The default speed in libavif (-s 6 in avifenc) is fast enough, and it has been used by big CDNs to encode AVIFs for years. You can make the encoder as fast or slow as you'd like, depending on your compute/compression efficiency trade-off requirements.

I've been noticing people in this space react to these news in a very worrisome manner, either by downplaying the need of nsfw in their lives (ironically, hours after discussing a clearly-nsfw matter!), or even worse: by equating all nsfw to "porn"; giving them carte blanche to judge others who want the option for nsfw talk as "being in it just for sex".

It's been shocking for me to see this phenomenon unveil in real time. This overwhelming "sanitizing" force that bulldozes through any nuance regarding the nature of being an adult in shared online adult spaces. It's especially rough for marginalized or minority communities, who oftentimes don't even have IRL spaces to talk about adult subject matters.

BTW, this is no longer true. With the introduction of tune IQ (Image Quality) to libaom and SVT-AV1, AVIF can be competitive with (and oftentimes beat) JXL at the medium to high quality range (up to SSIMULACRA2 85). AVIF is also better than JPEG independently of the quality parameter.

JXL is still better for lossless and very-high quality lossy though (SSIMULACRA2 >90).

This Proof of Concept shows promising results: at 27 characters, AvifHash outperforms BlurHash https://blurha.sh/ (using 4x3 components) in quality and detail retention. At a similar quality, BlurHash needs 54 (5x5) to 76 characters (6x6 components).

Given that AVIF decoding is done by the web engine, AvifHash is very small: the entire demo page (including parsing and re-hydration code) is only 2.3 kB gzipped.

Comments URL: https://news.ycombinator.com/item?id=41513469

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You can check if deltaq is being used in pictures, by opening the AVIFs in aomanalyzer (https://pengbins.github.io/aomanalyzer.io/), and in the Frame Info tab, check that the DeltaQ Res / Present Flag are set.

- `deltaq-mode=2` or `deltaq-mode=3` each enable different flavors of variance adaptive quantization, for a more consistent image quality

- `tune=ssim` enables the SSIM tune, which correlates better with subjective quality

In addition, if you're adventurous, you might want to switch to an alternative encoder that has further tuning for static images. Examples are https://gitlab.com/damian101/aom-psy101 (which notably has a fixed `sharpness` parameter that actually works beyond a value of 1), and most recently, https://svt-av1-psy.com/avif/ with the new "still tune" 4 that reliably beats aomenc in 4:2:0 chroma mode at medium effort speeds.