This is a potential problem in "AI" denoising as well.

These denoising models, the autoencoders more directly so, work by (lossily) mapping the raw input to a very low dimensional representation. The other part generates the desired image back from the low-d representation.

The problem is that nothing, in the vanilla versions, prevent the the low-d version to be a semantics representation such as, Moon, dark hair etc and the generative part to take cues from the semantic representation to a generated sub-image.

The Samsung phone Moon image was likely a result of deliberate choice / company policy, but these things can happen without explicit intent.

I disagree that it's only on a micro scale. If you look at the picture of the parrots it completely changes the black/white pattern in the face of the red parrot and if you look at the picture of the green bicycle where the luggage rack attaches close to the center of the rear wheel, it's completely mangled, in contrast to the more "blurry" picture where you can clearly see the bolts where it's attached also the rods going from the wheel hub up to the luggage rack also looks very jagged and weird whereas they look fine in the blurry one. There are certainly other errors as well but those where the most jarring I Noticed at a quick glance. I don't think a compression algorithm that does this poorly on cherry picked examples are going to fly when you start throwing real pictures at them. If you are going to screw with the ground truth I bet you could get better results by throwing the blurry pictures in one of those "AI" upscalers.