Video encoding requires using your eyes

You can see some ringing in the sky around the trees and on the line between the crow's beak/feathers if you look closely. (Alvin's?) fur goes much farther down his forehead as well. People who work deeply with codecs are usually hypersensitive to these sorts of issues that mere mortals like us need to try to see.

There used to be a legendary blog called "Diaries of an x264 developer" by Fiona Glaser [0] where she'd go on long rants about various ways to cheat in encoder comparisons [1], much like this.

[0] https://web.archive.org/web/2012/http://x264dev.multimedia.c...

[1] https://web.archive.org/web/20130215095527/http://x264dev.mu...

It’s mostly only visible in the closeup of the kid. There are hairs that have been unnecessarily accentuated, and his eye and eyebrow outline look hyper-sharpened, with rough edges.

The non-zoomed image looks fine to me, and I (to some extent) know what I’m looking for. Some private torrent trackers that pride themselves on having transparent encodes will look for this kind of stuff; you have to do multiple test encodes tweaking various parameters to ffmpeg, agonizing over A/B screencaps, only to inevitably be told you either missed some minuscule detail in a single scene, or that your encode is bloated.

As someone who works a lot with sound I notice a ton of artifacts most people don't recognize. Often those artifacts are harder to hear on cheap speakers but become far more obvious with a good setup. But they also add up and while untrained ears can't hear specific examples, they do result in an overall worse experience which is especially frustrating for high end users when they paid a lot for nice speakers and it's just revealing the grunge that was always there.

That's all to say - I also could not find all the things they are talking about, probably a combination of not being trained, not working a lot with video codecs, and not having the best monitors - but I get the authors frustration, and I'm glad there's people who care about these things! But yeah, I hunted for that color shift and just not seeing it...

The ringing is most obvious in the striped shirt in the painting on the wall. It's added entire new stripes that don't exist in the original.

I don't immediately see the issue either.

Though, even if I could, this is a new way to preprocess an image before feeding it into an encoder, and the examples have both been fed through the new downsizer, then the standard encoder, presumably at the standard Netflix bitrates and then (I think) upscaled back to the original size.

So if this didn't look a little compressed then that would be a methodological mistake, as you don't use downscaled encoding unless you've already decided that a full size encode has too much quality for your task.

And Netflix generally has incentives set up to reduce quality until their customers notice. That's why they quote stats based on that.

In web dev terms it's like reducing the size of your product images until it hurts sales. You're almost guaranteed to have artifacts visible to image compression experts before you hit the point that it affects your bottom line. If you are targeting customers on slow internet (and again if you are downscaling then you basically are) your sales are likely to initially rise as you get usable pictures to people faster.

the fact that it looks different is the problem.

Ringing is pretty obvious to me. It has a specific meaning in this context, it means edges are over-sharpened to the point that "fake" extra edges appear.

https://en.wikipedia.org/wiki/Ringing_artifacts

It's not really possible to say what's "best" because the criteria is super subjective.

I personally like the Spline family, and I default to Spline36 for both upscaling and downscaling in ffmpeg. Most people can't tell the difference between Spline36 and Lanczos3. If you want more sharpness, go for Spline64, for less sharpness, try Spline16.

Edit: As far as I'm aware though OpenCV doesn't have Spline as an option for resizing.

The thing is there's not much nuance to be had. Netflix's approach is just worse than existing best approaches on all axes.

Is “area” just a box filter, which is what it sounds like? If so, it gives really blurry results; hardly great.

In short: no, it shouldn't be.

The Netflix post is sort of bizarre. They claim to be optimizing for minimum mean-squared error (MSE) given a conventional (bicubic) upscaling process [0], but... that should have an analytic closed-form solution, as this post states? You definitely do not need multiple layers of neural networks to achieve it. Then they present VMAF results, but VMAF is very much not equivalent to MSE, so you have no idea if they even improved the metric they optimized for. Subjective results are similarly unpersuasive: it isn't clear if "the deep downscaler was preferred by 77% of test subjects" means they thought it was closer to the original or simply "better" than Lanczos[1]. Netflix may not care: longer watch times are longer watch times. But as an engineer you might want to know if that is because you actually achieved the thing you were optimizing for, or it is due to an artifact of the process that might go away the next time you change something to actually improve what you were optimizing for. You can famously make people prefer one audio track over another by making it louder, and video has similar things around sharpness and contrast (and now, thanks to ML, hallucinated detail).

I agree that you can do better than Lanczos for large downscale factors [2]: you need to do something area-based, like you suggest (I have not looked at OpenCV's implementation, but it could be fine). The biggest thing to get wrong is handling gamma incorrectly, but the right thing to do depends on whether you intend to display the result at the downscaled size or upscaled back to the original size as seems to be assumed here (and whether or not your upscaler is gamma-aware, which it probably isn't).

As an aside to those struggling to see the visual differences, make sure you are looking at the image in its original (1874x1596) resolution: https://redvice.org/assets/images/netflix-downscaler-compare... (or right-click, View Image on the original page). Otherwise you also have your browser's resampling algorithm in the mix. To my eyes on my display there is also a pretty big color shift in the featureless pink background of the painting on the right wall, but when I look at the actual pixel values, that appears to be an optical illusion. Subjective comparison is hard!

[0] Unlike the post, I think this is reasonable. In the past, we did experiments that showed that optimizing for bilinear when nearest-neighbor is used (for chroma) is worse than optimizing for nearest-neighbor when bilinear is used. I suspect something similar will be true for bicubic and bilinear, but these days it may be safe to assume that you will get at least bicubic upscaling (for luma), because bilinear luma looks really bad. I haven't done a recent survey of actual playack devices, though.

[1] It's also not how you report subjective test results: what was the statistical significance? There are standard protocols for these kinds of tests and it would have been helpful to cite which one was used.

[2] Nobody ever says what downscaling factors are being tested here. The example graph shows 1080p to 342p, or ~3.16x, but Netflix goes as low as 144p (from, e.g, 2160p), so they can get pretty large (15x) in practice. A 6-tap filter is not going to cut it.

I am unsurprised that the author would hold a video distribution company which supposedly pays good money for experts to a higher standard than the average hobbyist blogger. I don't think it's hating for the sake of hating.

When will people finally learn that people do not know what they want. You, the expert has to know what works and how and whats the best way to use it properly.

And do not rely on user feedback. Again. People don't know whats wrong, they don't even know what's right. They just feel that something is off. And only a very small percentage of users would write an email and even fewer would get through the automated AI bullshit or the snotty person who would dare to hand you a HOW TO USE NETFLIX pdf when you want to report something.