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252 points CharlesW | 2 comments | | HN request time: 0.011s | source
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crazygringo ◴[] No.44459098[source]
This fails to acknowledge that synthesized noise can lack the detail and information in the original noise.

When you watch a high-quality encode that includes the actual noise, there is a startling increase in resolution from seeing a still to seeing the video. The noise is effectively dancing over a signal, and at 24 fps the signal is still perfectly clear behind it.

Whereas if you lossily encode a still that discards the noise and then adds back artificial noise to match the original "aesthetically", the original detail is non-recoverable if this is done frame-by-frame. Watching at 24 fps produces a fundamentally blurrier viewing experience. And it's not subtle -- on old noisy movies the difference in detail can be 2x.

Now, if h.265 or AV1 is actually building its "noise-removed" frames by always taking into account several preceding and following frames while accounting for movement, it could in theory discover the signal of the full detail across time and encode that, and there wouldn't be any loss in detail. But I don't think it does? I'd love to know if I'm mistaken.

But basically, the point is: comparing noise removal and synthesis can't be done using still images. You have to see an actual video comparison side-by-side to determine if detail is being thrown away or preserved. Noise isn't just noise -- noise is detail too.

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kderbe ◴[] No.44459330[source]
Grain is independent frame-to-frame. It doesn't move with the objects in the scene (unless the video's already been encoded strangely). So long as the synthesized noise doesn't have an obvious temporal pattern, comparing stills should be fine.

Regarding aesthetics, I don't think AV1 synthesized grain takes into account the size of the grains in the source video, so chunky grain from an old film source, with its big silver halide crystals, will appear as fine grain in the synthesis, which looks wrong (this might be mitigated by a good film denoiser). It also doesn't model film's separate color components properly, but supposedly that doesn't matter because Netflix's video sources are often chroma subsampled to begin with: https://norkin.org/pdf/DCC_2018_AV1_film_grain.pdf

Disclaimer: I just read about this stuff casually so I could be wrong.

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godelski ◴[] No.44460409[source]
People often assume noise is normal and IID but it usually isn't. It's s fine approximation but isn't the same thing, which is what the parent is discussing.

Here's an example that might help you intuit why this is true.

Let's suppose you have a digital camera and walk towards a radiation source and then away. Each radioactive particle that hits the CCD causes it to over saturate, creating visible noise in the image. The noise it introduces is random (Poisson) but your movement isn't.

Now think about how noise is introduced. There's a lot of ways actually, but I'm sure this thought exercise will reveal to you how some cause noise across frames to be dependent. Maybe as a first thought, think about from sitting on a shelf degrading.

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1. notpushkin ◴[] No.44460676{3}[source]
I think this is geared towards film grain noise, which is independent from movement?
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2. godelski ◴[] No.44461826[source]
It's the same thing. Yes, not related to the movement of the camera, but I thought that would be easier to build your intuition about silver particles being deposited onto film. You make in batches, right?

The point is that just because things are random doesn't mean there aren't biases.

To get much more accurate, it helps to understand what randomness actually is. It is a measurement of uncertainty. A measurement of the unknown. This is even true for quantum processes that are truly random. That means we can't know. But just because we can't know doesn't mean it's completely unknown, right? We have different types of distributions and different parameters in those distributions. That's what we're trying to build intuition about