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Old 17th October 2020, 23:20   #1  |  Link
ShortKatz
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What happened to --auto-aq ?

In February there was a nice patch about a new parameter --auto-aq, which automatically decides the AQ Mode (2, 3, 4 or 5) for each frame using its scene statistics.
https://mailman.videolan.org/piperma...ry/012882.html
But this patch never made it into master. Does anybody know more why they did not pursue on that idea?
I quite liked that idea, so I made myself a customized version of HandBrake, that includes this patch. And it gives me nice results, good quality and much smaller files size. So, I don't really understand why this patch never made it into master.
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Old 19th October 2020, 17:23   #2  |  Link
benwaggoner
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Quote:
Originally Posted by ShortKatz View Post
In February there was a nice patch about a new parameter --auto-aq, which automatically decides the AQ Mode (2, 3, 4 or 5) for each frame using its scene statistics.
https://mailman.videolan.org/piperma...ry/012882.html
But this patch never made it into master. Does anybody know more why they did not pursue on that idea?
I quite liked that idea, so I made myself a customized version of HandBrake, that includes this patch. And it gives me nice results, good quality and much smaller files size. So, I don't really understand why this patch never made it into master.
Glad to hear you got some more testing in.

I can imagine it is a difficult feature to avoid thresholding issues with. For example, if the aq-mode changes mid-shot, that could be quite jarring. It likely wouldn't happen much with most content, or with long GOPs such that there aren't many mid-shot IDRs. But with content that varies a lot in style (like mixed CGI/cel/live action) there could be some challenges. And that's exactly the content where changing aq-mode would be most valuable.

With an analysis pass or long lookahead these could be addressed, but it strikes me as a finicky feature.

Plus, does it auto vary --aq-strength too? Because the same value might not be optimal in different modes. That likely would need some auto-adaption as well.

And there simply aren't any objective metrics that do a good job of comparing adaptive quantization modes anyway. Even VMAF gives very similar scores for different approaches, and sometimes penalizes the subjectively preferable mode. I suspect Netflix didn't include a lot of aq-mode variation in their subjective rating database, so VMAF didn't get trained on those variations.

Machine Learning/AI is only as good as the training set, and isn't good at finding types of differences it hasn't been exposed to.
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