  Automatic colourisation of grey-scale images is the process of creating a
full-colour image from the grey-scale prior. It is an ill-posed problem, as
there are many plausible colourisations for a given grey-scale prior. The
current SOTA in auto-colourisation involves image-to-image type Deep
Convolutional Neural Networks with Generative Adversarial Networks showing the
greatest promise. The end goal of colourisation is to produce full colour
images that appear plausible to the human viewer, but human assessment is
costly and time consuming. This work assesses how well commonly used objective
measures correlate with human opinion. We also attempt to determine what facets
of colourisation have the most significant effect on human opinion. For each of
20 images from the BSD dataset, we create 65 recolourisations made up of local
and global changes. Opinion scores are then crowd sourced using the Amazon
Mechanical Turk and together with the images this forms an extensible dataset
called the Human Evaluated Colourisation Dataset (HECD). While we find
statistically significant correlations between human-opinion scores and a small
number of objective measures, the strength of the correlations is low. There is
also evidence that human observers are most intolerant to an incorrect hue of
naturally occurring objects.
