Automatic, Viewing-Condition Dependent Contrast Grading based on Perceptual Models

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Publisher: SMPTE — White Plains, NY, USA
Doc Type: Conference Paper
Content Type: Original Research
Volume: 2016, No. 10, pp. 1–11
Abstract: Cameras automatically apply non-linear transformations to the sensor data, allowing for perceptually-uniform quantization suited to standard dynamic range displays in dim conditions. In the cinema industry, data is recorded in raw (linear) format and non-linearly corrected in post-production by a skilled technician who optimizes image appearance for cinema (dark) conditions. We propose a method that automatically performs this non-linear transformation taking into account the intended viewing conditions. It is based on visual perception models and produces results that look natural, without any spatio-temporal artifacts. User preference tests show that our method outperforms state of the art approaches. The technique is fast and could be implemented on camera hardware. It can be used for on-set monitoring on regular displays, as a substitute for gamma-correction, and as a way of providing the colorist with content that is both natural looking and has a crisp, clear image.
Publication Date: 2016-10-25
DOI: 10.5594/M001687
ISBN: Print: 978-1-61482-957-7
Link: https://doi.org/10.5594/M001687
Author(s): Praveen CyriacUniversitat Pompeu Fabra, Barcelona, Spain
David KaneUniversitat Pompeu Fabra, Barcelona, Spain
Marcelo BertalmioUniversitat Pompeu Fabra, Barcelona, Spain
Keyword(s): HDR, Tone Mapping, Human Visual System, Psychophysics, Natural Image Statistics, Contrast Grading
Copyright: © 2016 Society of Motion Picture and Television Engineers, Inc.

Bibliographic Reference(s)

An Analysis of System Contrast in Digital Cinema Auditoriums [Active]
Modelling of Achievable Contrast and its Impact on HDR Projection in Commercial Cinema Environments [Active]
S. Andriani, H. Brendel, T. Seybold, and J. Goldstone, “Beyond the kodak image set: A new reference set of color image sequences”, ICIP, pages 2289– 2293, 2013. EXTERNAL
W.E. Vinje, J.L. Gallant, "Sparse coding and decorrelation in primary visual cortex during natural vision", Science. 287 (5456): 1273–1276, 2000. EXTERNAL
P. Cyriac, D. Kane, and M. Bertalmío, “Optimized tone curve for in-camera image processing”, In SPIE/IS&T Image Quality and System Performance, 2016. EXTERNAL
D. L Ruderman, “The statistics of natural images”, Network: computation in neural systems, 5(4):517–548, 1994. EXTERNAL
D. Kane and M. Bertalmío, “System gamma as a function of image- and monitor-dynamic range”, Journal of Vision 16(6):4, 1-13, 2016. EXTERNAL
P. Cyriac, D. Kane, and M. Bertalmío, “Perceptual dynamic range for in-camera image processing”, In BMVC, 2015. EXTERNAL
M. Carandini and D. J Heeger, “Normalization as a canonical neural computation”, Nature Reviews Neuroscience, 13(1):51–62, 2012. EXTERNAL
B. A Olshausen and D. J Field, “Vision and the coding of natural images”, American Scientist, 88(3):238–245, 2000. EXTERNAL
H. Barlow, "Possible principles underlying the transformation of sensory messages", in Sensory Communication, MIT Press, 1961. EXTERNAL
N. Brenner, W. Bialek, and R. V. Steveninck, “Adaptive rescaling maximizes information transmission”, Neuron, 26(3):695–702, 2000. EXTERNAL
S. Ferradans, M. Bertalmío, E. Provenzi, and V. Caselles, “An analysis of visual adaptation and con- trast perception for tone mapping”, IEEE Transactions on Pattern Analysis and Machine Intelligence, 33(10):2002– 2012, 2011. EXTERNAL
J. Froehlich, S. Grandinetti, B. Eberhardt, S. Walter, A. Schilling, and H. Brendel, “Creating cinematic wide gamut hdr video for the evaluation of tone mapping operators and hdr displays” 2014. EXTERNAL
J. Huang and D. Mumford, “Statistics of natural images and models”, In Computer Vision and Pattern Recognition, 1999 volume 1 EXTERNAL
M. D Fairchild, “The hdr photographic survey”, In Color and Imaging Conference, volume 2007, pages 233–238. EXTERNAL
D. Kane and M. Bertalmío, “Is there a preference for linearity when viewing natural images?” In SPIE/IS&T Image Quality and System Performance XX, 2015. EXTERNAL

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Praveen Cyriac, David Kane, and Marcelo Bertalmio; Automatic, Viewing-Condition Dependent Contrast Grading based on Perceptual Models, SMPTE Meetings and Conferences ( October 2016); SMPTE, 2016, ISBN: 978-1-61482-957-7. Available at https://doi.org/10.5594/M001687

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Praveen Cyriac, David Kane, and Marcelo Bertalmio; Automatic, Viewing-Condition Dependent Contrast Grading based on Perceptual Models, SMPTE Meetings and Conferences ( October 2016); SMPTE, 2016, ISBN: 978-1-61482-957-7. Available at https://doi.org/10.5594/M001687

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<span class="citation">Praveen Cyriac, David Kane, and Marcelo Bertalmio; <cite>Automatic, Viewing-Condition Dependent Contrast Grading based on Perceptual Models</cite>, SMPTE Meetings and Conferences ( October 2016); SMPTE, 2016, ISBN: 978-1-61482-957-7. Available at <a href="https://doi.org/10.5594/M001687" target="_blank" rel="noopener">https://doi.org/10.5594/M001687</a></span>

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Praveen Cyriac, David Kane, and Marcelo Bertalmio; <cite id="bib-10-5594-m001687">Automatic, Viewing-Condition Dependent Contrast Grading based on Perceptual Models</cite>, SMPTE Meetings and Conferences ( October 2016); SMPTE, 2016, ISBN: 978-1-61482-957-7
<span class="doi">10.5594/M001687</span>
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