Estimation of scribble placement for painting colorization

Abstract

Image colorization has been a topic of interest since the mid 70's and several algorithms have been proposed that given a grayscale image and color scribbles (hints) produce a colorized image. Recently, this approach has been introduced in the field of art conservation and cultural heritage, where B&W photographs of paintings at previous stages have been colorized. However, the questions of what is the minimum number of scribbles necessary and where they should be placed in an image remain unexplored. Here we address this limitation using an iterative algorithm that provides insights as to the relationship between locally vs. globally important scribbles. Given a color image we randomly select scribbles and we attempt to color the grayscale version of the original. We define a scribble contribution measure based on the reconstruction error. We demonstrate our approach using a widely used colorization algorithm and images from a Picasso painting and the peppers test image. We show that areas isolated by thick brushstrokes or areas with high textural variation are locally important but contribute very little to the overall representation accuracy. We also find that for the case of Picasso on average 10% of scribble coverage is enough and that flat areas can be presented by few scribbles. The proposed method can be used verbatim to test any colorization algorithm.