Blind video temporal consistency
Nicolas Bonneel, James Tompkin, Kalyan Sunkavalli, Deqing Sun, Sylvain Paris, Hanspeter Pfister
In ACM Transactions on Graphics (TOG), 34(6), November 2015.
Abstract: Extending image processing techniques to videos is a non-trivial task; applying processing independently to each video frame often leads to temporal inconsistencies, and explicitly encoding temporal consistency requires algorithmic changes. We describe a more general approach to temporal consistency. We propose a gradient-domain technique that is blind to the particular image processing algorithm. Our technique takes a series of processed frames that suffers from flickering and generates a temporally-consistent video sequence. The core of our solution is to infer the temporal regularity from the original unprocessed video, and use it as a temporal consistency guide to stabilize the processed sequence. We formally characterize the frequency properties of our technique, and demonstrate, in practice, its ability to stabilize a wide range of popular image processing techniques including enhancement and stylization of color and tone, intrinsic images, and depth estimation.
@article{10.1145-2816795.2818107,
author = {Nicolas Bonneel and James Tompkin and Kalyan Sunkavalli and Deqing Sun and Sylvain Paris and Hanspeter Pfister},
title = {Blind video temporal consistency},
journal = {ACM Transactions on Graphics (TOG)},
volume = {34},
number = {6},
articleno = {196},
month = nov,
year = {2015},
}
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