Paper
4 February 2009 Unsupervised exposure correction for video
Author Affiliations +
Proceedings Volume 7244, Real-Time Image and Video Processing 2009; 72440N (2009) https://doi.org/10.1117/12.805812
Event: IS&T/SPIE Electronic Imaging, 2009, San Jose, California, United States
Abstract
The paper describes an "off-the-shelf" algorithmic solution for unsupervised exposure correction for video. An important feature of the algorithm is accurate processing not only for natural video sequences, but also for edited, rendered or combined content, including content with letter-boxes or pillar-boxes captured from TV broadcasts. The algorithm allows to change degree of exposure correction smoothly for continuous video scenes and to change it promptly on cuts. Solution includes scene change detection, letter-box detection, pillar-box detection, exposure correction adaptation, exposure correction and color correction. Exposure correction adaptation is based on histogram analysis and soft logics inference. Decision rules are based on relative number of entries in the low tones, mid tones and highlights, maximum entries in the low tones and mid tones, number of non-empty histogram entries and width of the middle range of the histogram. All decision rules have physical meaning, which allows to tune parameters easily for display devices of different classes. Exposure correction consists of computation of local average using edge-preserving filtering, applying local tone mapping and post-processing. At the final stage color correction aiming to reduce color distortions is applied.
© (2009) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
X. Petrova, S. Sedunov, and A. Ignatov "Unsupervised exposure correction for video", Proc. SPIE 7244, Real-Time Image and Video Processing 2009, 72440N (4 February 2009); https://doi.org/10.1117/12.805812
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Cited by 1 scholarly publication.
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KEYWORDS
Video

Video processing

Visualization

Automatic exposure

Detection and tracking algorithms

Linear filtering

Algorithm development

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