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COLOR CONSTANCY IN THE COMPRESSED DOMAIN

Jayanta Mukhopadhyay Department of Computer Science & Engineering Indian Institute of Technology, Kharagpur, 721302, India j ay@cse.iitkgp.ernet.in Sanjit K. Mitra Ming Hsieh Dept. of Electrical Engineering University of Southern California Los Angeles, CA 90089, USA s kmitra@usc.edu.

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COLOR CONSTANCY IN THE COMPRESSED DOMAIN

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  1. Jayanta Mukhopadhyay Department of Computer Science & Engineering Indian Institute of Technology, Kharagpur, 721302, India jay@cse.iitkgp.ernet.in Sanjit K. Mitra Ming Hsieh Dept. of Electrical Engineering University of Southern California Los Angeles, CA 90089, USA skmitra@usc.edu COLOR CONSTANCY IN THE COMPRESSED DOMAIN

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

  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: http://www.cs.sfu.ca/ 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 Color Correction

  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!

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