> the nuance that these colorizations are interpretations gets lost immediately.
True for all AI; neural networks are doing amazing things, but the output is a synthesis, it's a complex interpolation of it's training inputs that may seem "good" or reliable, but it is never to be taken as truth or fact, and it can be arbitrarily wrong with unbounded errors.
> I'm also a little confused as to why colorization always aim to restore color to the equivalent of a faded color negative, with muted tonality and grain. Human logic is funny.
This isn't a human logic problem. Normally colorizations don't affect tonality and grain much, they are putting color splashes on top of a B/W image. This is true of hand-painted colorization, as well as the digital colorization here. You can't get rid of grain or adjust tone by adding color.
One can adjust tone and grain, but then you're doing more than colorizing, and going even further down the road of "interpretation" you're concerned about.
In this particular case, the author did mention "A more diverse dataset makes the pictures brownish". Brown is the average color in natural photos, so minimizing error tends to make things browish. That is separate from leaving faded tone & grain in tact, but it's a second reason why AI based colorization will tend toward muted color.
True for all AI; neural networks are doing amazing things, but the output is a synthesis, it's a complex interpolation of it's training inputs that may seem "good" or reliable, but it is never to be taken as truth or fact, and it can be arbitrarily wrong with unbounded errors.
> I'm also a little confused as to why colorization always aim to restore color to the equivalent of a faded color negative, with muted tonality and grain. Human logic is funny.
This isn't a human logic problem. Normally colorizations don't affect tonality and grain much, they are putting color splashes on top of a B/W image. This is true of hand-painted colorization, as well as the digital colorization here. You can't get rid of grain or adjust tone by adding color.
One can adjust tone and grain, but then you're doing more than colorizing, and going even further down the road of "interpretation" you're concerned about.
In this particular case, the author did mention "A more diverse dataset makes the pictures brownish". Brown is the average color in natural photos, so minimizing error tends to make things browish. That is separate from leaving faded tone & grain in tact, but it's a second reason why AI based colorization will tend toward muted color.