  Graphics Interchange Format (GIF) is a highly portable graphics format that
is ubiquitous on the Internet. Despite their small sizes, GIF images often
contain undesirable visual artifacts such as flat color regions, false
contours, color shift, and dotted patterns. In this paper, we propose
GIF2Video, the first learning-based method for enhancing the visual quality of
GIFs in the wild. We focus on the challenging task of GIF restoration by
recovering information lost in the three steps of GIF creation: frame sampling,
color quantization, and color dithering. We first propose a novel CNN
architecture for color dequantization. It is built upon a compositional
architecture for multi-step color correction, with a comprehensive loss
function designed to handle large quantization errors. We then adapt the
SuperSlomo network for temporal interpolation of GIF frames. We introduce two
large datasets, namely GIF-Faces and GIF-Moments, for both training and
evaluation. Experimental results show that our method can significantly improve
the visual quality of GIFs, and outperforms direct baseline and
state-of-the-art approaches.
