduction to Digital Image Processing Copyright Xin Li 1 Introduction What is image segmentation?  Technically speaking, image segmentation.

Color Fidelity in Multimedia H. J. Trussell Dept. of Electrical and Computer Engineering North Carolina State University Raleigh, NC

Describing Visual Air Quality Is A Complex Issue Depending On: characteristics of observer optical characteristics of target illumination of scene optical.

Why Care About Color? Accurate color reproduction is commercially valuable - e.g. Kodak yellow, painting a house Color reproduction problems increased.

Quintiles Intelligent Imaging Clear Vision for the Healthcare Industry

Color & Color Management. Overview I. Color Perception Definition & characteristics of color II. Color Representation RGB, CMYK, XYZ, Lab III. Color Management.

Dye Sublimation Color Management

1 Internet Graphics. 2 Representing Images  Raster Image: Images consist of “dots” of color, not lines  Pixel: Picture element-tiny rectangle  Resolution:

Perception-motivated High Dynamic Range Video Encoding

Website evaluation models and acceptability factors K.Vipartienė, E. Valavičius.

2001 by Jim X. Chen: 1 The purpose of a color model is to allow convenient specification of colors within some color gamut.

Any questions about the current assignment? (I’ll do my best to help!)

Computer Aided Perception Validation of Tone Mapping Operators in the Simulation of Disability Glare A Masters Thesis Proposal by Charles Ehrlich UC Berkeley.

Graphic Design Cantu. DO NOW What do you know about Color Theory? 3- 5 clear complete - sentences.

Color John C. Hart CS 418 Intro to Computer Graphics.

Validation of Color Managed 3D Appearance Acquisition Michael Goesele Max-Planck-Institut für Informatik (MPI Informatik) Vortrag im Rahmen des V 3 D 2.

Color Principles for Computer Graphics Donald House 9/17/09 Artist’s slides by Lynette House.

Design Visualization and Character Development Artistic Rendering Using Illustration Software.

Sparse Matrix Factorizations for Hyperspectral Unmixing John Wright Visual Computing Group Microsoft Research Asia Sept. 30, 2010 TexPoint fonts used in.

Crowdsourcing Color Perceptions using Mobile Devices Jaejeung Kim 1, Sergey Leksikov 1, Punyotai Thamjamrassri 2, Uichin Lee 1, Hyeon-Jeong Suk 2 1 Dept.

Purdue University Page 1 Color Image Fidelity Assessor Color Image Fidelity Assessor * Wencheng Wu (Xerox Corporation) Zygmunt Pizlo (Purdue University)

Computer Graphics Lecture 06 Fasih ur Rehman. Last Class Overview of Graphic Systems – LED Display – Plasma TV – Hardcopy Devices – Input Devices Human.

Database Management and Innovative Applications for Imaging Museum Laboratories Ruven Pillay, C2RMF Sauveur Prague, 31 May – 2 June 2006.

03/05/03© 2003 University of Wisconsin Last Time Tone Reproduction If you don’t use perceptual info, some people call it contrast reduction.

Chapter 1. Introduction. Goals of Image Processing “One picture is worth more than a thousand words” 1.Improvement of pictorial information for human.

Developing Commercial Design Plan Micah Rooke Georgia CTAE Resource Network 2010.

Image-Based Rendering of Diffuse, Specular and Glossy Surfaces from a Single Image Samuel Boivin and André Gagalowicz MIRAGES Project.

TVCG 2013 Sungkil Lee, Mike Sips, and Hans-Peter Seidel.

Histograms and Color Balancing Computational Photography Derek Hoiem, University of Illinois 09/10/15 “Empire of Light”, Magritte.

Three-Receptor Model Designing a system that can individually display thousands of colors is very difficult Instead, colors can be reproduced by mixing.

Color Principles for Computer Graphics Donald House 9/17/09 Artist’s slides by Lynette House.

Histograms and Color Balancing Computational Photography Derek Hoiem, University of Illinois 09/13/11 “Empire of Light”, Magritte.

EE 638: Principles of Digital Color Imaging Systems Lecture 14: Monitor Characterization and Calibration – Basic Concepts.

Remote Sensing Image Enhancement. Image Enhancement ► Increases distinction between features in a scene ► Single image manipulation ► Multi-image manipulation.

03/04/05© 2005 University of Wisconsin Last Time Tone Reproduction –Histogram method –LCIS and improved filter-based methods.

Learning Photographic Global Tonal Adjustment with a Database of Input / Output Image Pairs.

ECE 638: Principles of Digital Color Imaging Systems Jan P. Allebach School of Electrical and Computer Engineering

Tone mapping Digital Visual Effects, Spring 2007 Yung-Yu Chuang 2007/3/13 with slides by Fredo Durand, and Alexei Efros.

Cheng Li Supervisor : Prof. M. Ronnier Luo Colour and Imaging Group Department of Colour Science Evaluation of Visual Colour Fidelity.

Click to edit Master title style Click to edit Master text styles Second level Third level Fourth level Fifth level 1 Integrated Color Solutions A presentation.

Color transfer between high-dynamic-range images

Intrinsic images and shape refinement

EE 638: Principles of Digital Color Imaging Systems

Ch. 3 The Human Color-Imaging System

Color Management.

