Abstract
Video colorization is the process of assigning realistic, plausible colors to a grayscale video. Compared to its peer, image colorization, video colorization is a relatively unexplored area in computer vision. Most of the models available for video colorization are extensions of image colorization, and hence are unable to address some unique issues in video domain. In this paper, we evaluate the applicability of image colorization techniques for video colorization, identifying problems inherent to videos and attributes affecting them. We develop a dataset and benchmark to measure the effect of such attributes to video colorization quality and demonstrate how our benchmark aligns with human evaluations.
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