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.
