  In this work we focus on the problem of colorization for image compression.
Since color information occupies a large proportion of the total storage size
of an image, a method that can predict accurate color from its grayscale
version can produce dramatic reduction in image file size. But colorization for
compression poses several challenges. First, while colorization for artistic
purposes simply involves predicting plausible chroma, colorization for
compression requires generating output colors that are as close as possible to
the ground truth. Second, many objects in the real world exhibit multiple
possible colors. Thus, to disambiguate the colorization problem some additional
information must be stored to reproduce the true colors with good accuracy. To
account for the multimodal color distribution of objects we propose a deep
tree-structured network that generates multiple color hypotheses for every
pixel from a grayscale picture (as opposed to a single color produced by most
prior colorization approaches). We show how to leverage the multimodal output
of our model to reproduce with high fidelity the true colors of an image by
storing very little additional information. In the experiments we show that our
proposed method outperforms traditional JPEG color coding by a large margin,
producing colors that are nearly indistinguishable from the ground truth at the
storage cost of just a few hundred bytes for high-resolution pictures!
