> AFAIK "their choice" here is just the default
Well, the default in avifenc can always be changed. Do keep in mind there's no "one size fits all" implementation, as customers desire different loading tradeoffs. You might be surprised, but during testing (outside HN), we've seen people actually prefer "2 layer" loading.
I'm surprised HN likes progressive loading to be more granular, and use that to push back. I'm wondering if there are generational differences at play? Maybe it's the difference of being used to the blurhash vs. old-school JPEG loading experience.
Anyway, the more expressive mode in avifenc is `--layered` (yes, I know the name is weird).
> Usable for what?
Well, focusing on the "Poke bowl" example: with AVIF you can clearly tell apart each ingredient at 8kB. You can derive enough semantic understanding just from this base layer. Having decent edge preservation helps significantly here.
On the other hand, JXL is just too blurry at 8kB to make sense of the image -- only the egg and carrot are recognizable, maaybe the cucumber? IMO JXL needs the pass at ~28kB to make everything salient, including sprouts and beet. Yes, I know there are subjective effects at play and I'm sure we'll disagree on exact image thresholds, but recognizing objects within an image is so important in real-life use cases.
Well, the default in avifenc can always be changed. Do keep in mind there's no "one size fits all" implementation, as customers desire different loading tradeoffs. You might be surprised, but during testing (outside HN), we've seen people actually prefer "2 layer" loading.
I'm surprised HN likes progressive loading to be more granular, and use that to push back. I'm wondering if there are generational differences at play? Maybe it's the difference of being used to the blurhash vs. old-school JPEG loading experience.
Anyway, the more expressive mode in avifenc is `--layered` (yes, I know the name is weird).
> Usable for what?
Well, focusing on the "Poke bowl" example: with AVIF you can clearly tell apart each ingredient at 8kB. You can derive enough semantic understanding just from this base layer. Having decent edge preservation helps significantly here.
On the other hand, JXL is just too blurry at 8kB to make sense of the image -- only the egg and carrot are recognizable, maaybe the cucumber? IMO JXL needs the pass at ~28kB to make everything salient, including sprouts and beet. Yes, I know there are subjective effects at play and I'm sure we'll disagree on exact image thresholds, but recognizing objects within an image is so important in real-life use cases.