But what if it’s not fine? Even back in 1996, before a single component of the ISS was launched into orbit, NASA foresaw the possibility of an even worse worst-case scenario: an uncontrolled reentry. The crux of this scenario involves multiple systems failing in an improbable but not completely impossible cascade. Cabin depressurization could damage the avionics. The electrical power system could go offline, along with thermal control and data handling. Without these, systems controlling coolant and even propellant could break down. Unmoored, the ISS would edge slowly toward Earth, maybe over a year or two, with no way to control where it is headed or where its debris might land. And no, we could not save ourselves by blowing the station up. This would be extremely dangerous and almost certainly create an enormous amount of space trash—which is how we got into this hypothetical mess in the first place.
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This approach shares a lot in common with the idea of multivariate interpolation over scattered data. Multivariate interpolation attempts to estimate values at unknown points within an existing data set and is often used in fields such as geostatistics or for geophysical analysis like elevation modelling. We can think of our colour palette as the set of variables we want to interpolate from, and our input colour as the unknown we’re trying to estimate. We can borrow some ideas from multivariate interpolation to develop more effective dithering algorithms.