Technology

JXL has been ready for practical use for a while now - the only place where JXL support is still missing is browsers (due to Google's politically-motivated removal from chromium). I'm not sure if anyone has tried using JXL with ML, but it's certainly ready to be tested right now. IMO JXL has been ready since their libJXL 0.7.0 release, which happened September 2022 last year. They're still working towards a 1.0 but every image-related application has built-in support for JXL already and it can more or less be considered ready.

haven’t seen any major downsides, besides less optimal performance for very low resolution images

Just to note here, to be precise AVIF starts (barely) winning at low fidelity ranges, not low resolution. Meaning if you want a blurry mess that looks like this, AVIF will compress slightly better (that's an actual AVIF converted to PNG by the way).

At the risk of sounding like sour grapes, this compression advantage doesn't truly matter. This level of compression is almost never used, and even if it was, even drastic relative filesize savings would ultimately amount to bytes/kilobytes in the grand scheme of all images you're serving. It's more impactful to compress large images simply because they are larger. Smaller images are already small and efficiency deltas in a 1kB vs 1.1kB image are meaningless compared to a 600kB vs 800kB image.

I also wonder if the support for progressive loading might be useful for more efficient, low resolution variants of high resolution models. Just store one set of high res images and load them in progressive steps to make smaller data sets. Like, say you have a bunch of 8k images, but you only want to make a website banner based on the model from those 8k res images. I wonder if it’s possible to use the the progressive loading support to halt reading in the images at 1k

I'm not fully confident on this aspect but I'm pretty sure that JXL supports more than just traditional progressive decoding - you can actually pull "complete" images out of the bitstream from arbitrary ranges. Meaning you could efficiently store a full range of quality options in just one image, then serve them on the fly.

Any time I see a big feature jump, like better file size, I assume the trade off in another feature negates at least half the benefit. It’s pretty rare, from what I’ve seen, to have improvements on all fronts.

JXL is self-described "alien technology from the future", and it was made by a "dream team" of image engineers who have had a hand in just about every image codec and compression technique from our past. It also benefits from being a real image codec, whereas every recent image format that has gained widespread adoption has been derived from a video codec (WebP, AVIF, HEIC).

The only truly useful thing it doesn't perform best-in-class at is animation encoding (losing to AVIF because it's based on the amazing AV1 video codec), and I would honestly recommend just serving AV1 videos instead, and skipping image formats entirely.

A neutral aspect of JXL is that it does worse in single-core decode speed compared to JPEG (which is disgustingly fast), but JXL can be parallelized whereas JPEG cannot. This is ultimately an advantage for JXL for general usecase where users have at least 4 cores available, but for large-scale distributed processing I imagine this property of JPEG may still have an edge use-case?

If you're curious about the technical aspects of JXL, I recommend reading their official slidedeck. The nitty-gritty details start at page 59, but the whole thing is a good read.