Multispectral-Based Color Reproduction Research at the Munsell Color Science Laboratory

Transcription

1 Multispectral-Based Color Reproduction Research at the Munsell Color Science Laboratory ROY S. BERNS Chester F. Carlson Center for Imaging Science Rochester Institute of Technology 54 Lomb Memorial Drive Rochester, New York

2 Overview for PDF This talk was presented at the Center for Imaging Science Fall Industrial Associates Meeting on October 20, It is an overview talk for a session on multispectral imaging. The session included the following speakers: Peter Burns, Geoff Woolfe, Jonathan Arney, and Roger Easton. All of the artwork images were copied from Microsoft Art Gallery, a collection of the National Gallery, London. No attempt has been made by R. Berns to achieve color accuracy.

3 The Goal: The "Virtual" World Matches The Real World

4 The Goal: The "Virtual" World Matches The Real World

5 The Goal: The "Virtual" World Matches The Real World

6 The Apparent Solution: Color Management

7 Device Profile X L* Device Profile Y Z or a* b* reference device Reference device: Reflection print viewed at 45/0 by the 1931 observer under D 50 without flare "Profile Connection Space (PCS)"

8 Implementation dr dg db PCS CRT Colorimetry L* a* b* L* a* b* X Y Z Appearance Model (CRT conditions) -1 Appearance Model (PCS conditions) L C H CRT Profile L* a* b* L* a* b* dc dm dy dk Appearance Model (PCS conditions) -1 Print Colorimetry L* a* b* L C H Gamut Mapping Appearance Model (Print conditions) -1 Printer Proflie

9 CRT to Print Via Color Management

10 Color Imaging Matches Are, By Definition, Metameric Real objects are not composed of cyan, magenta, yellow, and black ink

11 Problems Remain: Controlling Lighting

12 Monet's Garden Yes, we expect lighting to affect color, but...

13 Metameric Matches Are Unstable With Changes in Lighting Reflectance Factor Reflectance Factor Wavelength (nm) Wavelength (nm) 680 Two painted surfaces, each colored with different pigments

14 Daylight Illumination What the average observer sees: Note match

15 Incandescent Illumination What the average observer sees: Note mismatch

16 Conventional Imaging Input Devices Are Not Linearly Transformable To Humans Relative sensitivity nm

17 The Camera Is Not A Human What the camera really sees under daylight: Note mismatch

18 The Camera Is Not A Human What the camera really sees under incandescent: Note lack of color balance Note mismatch

19 Problems Remain: Gamut Mismatch

20 The Original and its Reproduction Do Not Match Original Reproduction Copyright 1993, The National Gallery, London

21 The Solution: Multispectral-Based Color Reproduction Multi-channel image storage Spectral reconstruction Ink selection Image capture Multi-ink direct digital printing Spectral-based printing separation minimizing metamerism Copyright 1993, The National Gallery, London

22 Copyright 1993, The National Gallery, London Multispectral-Based System Multi-channel image storage Multispectral digital image capture CRT device colorimetric characterization (inverse) Gamut mapping Colorimetric and appearance conversion Desktop printer device colorimetric characterization (inverse) Multi-ink direct digital printing Spectral-based printing separation minimizing metamerism Spectral reconstruction

23 Research Accomplishments Debra Vent, M.S. Imaging Science, 1994 Multichannel Analysis of Object-Color Spectra James Shyu, M.S. Color Science, 1994 Colorimetric Characterization of a Desktop Drum Scanner Using a Spectral Model Rick Alfvin, M.S. Color Science, 1995 Computational Analysis of Observer Metamerism in Cross-Media Color Matching

24 Research Accomplishments Brian Hawkins, M.S. Color Science, 1996 Defining a Gamut Space Given a Set of Colorants Peter Burns, Ph.D. Imaging Science, 1997 Analysis of Image Noise in Multispectral Color Acquisition Koichi Iino, Visiting Scientist, Spectral Modeling of Ink on Paper

25 Research Proposal

26 Current Research Efforts Di-Yuan Tzeng, Ph.D. Candidate Spectral-Based Color Separation Algorithm Development for Multiple-Ink Color Francisco Imai, Postdoctoral Fellow Multispectral Scene Estimation Using Commercial Imaging Devices

27 Related Research Efforts Jonathan Arney, Faculty Imaging Science Light Scattering and Halftone Modeling Mark Fairchild, Faculty Imaging Science Color Appearance Psychophysics Ethan Montag, Faculty Imaging Science Color Gamut Mapping Geoff Woolfe, M.S. Imaging Science 1997 Spectral Data to Improve Modeling Accuracy

28 Di-Yuan Tzeng Ph.D. Candidate in Imaging Science

29 Preliminary Computations Reflectance factor Wavelength (nm) R fl The Optimal Ink-S Warm red Purple Reflex blue Process blue process black process yellow Ink selection scheme: continuous-tone assumption canonical correlation analysis K/S Wavelength (nm) Mean V1: % V2: % V3: % V4: % V5: % Wavelength

30 Conclusions Multispectral-Based Color Reproduction Has Tremendous Possibilities We Invite Our Industrial Associates To Get Involved