  Video colorization is a challenging and highly ill-posed problem. Although
recent years have witnessed remarkable progress in single image colorization,
there is relatively less research effort on video colorization and existing
methods always suffer from severe flickering artifacts (temporal inconsistency)
or unsatisfying colorization performance. We address this problem from a new
perspective, by jointly considering colorization and temporal consistency in a
unified framework. Specifically, we propose a novel temporally consistent video
colorization framework (TCVC). TCVC effectively propagates frame-level deep
features in a bidirectional way to enhance the temporal consistency of
colorization. Furthermore, TCVC introduces a self-regularization learning (SRL)
scheme to minimize the prediction difference obtained with different time
steps. SRL does not require any ground-truth color videos for training and can
further improve temporal consistency. Experiments demonstrate that our method
can not only obtain visually pleasing colorized video, but also achieve clearly
better temporal consistency than state-of-the-art methods.
