AI models are a form of compression.

Neural compression wouldn't be like HVEC, operating on frames and pixels. Rather, these techniques can encode entire features and optical flow, which can explain the larger discrepancies. Larger fingers, slightly misplaced items, etc.

Neural compression techniques reshape the image itself.

If you've ever input an image into `gpt-image-1` and asked it to output it again, you'll notice that it's 95% similar, but entire features might move around or average out with the concept of what those items are.