1. Jueqin Qiu, Haisong Xu*, Peng Xu
Comparison of Object-Color and Illuminant Metamer Mismatching for Digital Image Color Correction
State Key Laboratory of Modern Optical Instrumentation
College of Optical Science and Engineering
Zhejiang University, China

2. BACKGROUND | 1

3. = dλ Metamerism ! Background × ×
Metamer mismatching is a phenomenon that two object colors, which are colorimetrically indistinguishable under one lighting and viewing condition, become distinguishable under another one.
Metamerism !

4. Background
Raw Data
After White-balancing
Displayable Output

5. Background
Color correction transforms the images to the appearance that captured under a reference illuminant, like D65
Usually completed by linear transformations such as a 3×3 matrix
Color correction can possibly enlarge the metamer mismatching
Raw Data
After White-balancing
Displayable Output

6. METHODS | 2

7. Methods
Object-color Metamer Mismatching 𝜆
Hyper-spectral image (HSI)

8. Methods
Object-color Metamer Mismatching
Illuminant A
D65
D75
TL84
CWF

9. Methods 106 hyper-spectral images (HSIs) in total
Object-color Metamer Mismatching
106 hyper-spectral images (HSIs) in total
Cover most of image categories in daily life: Plants, Human skins, Outdoor scenes, Printing materials, Artifact, etc.
For each test hyper-spectral image, 8 test illuminants were repeated and then averaged

10. Methods Object-color Metamer Mismatching
Reconstruction of the color corrected images
as well as the object-color metamer mismatching image

11. printing materials and
Methods
Object-color Metamer Mismatching
Database name
Content
HSI number
Foster
plants, architectures
and paper products 19
Yasuma
Miscellaneous 32
UEF
skins and artefacts 12
Skauli
facial skins, plants
and architectures 33
Moan 9
Hordley
printing materials and
Packages 21
Parraga
Plants 27
Eckhard
outdoor scenes and
Architectures 14
Spectral Reflectance Database
From 8 online databases
7,326,497 samples in total
100% sRGB gamut
Duplicates and outliers have been removed

12. Methods Illuminant Metamer Mismatching
Each test illuminant has 2 metamers:
daylight series (daylight spectra by Judd)
4-channel LED series (NIST-CQS by Ohno and Davis)
F2 (Cool White Fluorescent)
4-ch LED metamer: same chromaticity coordinate
Daylight metamer: same correlated color temperature (CCT) but different chromaticity coordinate

13. Methods Illuminant Metamer Mismatching
Each test illuminant has 2 metamers:
daylight series (daylight spectra by Judd)
4-channel LED series (NIST-CQS by Ohno and Davis)
4-ch LED metamer: same chromaticity coordinate
Daylight metamer: same correlated color temperature (CCT) but different chromaticity coordinate
8 test illuminants in total were investigated: CIE illuminant A, D50, D100, CWF, F8, TL84, D50 LED simulator, and iPhone Flash

14. RESULTS | 3

15. Results Ground Truth D65 Cool White Fluorescent
Object-color Metamer Mismatching (50%)
Object-color Metamer Mismatching (95%)
Illuminant Metamer Mismatch (Daylight)
Illuminant Metamer Mismatch (4-LED)
Color Corrected

16. Results Full-reference Image Quality Assessment Metric:
Directional Statistics based Color Similarity Index (DSCSI)†
† Dohyoung Lee et. al., Towards a Full-Reference Quality Assessment for Color Images Using Directional Statistics, IEEE Transactions on Image Processing, 24 (11), Nov, 2015.

17. Results DSCSI Scores 0.9933 0.9803 0.9508 0.8320 0.9084 Ground Truth
Cool White Fluorescent
Object-color Metamer Mismatching (50%)
Object-color Metamer Mismatching (95%)
Illuminant Metamer Mismatch (Daylight)
Illuminant Metamer Mismatch (4-LED)
Color Corrected
0.9933
0.9803
0.9508
0.8320
0.9084
DSCSI Scores

18. Results
Pooling 106 hyper-spectral images and 8 test illuminants together:
Median
Quantiles
Min & Max

19. Conclusions
Object-color metamer mismatching is tolerable for digital image color correction
Illuminant metamer mismatching is more severe to degrade the fidelity of color reproduction
Detecting the spectral information of the captured scene is the only way to solve the metamer mismatching
Acquisition of the spectral power distribution (SPD) of the light source is much easier than acquiring a hyper-spectral image

20. Thanks for Your Attention!
Jueqin Qiu, Haisong Xu*, Peng Xu
Thanks for Your Attention!