This reminds me of an experiment [1] we run a couple months back. We crawled top 100 Alexa websites and check the bloat in the images served to billions of users.
It's a perceptually lossless optimization and recompression.
We use saliency detection (trained on an eye-tracker) which tells us where the human vision system would look in the images and optimise those fragments (heatmaps) using our own comparison metrics.
If you're interested in the details shoot me an email to przemek [at] optidash [dot] ai
Curious, did your comparison ensure that none of the images lost any detail, etc? Or how much "lossiness" did you introduce to get the 32%?