Color-matching Shots from Different Cameras Having Unknown Gamma or Logarithmic Encoding Curves

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Publisher: SMPTE — White Plains, NY, USA
Doc Type: Conference Paper
Content Type: Original Research
Volume: 2017, No. 10, pp. 1–15
Abstract: In cinema and TV it is quite usual to have to work with footage coming from several cameras, which show noticeable color differences among them even if they are all the same model. In TV broadcasts, technicians work in camera control units so as to ensure color consistency when cutting from one camera to another. In cinema post-production, colorists need to manually color-match images coming from different sources. Aiming to help perform this task automatically, the Academy Color Encoding System (ACES) introduced a color management framework to work within the same color space and be able to use different cameras and displays; however, the ACES pipeline requires to have the cameras characterized previously, and therefore does not allow to work ‘in the wild’, a situation which is very common. We present a color stabilization method that, given two images of the same scene taken by two cameras with unknown settings and unknown internal parameter values, and encoded with unknown non-linear curves (logarithmic or gamma), is able to correct the colors of one of the images making it look as if it was captured with the other camera. Our method is based on treating the in-camera color processing pipeline as a combination of a 3x3 matrix followed by a non-linearity, which allows us to model a color stabilization transformation among two shots as a linear-nonlinear function with several parameters. We find corresponding points between the two images, compute the error (color difference) over them, and determine the transformation parameters that minimize this error, all automatically without any user input. The method is fast and the results have no spurious colors or spatio-temporal artifacts of any kind. It outperforms the state of the art both visually and according to several metrics, and can handle very challenging real-life examples.
Publication Date: 2017-10-23
DOI: 10.5594/M001807
ISBN: Print: 978-1-61482-959-1
Link: https://doi.org/10.5594/M001807
Author(s): Raquel Gil RodriguezUniversitat Pompeu Fabra, Barcelona, Spain
Javier Vazquez-CorralUniversitat Pompeu Fabra, Barcelona, Spain
Marcelo BertalmioUniversitat Pompeu Fabra, Barcelona, Spain
Keyword(s): Color Stabilization, Color Matching, Non-linearity Estimation, Logarithmic Encoded Images, Gamma Correction
Copyright: © 2017 Society of Motion Picture and Television Engineers, Inc.

Bibliographic Reference(s)

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Raquel Gil Rodriguez, Javier Vazquez-Corral, and Marcelo Bertalmio; Color-matching Shots from Different Cameras Having Unknown Gamma or Logarithmic Encoding Curves, SMPTE Meetings and Conferences ( October 2017); SMPTE, 2017, ISBN: 978-1-61482-959-1. Available at https://doi.org/10.5594/M001807

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Raquel Gil Rodriguez, Javier Vazquez-Corral, and Marcelo Bertalmio; Color-matching Shots from Different Cameras Having Unknown Gamma or Logarithmic Encoding Curves, SMPTE Meetings and Conferences ( October 2017); SMPTE, 2017, ISBN: 978-1-61482-959-1. Available at https://doi.org/10.5594/M001807

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<span class="citation">Raquel Gil Rodriguez, Javier Vazquez-Corral, and Marcelo Bertalmio; <cite>Color-matching Shots from Different Cameras Having Unknown Gamma or Logarithmic Encoding Curves</cite>, SMPTE Meetings and Conferences ( October 2017); SMPTE, 2017, ISBN: 978-1-61482-959-1. Available at <a href="https://doi.org/10.5594/M001807" target="_blank" rel="noopener">https://doi.org/10.5594/M001807</a></span>

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<li>
Raquel Gil Rodriguez, Javier Vazquez-Corral, and Marcelo Bertalmio; <cite id="bib-10-5594-m001807">Color-matching Shots from Different Cameras Having Unknown Gamma or Logarithmic Encoding Curves</cite>, SMPTE Meetings and Conferences ( October 2017); SMPTE, 2017, ISBN: 978-1-61482-959-1
<span class="doi">10.5594/M001807</span>
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