Automatic%20Color%20Balancing

1
Automatic Color Balancing

• - Prasanna Venkatesan

2
Outline

• Motivation
• Problem Statement
• Algorithms Attempted
• Solution
• Lessons Learnt

3
Motivation

• Mosaicing images using cameras arranged to give
  omni direction view results in color mismatch.
• Each camera is capturing a different scene

4
(No Transcript)
5
Problem Statement

• To extract the parameters used by the FireWire
  Camera (it uses 2 channels) for color balancing
  an acquired image.

6
(No Transcript)
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.

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

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)

10
(No Transcript)
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.

12
Description of White Patch

• Assumes that maximum value of each channel
  corresponds to 255
• RnRo255/max(Ro)
• GnGo255/max(Go)
• BnBo255/max(Bo)

13
(No Transcript)
14
Results

• Very poor color correction.
• Used along with gray world, much better
• color correction.

15
Description of Polynomial mapping method
Polynomial Coefficients
Non color balanced image
Cameras Color balanced image
Coefficients
a1Mean ( Pixels(Meancb /- sdcb)) /
Mean(Pixels(Meanncb /- sdncb))
a2Mean( Pixels(Meancb/- 2sdcb)) /
Mean(Pixels(Meanncb /- 2sdncb))
a3Mean ( Pixels(Meancb /- 3sdcb)) /
Mean(Pixels(Meanncb/- 3sdncb))
16
Results

• In case of a good distribution of colors
  -resultant image similar to cameras color
  balanced image

17
(No Transcript)
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

19
(No Transcript)
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.

21
(No Transcript)
22
Mean (red)-0.5888 Mean (green)-0.5719 Mean
(blue)-0.5965
Color balanced Image
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

24
Demo

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 0.299R0.587G0.114B
• U/B 0.492(1-Y/B)
• 0.3780-0.1471(R/B) -0.2888(G/B)
• V/R 0.877(1-Y/R)
• 0.6148-0.5148(G/R) -0.1006(B/R)

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
• www.poynton.com
• http//www.vision.ee.ethz.ch/buc/brechbuehler/mir
  ror/color/Poynton-color.html
• 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, jasv_at_acolyte-color.comAcolyte
  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