That's a nice theory, but what if your ML encoder uses textures to encode perceptual equivalence efficiently that's actually got very high errors measured by pi

That's a nice theory, but what if your ML encoder uses textures to encode perceptual equivalence efficiently that's actually got very high errors measured by pixel? It seems to me the best ML-driven encoders would work by inferring higher abstraction details from the image in ways which are hard to get back to lossless - that there's an intrinsic tension there.