But that is correct and why any proper study should first train the participants on the type of artifacts and trade-offs. Like the reference to JXL choices: think its creator mentioned that AVIF might look nicer for faces because it was "smoothing" them but it was a worse representation of the original. So the study participants would need to be conscious of that trade off and be clear in terms of what they're asked to evaluate - whether the result looks nicer or whether it represents the original better.
Perhaps we can use clever experiment design to address it, like doing a "full-reference" evaluation (e.g., pick most similar two images, out of encoder A, encoder B, and original) rather than strictly A/B.
Maybe the first step to designing better encoders is to build an extremely accurate visuoperceptual error metric. Seems like ML's rapid progress haven't caught onto it just yet.
Seems like others were curious too, given the Wikipedia page on Guetzli mentions:
> Butteraugli. ... How the hundreds of parameters that model the properties of the human visual system were derived remains unexplained.
My rough understanding is its multi-scale convolution architecture was inspired by biological visual perception (perhaps how the layers of visual cortex gradually represent higher level features, ish), and involved some sort of empirical parameter tuning based on human evaluations. It'd also be a very nice resource for beginners wanting to learn more about image/video codecs (like me :)