Illuminant Estimation Through Reverse Calibration of an Auto White-Balanced Image That Contains Displays (CIC 27 Oral Presentation)

Illuminant Estimation Through Reverse algorithm of
an Auto White-Balanced Image That Contains Displays
Taesu Kim, Eunjin Kim, Hyeon-Jeong Suk*
Color lab, Department of Industrial Design, KAIST, Daejeon, Korea
AWB image Reversed image
Matrix applied
(N=27) (N=27)
RGBafterAWB RGBoriginalExtract gain matrix
Rgain
Ggain
Bgain
Ggain
0 0
10 0
00
[ ]

© Color lab, 2019 | Color Imaging Conference 27 2
Auto white balancing
Introduction
When people look at the object,  
its colors stay relatively constant,
relative to the illuminant changes.
However, camera doesn’t
© Color lab, 2019 | Color Imaging Conference 27

SUBtitle
TITLE
So, in digital image processors,  
color constancy is achieved through
automatic white balancing (AWB).

Previous methods
Introduction
Among various illuminant estimation methods,  
White Patch* and Gray World** are widely known solutions 1
* typical of the Lightness Constancy adaptation;
it centers the histogram dynamic, working the same way as the exposure control on a camera
** typical of the Color Constancy adaptation;
searching for the lightest patch to use as a white reference similar to how the human visual system does
1 Rizzi, A., Gatta, C. and Marini, D. Color correction between gray world and white patch. International Society for Optics and Photonics, City, 2002.

Previous methods
Introduction
However, the lack of the previous method’s limitations,
people use a Macbeth Color Checker to estimate  
the original illuminant at the time the picture was taken.

motivation
Introduction
Manual mode imageAuto white balanced image
Look more bluish

motivation
Introduction
Manual mode imageAuto white balanced image
Reverse AWB
by using display as target

motivation
Introduction
Reversed image
Get information of ambient lighitngs.
Do not have to use color checker 
Can predict far away lighting condition

Are display’s white points did not change  
under varying illuminants?
Research question 1.
Research question 2.
Can we reverse the AWB image  
by using display as reverse target?

Display white measurement
Session 1.
Session 2.
Illuminant estimation using reverse algorithm

Session 2.
Session 1.

Photo was taken by Canon 100D, in manual mode, recorded to .CR2 format
12
Image setup
Session 1. Display white measurement
M

Ambient lighting setup
Plankian
locus
0.3
0.3
0.6
0.9
0.6 0.9
x
y
780
380450
475
500
525
575
550
600
625
650
Spectral locus
Purple line
Spectral locus
Purple line
High Chroma
Chroma
Nuanced-white
Illuminant
category
Name
CCT [K] /
Dominant wavelength (nm)
x y
Nuanced-
white
(800 lux)
N=9
3K 3035 0.436 0.406
4K 3911 0.379 0.360
5K 5087 0.345 0.385
6.5K 6266 0.317 0.336
8.5K 8708 0.283 0.313
10K 10291 0.270 0.301
11.5K 11498 0.261 0.297
15K 15116 0.254 0.271
20K 21351 0.245 0.253
Chroma
(800 lux)
N=12
wR 616.8 0.488 0.323
wO 573.1 0.413 0.463
wY 559.0 0.344 0.508
wYG 529.8 0.230 0.576
wG 485.9 0.178 0.269
wC 478.8 0.164 0.186
wCB 473.8 0.160 0.138
wB 466.6 0.156 0.083
wBP 443.8 0.207 0.108
wP 445.0 0.206 0.108
wM -563.3 0.251 0.127
wPi -550.6 0.316 0.174
High
Chroma
N=6
R 619.7 0.602 0.314
Y 574.4 0.450 0.494
G 530.6 0.195 0.695
B 477.9 0.145 0.160
P 464.3 0.140 0.043
M -563.3 0.239 0.091

Stimuli setup
iPad 3 iPhone 6 Galaxy S7 LG G5
Using Photoshop’s eye dropper tool to measure each display’s R, G, B value

results
9 kinds of
Nuanced
whites
[113, 111, 113] ±  
[7.9, 4.1, 16]
[137, 144, 162] ±  
[2, 4, 5.5]
[135, 153, 148] ±  
[4.3, 2.9, 2.7]
[109, 118, 127] ±  
[5.4, 2.5, 5.2]
18 kinds of
Chromatic
lights
[100, 101, 142] ±  
[23, 23, 52]
[134, 142, 169] ±  
[6.3, 7, 9.3]
[131, 151, 153] ±  
[4.9, 4.6, 11]
[104, 116, 142] ±  
[10, 8.5, 26]
* Average red (R), green (G), blue (B) values and standard deviation of white points

© Color lab, 2019 | Color Imaging Conference 27 16© Color lab, 2019 | Color Imaging Conference 27
9 kinds of
Nuanced
whites
[113, 111, 113] ±  
[7.9, 4.1, 16]
[137, 144, 162] ±  
[2, 4, 5.5]
[135, 153, 148] ±  
[4.3, 2.9, 2.7]
[109, 118, 127] ±  
[5.4, 2.5, 5.2]
18 kinds of
Chromatic
lights
[100, 101, 142] ±  
[23, 23, 52]
[134, 142, 169] ±  
[6.3, 7, 9.3]
[131, 151, 153] ±  
[4.9, 4.6, 11]
[104, 116, 142] ±  
[10, 8.5, 26]
Discussion
* Average red (R), green (G), blue (B) values and standard deviation of white points
In Session 2,  
we used the iPhone 6 and iPad 3  
as the best and worst devices.

