1. COLOR CONSTANCY IN THE COMPRESSED DOMAIN
Jayanta Mukhopadhyay
Department of Computer Science & Engineering
Indian Institute of Technology, Kharagpur, , India
Sanjit K. Mitra
Ming Hsieh Dept. of Electrical Engineering
University of Southern California
Los Angeles, CA 90089, USA

2. Problem of Color Constancy
Three factors of image formation:
Objects present in the scene.
Spectral Energy of Light Sources.
Spectral Sensitivity of sensors.
Spectral Response of a Sensor
Spectral Power Distribution
Surface Reflectance Spectrum

3. Same Scene Captured under Different Illumination
Can we transfer colors from one illumination to another one?

4. Computation of Color Constancy
Deriving an illumination independent representation.
- Estimation of SPD of Light Source.
Color Correction
- Diagonal Correction.
E(λ)
<R, G, B>
To perform this computation with DCT coefficients.

5. Different Spatial Domain Approaches
Gray World Assumption (Buchsbaum (1980), Gershon et al. (1988))
<R, G, B> ≡ <Ravg, Gavg, Bavg>
White World Assumption (Land (1977))
<R, G, B> ≡ <Rmax, Gmax, Bmax>

6. Select from a set of Canonical Illuminants
Observe distribution of points in 2-D
Chromatic Space.
Assign SPD of the nearest illuminant.
Gamut Mapping Approach (Forsyth (1990), Finlayson (1996))
- Existence of chromatic points.
Color by Correlation (Finlayson et. al. (2001))
- Relative strength over the distribution.
Nearest Neighbor Approach (Proposed)
- Mean and Covariance Matrix.
- Use of Mahalanobis Distance.

7. Processing in the Compressed Domain
Consists of non-overlapping DCT blocks (of 8 x 8).
Use DC coefficients of each block.
The color space used is Y-Cb-Cr instead of RGB.
Chromatic Space for Statistical Techniques is the Cb-Cr space.

8. Different Algorithms under consideration

9. List of Illuminants

10. Images Captured at Different Illumination
Source: colour/data.

11. Performance Metrics Estimated SPD: E=<RE,GE,BE>
True SPD: T= <RT,GT,BT>

12. Average Δθ

13. Average Δrg

14. Average ΔRGB

15. Average ΔL

16. Time and Storage Complexities
nl: number of illuminants.
nc: size of the 2-D chromaticity space
n: number of image pixels
f: Fraction of chromaticity space covered.
aM+bA  a number of Multiplications and b number of Additions.

17. Time and Storage Complexities

18. Equivalent No. of Additions per pixel (1 M= 3 A)
n=512, nc=32, nl=12, f=1

19. Color Correction: An Example
Image captured with
(solux-4100)
Target Ref. Image
(syl-50mr16q)
COR-DCT
MXW-DCT-Y
COR

20. Color Restoration Original Enhanced w/o Color Correction Enhanced with

21. Conclusion Color-constancy computation in the compressed domain :
- requires less time and storage.
- comparable quality of results.
Both NN and NN-DCT perform well compared to other existing statistical approaches.
Color constancy computation is useful in restoration of colors.

22. Thanks!