Cooool! Exactly what I'm looking for. Actually think I've heard of this before (I follow one of the GH contributors) but realized, just now, it's the mirror I am looking for. Thanks!

Luxor, "a Julia package for drawing simple 2D vector graphics", "a high-level easier to use interface to Cairo.jl", is so named because it's Cairo "for tourists!"

https://github.com/JuliaGraphics/Luxor.jl/

brilliant!

Is there a mathematical theory trying to quantify this value?

You might find relative entropy, aka information gain, aka Kullback-Liebler divergence interesting: https://en.m.wikipedia.org/wiki/Kullback–Leibler_divergence

Also this treatment of K-L divergence as a measure of "Bayesian surprise": http://ilab.usc.edu/surprise/

That's all based on Shannon entropy (probabilistic), not Kolmogorov complexity (algorithmic), but there are a lot of connections between them. This paper is a pretty thorough summary: https://homepages.cwi.nl/~paulv/papers/info.pdf

And here's a paper defining algorithmic relative complexity by analogy to relative entropy: http://www.mdpi.com/1099-4300/13/4/902/pdf-vor

"We define the cross-complexity of an object x with respect to another object y as the amount of computational resources needed to specify x in terms of y, and the complexity of x related to y as the compression power which is lost when adopting such a description for x, compared to the shortest representation of x"

Might not be exactly what you're asking, but Landauer's principles that information is physical and necessarily requires some energy to compute is relevant. The 'cost' Bennett is talking about here might be the energy required to change the entropy.

Bennett’s logical depth.