Session 1.
Session 2.

Stimuli
Session 2. Illuminant estimation using reverse calibration
Original image AWB image
AWB applied 
using Lightroom CC
(N=27) (N=27)

Reverse algorithm
Matrix applied
(N=27) (N=27)
Rgain
Ggain
Bgain
Ggain
0 0
10 0
00
[ ]

RESULTs Nuanced White
(5087K)
Green Light
(Mixed with white)
Yellow Light
Original
image
Estimated
by iPhone 6
Estimated
by iPad 3

RESULTs Nuanced White
(5087K)
Green Light
(Mixed with white)
Yellow Light
Original
image
Estimated
by iPhone 6
Estimated
by iPad 3
Using white patch algorithm to
calculate ambient illuminants’ chromaticity.
For discussion,
then, calculate CCT for Nuanced whites,
dominant wavelength for Chromatic lightings

Discussion
Nuanced White
(5087K)
Measured
Illuminant
by Minolta CL 200A
3035 3911 5087 6266 8708 10291 11498 15116 21351
Original Image
by White patch
2199 2926 3846 6075 6977 8229 8880 9790 12123
iPhone 6
by White patch
1913 2094 2898 4889 5611 6138 6520 6886 7444
iPad 3
by White patch
2964 3563 4381 5935 6386 7009 6807 7094 7080
* Correlated Color Temperature of illuminants [K]
Original image
Estimated by iPhone 6
Estimated by iPad 3

Discussion
Nuanced White
(5087K)
Measured
Illuminant
by Minolta CL 200A
3035 3911 5087 6266 8708 10291 11498 15116 21351
Original Image
by White patch
2199 2926 3846 6075 6977 8229 8880 9790 12123
iPhone 6
by White patch
1913 2094 2898 4889 5611 6138 6520 6886 7444
iPad 3
by White patch
2964 3563 4381 5935 6386 7009 6807 7094 7080
* Correlated Color Temperature of illuminants [K]
Average difference from the
measured CCT (~6266 K)
Average difference from the
measured CCT (all range)
813 2691
1626 4541
364 3783
Reverse algorithm shows more accurate results on  
CCT range that we experience in daily life.
23

Discussion
Green Light
(Mixed with white)
Measured
Illuminant
by Minolta CL 200A
616.8 573.1 559.0 529.8 485.9 478.8 473.8 466.6 443.8 445.0 -563.3 -550.6
Original
Image
by White patch
611.8 587.7 574.4 561.1 537.8 485.2 476.5 495.0 471.0 465.0 -562.8 -550.4
iPhone 6
by White patch
623.1 594.5 580.0 567.6 548.9 487.1 478.3 475.7 470.5 -560.8 -541.9 -541.6
iPad 3
by White patch
615.4 539.5 541.7 549.6 492.0 492.0 491.7 500.1 497.0 518.2 529.1 591.9
wR wO wY wYG wG wC wCB wB wBP wP wM wPi
* Dominant wavelength, relative to D65 white standard (nm)
Original image
Estimated by iPad 3

Discussion
Yellow Light
Measured
Illuminant
by Minolta CL 200A
619.7 574.4 530.6 477.9 464.3 -563.3
Original Image
by White patch
611.8 587.7 574.4 561.1 537.8 485.2
iPhone 6
by White patch
623.1 594.5 580.0 567.6 548.9 487.1
iPad 3
by White patch
611.8 587.7 574.4 561.1 537.8 485.2
R Y G B P M
Original image
Estimated by iPad 3

Yellow Light
Green Light
(Mixed with white)
Discussion
Measured
Illuminant
by Minolta CL 200A
-563.3 -550.6 477.9 464.3 -563.3
Original Image
by White patch
-562.8 -550.4 561.1 537.8 485.2
iPhone 6
by White patch
-541.9 -541.6 567.6 548.9 487.1
iPad 3
by White patch
529.1 591.9 561.1 537.8 485.2
wM wPi B P Pk
Reverse algorithm shows less accurate results on  
both purplish and vividly bluish illuminants.
iPad 3 iPhone 6
Nuanced
whites
[113, 111, 113] ±  
[7.9, 4.1, 16]
[137, 144, 162] ±  
[2, 4, 5.5]
Chromatic
lights
[100, 101, 142] ±  
[23, 23, 52]
[134, 142, 169] ±  
[6.3, 7, 9.3]
This may be due mainly to the fact that  
the recording of B was more unstable
26

Session 2. Can we reverse the AWB image by using display as reverse target?
Conclusion
Conclusion
• luminous surfaces of devices under varying illuminants to make sure the  
hue characteristics of luminous surfaces appear constantly.
27
• Estimation revealed better estimation performance for the illuminants up to 6200 K.
• Reverse algorithm is more powerful for estimating the range between green and red.
Session 1. Are display’s white points did not change under varying illuminants?

Limitation & future work
Conclusion
• Have to approve blue value error by calculating difference between color checker and display.
• Research focused on experimental environment, so we need to check on real environment
• In case of true tone adjusts the white point of the display, according to the ambient lighting,  
to best serve color consistency, should be hard to use this method.
28

Thank you for listening
Taesu Kim, Eunjin Kim, Hyeon-Jeong Suk
eddie.ts.kim@kaist.ac.kr
Color lab, Department of Industrial Design, KAIST, Daejeon, Korea
Matrix applied
(N=27) (N=27)
Rgain
Ggain
Bgain
Ggain
0 0
10 0
00
[ ]

Illuminant Estimation Through Reverse Calibration of an Auto White-Balanced Image That Contains Displays (CIC 27 Oral Presentation)

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