  Colorization is a process that converts a grayscale image into a color one
that looks as natural as possible. Over the years this task has received a lot
of attention. Existing colorization methods rely on different color spaces:
RGB, YUV, Lab, etc. In this chapter, we aim to study their influence on the
results obtained by training a deep neural network, to answer the question: "Is
it crucial to correctly choose the right color space in deep-learning based
colorization?". First, we briefly summarize the literature and, in particular,
deep learning-based methods. We then compare the results obtained with the same
deep neural network architecture with RGB, YUV and Lab color spaces.
Qualitative and quantitative analysis do not conclude similarly on which color
space is better. We then show the importance of carefully designing the
architecture and evaluation protocols depending on the types of images that are
being processed and their specificities: strong/small contours, few/many
objects, recent/archive images.