COLOR CONSTANCY IN THE COMPRESSED DOMAIN

Gradient Domain Salience-preserving Color-to-gray Conversion

Presentation transcript:

Controlling Color in Displays: A discussion on Quality Jean-Baptiste Thomas Centre de Recherche et de Restauration des musées de France CNRS UMR 171 Jean-Baptiste THOMAS, 1

V D65 (λ) AcquisitionReproduction Technology, Model and Quality Direct view Transfer via a network, storage, compression, etc. I. Color management Jean-Baptiste THOMAS, 2 09/06/2010

I. Color management Jean-Baptiste THOMAS, D s fc D s fc V D65 (λ) COLOR PCS CIEXYZ CIELAB … RGB 3 Color management 09/06/2010

I. Color management Jean-Baptiste THOMAS, D s fc D s fc V D65 (λ) COLOR PCS CIEXYZ CIELAB … RGB 4 Color management 09/06/2010

Technology Gamut mappingModel II. Displays Jean-Baptiste THOMAS, 5 Quality 09/06/2010

II. Displays Jean-Baptiste THOMAS, 6 Colorimetric characterization RGBCIELAB Forward model CIEXYZ = F(RGB) CIELAB = F(RGB) Inverse model RGB = F -1 (CIELAB) 09/06/2010

II. Displays Jean-Baptiste THOMAS, 7 Quality estimation CIELAB 1 DISPLAY MODEL MEASURE RGB DISPLAY WHITE XYZ 1 CIELAB 2 METRIC XYZ 2 09/06/2010

III. Quality Jean-Baptiste THOMAS, 8 Quality estimation Metrics o CIELAB color space Quality criterion (colorimetry) o Just noticeable difference: Kang (97), Mahy et al (94) Quality criterion (Color imaging) o Rules of thumb: Hardeberg (99), Abrardo et al (96) o Perceptability acceptance for pictorial images: Stokes et al (92), Catrysse et al (99), Gibson and Fairchild (00) 09/06/2010

III. Quality Jean-Baptiste THOMAS, 9 Need Professional o THE COLOR RENDERING HAS TO BE PERFECTLY ACCURATE Consumer o AESTHETIC AND INTENDED MEANING HAVE TO BE PRESERVED 09/06/2010

III. Quality Jean-Baptiste THOMAS, 10 Need Want to ask for more funding ? I am a good worker, Give me more money! CONSUMER: AESTHETIC AND INTENDED MEANING HAVE TO BE PRESERVED 09/06/2010

III. Quality Jean-Baptiste THOMAS, 11 Need Get more work! I am a good worker, Give me more money! CONSUMER: AESTHETIC AND INTENDED MEANING HAVE TO BE PRESERVED 09/06/2010

III. Quality Jean-Baptiste THOMAS, 12 Need PROFESSIONAL: THE COLOR RENDERING HAS TO BE PERFECTLY ACCURATE Color rendering of multi-spectral images of art paintings under different illuminants (Image: C2RMF) 09/06/2010

III. Quality Jean-Baptiste THOMAS, 13 Set of thresholds Compilation of previous works adapted to the need Nothing more than a rule of thumb 09/06/2010

III. Quality Jean-Baptiste THOMAS, 14 Models classification PLVCBalaPLCC*Polyharmonic splines GOGO 54 (XYZ) measures 1 to 3 visual tasks times 1 to 3 pictures 54 (Y) measures 3 (XYZ) 216 (XYZ) measures 4 to 54 (Y) measures 3 (XYZ) Technology dependent Technology independent CRT Professional or Consumer ConsumerProfessional or Consumer ProfessionalConsumer The efficiency of a model is dependent on several factors: the number of measurements, the nature of the data to measure, the computational cost, its accuracy, etc. It depends strongly on the display 09/06/2010

III. Quality Jean-Baptiste THOMAS, 15 Global quality of a system ? Technology: spatial, temporal Gamut mappingInverse model 09/06/2010

Jean-Baptiste THOMAS, 16 MODEL -1 CIELAB RGB 1 DISPLAY WHITE XYZ 1 METRIC XYZ 2 RGB 2 Evaluation of the model inversion Global quality of a system ? 09/06/2010 MODEL III. Quality

Jean-Baptiste THOMAS, 17 MODEL -1 CIELAB DISPLAYMEASURERGB 1 DISPLAY WHITE XYZ 1 METRIC XYZ 2 RGB 2 Evaluation of the inverse model Global quality of a system ? 09/06/2010 III. Quality

Jean-Baptiste THOMAS, 18 MODEL -1 CIELAB 1 DISPLAYMEASURE RGB DISPLAY WHITE XYZ 1 CIELAB 2 METRIC XYZ 2 Perceptual evaluation of the inverse model Global quality of a system ? 09/06/2010 III. Quality

Jean-Baptiste THOMAS, 19 Spatial difference in lightness and chroma Global quality of a system ? 09/06/2010

III. Quality Jean-Baptiste THOMAS, 20 A illuminationD65 illumination Colors out of gamut Global quality of a system ? Gamut mapping 09/06/2010

III. Quality Jean-Baptiste THOMAS, 21 Global quality of a system ? Technology: spatial, temporal Gamut mappingInverse model 09/06/2010

III. Quality Jean-Baptiste THOMAS, 22 Global quality of a system ? NO INFORMATION ON ACTUAL QUALITY 09/06/2010

IV. This is almost the end Jean-Baptiste THOMAS, 23 So what? Required accuracy depends on the need o No opposition with constraints Model quality ≠ Color reproduction quality o Depending on purpose Spatial issue o Colorimetry and color management are point wise Psycho-physical evaluation is difficult and expensive 09/06/2010

IV. This is almost the end Jean-Baptiste THOMAS, 24 The worst thing IT IS IMAGE DEPENDENT 09/06/2010

IV. This is the end, beautiful friend Jean-Baptiste THOMAS, 25 CHOOSE WHAT YOU WANT TO BE:  Be silly: Try to find a solution  Be pragmatic: Pretend it is OK like this  Be wise: Change research interest  Be lazy: Go to fish 09/06/2010