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SMPTE Motion Imaging Journal ( Volume: 128, Issue: 7, August 2019)
[ACTIVE]

Bayesian Methods for Radiometric Calibration in Motion Picture Encoding Workflows

Metadata

Publisher
SMPTE — White Plains, NY, USA
Doc Type
Journal Article
Content Type
Original Research
Abbreviated Title
SMPTE Mot. Imag. J
Volume
128, No. 7, pp. 25–30
Abstract
The accurate estimation of a camera response function (CRF) allows for proper encoding of camera exposures into motion picture post-production workflows like the Academy Color Encoding System (ACES), helping minimize noncreative manual adjustments. Although there are well-known standard CRFs implemented in typical video camera workflows, motion picture camera workflows and new high dynamic range (HDR) workflows have introduced new standard CRFs, as well as custom and proprietary CRFs. Current methods to estimate this function rely on the use of test charts, restrictive exposure and/or lighting conditions, or assume a simplistic model of the function’s shape. All of these methods become problematic and tough to fit into motion picture production and post-production workflows, where the use of test charts and varying camera or scene setups becomes impractical. We propose a method initially based on the work of Lin, Gu, Yamazaki, and Shum that considers edge color mixtures in an image or image sequence that are affected by the nonlinearity introduced by a CRF. This feature is then used in a Bayesian framework to estimate a posterior probability distribution function of the CRF model parameters approximated by a Markov Chain Monte Carlo (MCMC) algorithm, allowing for a more robust description of the CRF over methods like maximum likelihood (ML) and maximum a posteriori (MAP).
Publication Date
2019-08-01
DOI
10.5594/JMI.2019.2917595
ISSN
Print: 1545-0279 | Electronic: 2160-2492
Link
https://doi.org/10.5594/JMI.2019.2917595
Author(s)
Ricardo R. Figueroa
Rui Li
Keyword(s)
Camera Response Function (CRF), Gamma Correction, Radiometric Calibration
Copyright
© 2019 SMPTE

Bibliographic Reference(s)

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  • M. Diaz and P. Sturm, “Radiometric Calibration Using Photo Collections,” Proc. IEEE International Conference on Computational Photography, pp. 1–8, Apr. 2011. EXTERNAL
  • R. R. Figueroa and J. Gu, “Camera Radiometric Calibration From Motion Images,” presented at the SMPTE 2013 Annual Technical Conference and Exhibition, Hollywood, CA, Oct. 2013. EXTERNAL
  • M. D. Grossberg and S. K. Nayar, “Determining the Camera Response From Images: What Is Knowable?” IEEE Trans. Pattern Anal. Mach. Intell., 25(11):1455–1467, Nov. 2003. EXTERNAL
  • S. Lin, J. Gu, S. Yamazaki, and H.-Y. Shum. “Radiometric Calibration from a Single Image,” Proc. IEEE Computer Society Conference on Computer Vision and Pattern Recognition, vol. 2, pp. II–938–II–945, Jul. 2004. EXTERNAL
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  • S. J. Kim, J.-M. Frahm, and M. Pollefeys. “Joint Feature Tracking and Radiometric Calibration From Auto-Exposure Video,” Proc. IEEE 11th International Conference on Computer Vision, pp. 1–8, Oct. 2007. EXTERNAL
  • T. Mitsunaga and S. K. Nayar, “Radiometric Self Calibration,” Proc. IEEE Computer Society Conference on Computer Vision and Pattern Recognition, vol. 1, p. 2, 1999. EXTERNAL
  • M. D. Grossberg and S. K. Nayar, “Modeling the Space of Camera Response Functions,” IEEE Trans. Pattern Anal. Mach. Intell., 26(10):1272–1282, Oct. 2004. EXTERNAL
  • H. Farid. “Blind Inverse Gamma Correction,” IEEE Trans. Image Process., 10(10):1428–1433, Oct. 2001. EXTERNAL
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  • Andrieu, N. D. Freitas, A. Doucet, and M. I. Jordan, “An Introduction to MCMC for Machine Learning,” Mach. Learn., 50(1–2):5–43, 2003. EXTERNAL
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Ricardo R. Figueroa and Rui Li; Bayesian Methods for Radiometric Calibration in Motion Picture Encoding Workflows, SMPTE Motion Imaging Journal ( Volume: 128, Issue: 7, August 2019); SMPTE, 2019. Available at https://doi.org/10.5594/JMI.2019.2917595
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Ricardo R. Figueroa and Rui Li; Bayesian Methods for Radiometric Calibration in Motion Picture Encoding Workflows, SMPTE Motion Imaging Journal ( Volume: 128, Issue: 7, August 2019); SMPTE, 2019. Available at https://doi.org/10.5594/JMI.2019.2917595

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Ricardo R. Figueroa and Rui Li; Bayesian Methods for Radiometric Calibration in Motion Picture Encoding Workflows, SMPTE Motion Imaging Journal ( Volume: 128, Issue: 7, August 2019); SMPTE, 2019. Available at https://doi.org/10.5594/JMI.2019.2917595
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<span class="citation">Ricardo R. Figueroa and Rui Li; <cite>Bayesian Methods for Radiometric Calibration in Motion Picture Encoding Workflows</cite>, SMPTE Motion Imaging Journal ( Volume: 128, Issue: 7, August 2019); SMPTE, 2019. Available at <a href="https://doi.org/10.5594/JMI.2019.2917595" target="_blank" rel="noopener">https://doi.org/10.5594/JMI.2019.2917595</a></span>

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Ricardo R. Figueroa and Rui Li; Bayesian Methods for Radiometric Calibration in Motion Picture Encoding Workflows, SMPTE Motion Imaging Journal ( Volume: 128, Issue: 7, August 2019); SMPTE, 2019
doi: 10.5594/JMI.2019.2917595
url: https://doi.org/10.5594/JMI.2019.2917595
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Ricardo R. Figueroa and Rui Li; <cite id="bib-10-5594-jmi-2019-2917595">Bayesian Methods for Radiometric Calibration in Motion Picture Encoding Workflows</cite>, SMPTE Motion Imaging Journal ( Volume: 128, Issue: 7, August 2019); SMPTE, 2019
<span class="doi">10.5594/JMI.2019.2917595</span>
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