1. Camerabased projector calibration, investigation of the Bala method
Espen Bårdsnes Mikalsen
The Norwegian Color Research Laboratory
Faculty of Computer Science and Media Technology
Gjøvik University College, Gjøvik, Norway
Supervisors: Jon Yngve Hardeberg and Jean-Baptiste Thomas
Master thesis presentations, Gjøvik,

2. Outline Introduction Experimental setup Results Conclusion
Implementing the Bala method
Experimental setup
Results
Conclusion

3. The Bala method
Calibration method for projectors presented by Raja Bala and Karen Braun, Xerox co, 2006.
Focuses on tone response calibration, no 3 x 3 transform matrix.
Using a digital photo camera as a luminance measurement device by calibrating camera through visual luminance matching. Using the calibrated camera to tone response calibrate projector.

4. Research questions Q1: Verification of the Bala method
An evaluation of the calibration method suggested by Bala and Braun. Implementation and performance testing with a final numeric results and analysis as a measure of performance. Separate evaluation of methods parts to identify strengths and weaknesses in the approach.
Q2: Extensions to the Bala method
In the original paper where the calibration method was presented the authors purposed some extensions that could increase the methods performance. Through implemetation and analysis these will be evaluated. Extensions are visual matching of three luminance values per estimated curve instead of one, and separate correction of R, G and B color channel replacing uniform luminance correction.

5. Simple walkthrough of the Bala method
Step 1 - gather information
Step 2 - process information to estimate projector tone response and correction curve

6. Step 1 - Visual matching of luminance
A binary rasterpattern consisting of 50% black and 50% white pixels. Adjust the background colors luminance to perceptually match binary pattern. The adjusted luminance of background is the perceptually found 50% luminance value.

7. Display calibration target
Luminance patch chart ranging from min to max luminance, with horizontal and vertical duplications of matched 50% luminance for use with non-uniformity correction.

8. Step 1 – Capture image
Capture an image of the projected chart with the uncalibrated camera.

9. Step 2 – Retrive data from captured image
Rotate and crop image

10. Step 2 – Retrive data from captured image
Read RGB data from image. Gives 24 individual sets of RGB.
RGB sets are converted to a luminance value for each patch.

11. Step 2 – Perform non-uniformity correction
Devices like cameras and projectors often suffer from some kind of spatial non-uniformity. Non-uniformity is when a device responds spatially non-uniform to a uniform input. When capturing an image of a projection, the image will suffer from both non-uniformity of projector and of camera.
This method does not correct for non-uniformity in projection, but corrects for non-uniformity in data used for calibration of camera and projector.
Correction are based on calculating differances between the spread out duplications of the 50% luminance patch.

12. Step 2 – Estimating camera tone response curve
Knowing the relationship between projected luminances and target luminances ( min, max and 50% ) makes it possible to interpolate an estimation of the cameras tone response curve.
Description
Luminance
Captured camera value
Projector white
Yw = 1
Ypatch white
Projector black Yb
Ypatch black
Mid-gray
( 1 + Yb ) / 2
Ypatch 50% lum
Perfect black

13. Step 2 - Example of estimated camera TRC

15. Extensions to original method
Instead of only determining camera TRC based on the 50% luminance point. Add two new luminances ( 25% and 75% ) to help determine curve.
Instead of using same correction curve for R, G and B channel. Estimate curves separatly.

16. Experiment setup Projectors Projectiondesign ActionOne DLP, 2003 model
Panasonic AX-100 LCD, 2006 model
Cameras
Nikon D200 DSLR
FujiFilm s7000 compact digital camera
Spectroradiometer
Minolta
Room conditions
Dark room, only luminance from projection

17. Results – Visual matching
Experiment set up to determine if visually matched luminance values deviate from person to person and when repeating matching of same value. Matching done at 3 luminance levels for R, G, B and gray channel.
6 observers visually matching 12 luminances 3 times. A total of 216 values were matched.
Channel
Mean
Minimum
Maximum
% Deviance
Gray 25%
0,5114
0,4748
0,5262
5,14
Gray 50%
0,6959
0,6889
0,7020
1,31
Gray 75%
0,8554
0,8419
0,8706
2,87
Red 25%
0,5096
0,4850
0,5377
5,27
Red 50%
0,6957
0,6848
0,7076
2,28
Red 75%
0,8423
0,8141
0,8594
4,53
Green 25%
0,5099
0,4831
0,5237
4,06
Green 50%
0,6939
0,6894
0,7023
1,29
Green 75%
0,8521
0,8328
0,8656
3,28
Blue 25%
0,5302
0,5204
0,5622
4,18
Blue 50%
0,7169
0,7082
0,7308
2,26
Blue 75%
0,8628
0,8496
0,8748
2,52

18. Results - non-uniformity correction

19. Results – Camera TRC estimation (ActionOne - Nikon)

20. Results – Camera TRC estimation (Panasonic - Nikon)

21. Results – Camera TRC estimation (ActionOne - Nikon)

22. Results – Estimated projector TRC (ActionOne - Nikon)

23. Results – Estimated projector TRC (Panasonic - Nikon)

24. Results – Correction w/ correction curve (ActionOne-Nikon)

25. Results – Correction w/ separate RGB correction curves (ActionOne-Nikon)

26. Results – Correction w/ correction curve (ActionOne-Nikon)

27. Conclusion Q1: Verification of the Bala method
It has to some extent been proven that calibration of projectors using this method will result in a more exact reproduction of color then for example using standard sRGB gamma correction. Correction results are better for DLP then LCD projector, probably because of LCD conforms better to the sRGB gamma curve, and correction will therefore be less necessary.
Q2: Extensions to the Bala method
It has been proven that interpolating camera TRC with not only one visually matched point, but several will improve accuracy of camera TRC, and therefore also estimated projection TRC and correction curve. If retrieving separate camera TRC for R, G and B color channel has a positive effect on method performance has not yet been proven.