1. Automatic Color Balancing
- Prasanna Venkatesan
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2. Outline Motivation Problem Statement Algorithms Attempted Solution
Lessons Learnt
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3. Motivation
Mosaicing images using cameras arranged to give omni direction view results in color mismatch.
Each camera is capturing a different scene
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5. Problem Statement
To extract the parameters used by the FireWire Camera (it uses 2 channels) for color balancing an acquired image.
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7. Problem in extracting these parameters
Fire Wire APIs – Provide for setting and recovering the color channel parameters.
APIs for recovering color balancing parameters are non-existent.
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8. Algorithms Attempted
To use an algorithm which would give similar results to that used by the cameras algorithm.
Algorithms attempted
Gray world, White patch
hybrid of gray world and white patch assumption
Polynomial Mapping
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9. Description of Gray world Algorithm
Assumes average color of image – predefined value of grey.
Rn = Ro * mean(Intensity)/ mean(Ro)
Gn = Go * mean(Intensity)/ mean(Go)
Bn = Bo *mean(Intensity)/ mean(Bo)
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11. Results
Dependant - average Intensity and means of respective channels in non color balanced image
For high intensity pixels, for lower intensity pixels algorithm over corrects.
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12. Description of White Patch
Assumes that maximum value of each channel – corresponds to 255
Rn=Ro*255/max(Ro)
Gn=Go*255/max(Go)
Bn=Bo*255/max(Bo)
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14. Results Very poor color correction.
Used along with gray world, much better
color correction.
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15. Description of Polynomial mapping method
Polynomial Coefficients
Non color balanced image
Cameras Color balanced image
Coefficients
a1=Mean ( Pixels(Meancb +/- sdcb)) / Mean(Pixels(Meanncb +/- sdncb))
a2=Mean( Pixels(Meancb+/- 2*sdcb)) / Mean(Pixels(Meanncb +/- 2*sdncb))
a3=Mean ( Pixels(Meancb +/- 3*sdcb)) / Mean(Pixels(Meanncb+/- 3*sdncb))
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16. Results
In case of a good distribution of colors -resultant image similar to cameras color balanced image
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18. Results (contd.)
If the distribution of colors is less, then resultant image
Has out of gamut pixels
Not similar to that of color balanced image
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20. The solution Properties of Color Balanced Image (cameras Algorithm)
Means of the channels (corresponding to color space) converge -similar value.
value dependant on illuminant of the scene.
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21. Mean(red)-0.9569 Mean(green)-0.5807 Mean(blue)-1.4180
Color Imbalanced Image
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22. Mean (red)-0.5888 Mean (green)-0.5719 Mean (blue)-0.5965
Color balanced Image
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23. Reverse Engineering to Obtain Parameters
Using Fire Wire APIs
Set parameters for the color channels
Scan through range of all the possible parameters.
Find the 2 mean channel ratios
redavg/greenavg,
blueavg/greenavg for each set value.
Find the combinations of the parameters which make the 2 mean ratios to lie within the threshold of 0.9 & 1.1.
Choose those values whose mean ratios are nearest to one.
Retrieve these values, as the camera balancing parameters
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24. Demo
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25. Lessons Learnt In any project – what ever be its nature.
Spend enough time in the problem space, understanding the problem.
Understanding why it is a problem.
Clients way of viewing the problem- need not be right.
For instance, U/B & V/R was mistaken to be U over B & V over R rather than U or B and V or R
Y = *R+0.587*G+0.114*B
U/B = 0.492*(1-Y/B)
= *(R/B) *(G/B)
V/R = 0.877*(1-Y/R)
= *(G/R) *(B/R)
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26. References
Fast color correction using principal regions mapping in different color spaces
Maojun Zhang*, Nicolas D. Georganas Distributed and Collaborative Virtual Environments Research Laboratory (DISCOVER), University of Ottawa, Ottawa, Canada
A new algorithm for unsupervised global and local color correction Alessandro Rizzi, Carlo Gatta, Daniele Marini July 2003  Pattern Recognition Letters,  Volume 24 Issue 11
Comparison of the accuracy of different white balancing options as
quantified by their color constancy J A Stephen Viggiano, Color Research,West Henrietta, NY, USA
A Comparison of Algorithms for Mapping Color Between Media of Differing
Luminance Ranges ,J. A. Stephen Viggiano and C. Jeffrey Wang,Imaging Division RIT Research Corporation
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