RED Rushes Resample Filters Comparison

[D'Arcy Foley-Dawson]

I just want to say at the outset: yes, I have tried searching the forum to see if this has already been addressed. So if it has, I would very much appreciate being re-directed.

My question is: has anyone done a comparison test of the various Resample filter algorithms available in Red rushes - eg. Cubic, Mitchell, Gauss, Quadratic etc. etc. - and posted their results ie. some screencaps? Any sort of general, 'in my opinion, this filter is more suited for blah blah blah type footage'....?

Whilst I know many people will have a particular algorithm they always favour, I'm assuming none are necessarily 'better' than another, merely a matter of taste and/or what suits the particular material being transcoded. Otherwise why would Red rushes offer so many different ones? (I'm also assuming it's not because some are faster to transcode than others, because the various debayering options are far more relevant to speed issues).

I know I could also do the comparison myself, and I might when I get the time. But I was just wondering if anyone has done some real comprehensive tests that might be of value to others. Cos that'd be frickin awesome.

[Nicholas Shields]

Great question. I too look forward to a response.

Nick.

[SF Geek]

good luck

[Gunleik Groven]

Not in the way you suggest.

Problem is that is quite a huge task, as I tend to favor different filters based on:
What the footy looks like
Which deliveryformat(s) is/are relevant

Interpolate footagelooks (individual in some extreme cases) with deliveryformats and the choice of filters, you'll end up with a diagram so huge, that it basically goes back to:
Test, test, test and make up your opinion yourself.

Generally, though, I'd say, stay away from sharpening filters if you're going to SD of any kind. I tend to use gauss more and more for those applications. Sharpening should happen at the end of the signal chain (if at all...). Just makes problems on the way there.

But that's just me...

[D'Arcy Foley-Dawson]

Yeah I know it's not something that you could get easy straightforward answers for, but I think some sort of basic comparison could enable each individual to draw their own conclusions and/or guide them in their choices.

If I were able to see a bunch of high res 1920x1080 screencaps from a small sample of source footage: just for example, say a bright outdoors high contrast scene, a low lit indoors scene and a focus test chart, all transcoded with each filter (painstaking, I know); then I might be able to pick say three filters I feel would be best suited to my current project, and then test those three myself with the relevant footage. Or if I decide that I can't see any difference between a set of filters, that could also be valuable knowledge.

Of course it's something that requires trial and error and guesswork, but that doesn't mean we can't be helped along the way by something that takes out a little bit of the guesswork. Do other people feel the same way?

I say this as someone who has just started his first project with Red Rushes, and like many people I must confess I didn't do much research into it beforehand and now I'm working to a tight deadline .Hell, it's the first time I'm doing the down-scaling myself, and whilst I am very picky about my visuals, I don't have much personal experience playing around with different scaling algorithms. Nor do I have the time, cos I gots me a video ta edit, finish and deliver!

[Nook Kim]

I'd appreciate if people can just give their opinions like what filers they normally use and why those filters.

Actually, a short explanation from Graeme would help a lot.

[Florian Stadler]

gauss and Mitchell seem to be a good compromise for a smooth filmic 1080p finish

[Deanan]

gauss and Mitchell seem to be a good compromise for a smooth filmic 1080p finish

Agreed. They are my favorites. However if you prefer sharper, catmul-rom is nice. Lanczos is sharp to but tends to be a bit harder.

On the other hand, alot of film outs are being sharpened these days. I was really turned off at the amount of artificial sharpening on Body of Lies. It was almost like looking at F900 footage at times.

[GlennChan]

Another approach would be to use gaussian to prevent ringing + aliasing, and then enhance perceived sharpness via contrast enhancing tricks. e.g. unsharp mask with larger radii. A S-curve on the RGB channels helps too, but presumably you're already doing that.
(Of course, this is if you have a lot of time to burn. USM may take a while to render... but may be fine if you like to finish in After Effects and are ok with its speed.)

Somewhat related article:
http://www.cambridgeincolour.com/tut.../sharpness.htm

- My prediction of the future: Presumably when Epic rolls around, Red will start looking at higher quality downsampling algorithms (e.g. being adaptive around edges to prevent ringing). Or if they don't, somebody else will (or should).

[Dj Joofa]

I'm also assuming it's not because some are faster to transcode than others

Different flavors of resampling filters have different complexity for performance measure. In hardware implementation some of the computational saving are achieved by moving the downsampler/upsampler through the filtering operation to achieve a configuration what is known as "polyphase filterbank."

Such filters typically have an inherent low-pass operation built-in. However, realtime hardware face another issue, and that is the ideal low pass filter does not exist in nature -- if somebody tells you it does, then politely ask them to go forth and multiply. Software solutions don't face this problem, as "future" data is already there. Hardware solutions counteract that on the expense of delay.

Additionally, many of these filters were conceived as pure resampling filters. They don't discriminate between what is noise and what is signal, beyond what low-pass filtering built into downsampler/upsampler might accidentally remove -- accidentally, because, the low-pass was there not for noise removal, but for adjusting to signal spectrum expansion/contraction after downsampling/upsampling.

As an example, the filter below, is the "best" least square estimator of the "true" signal in the presence of white noise, and an image correlation model that is typical, (using an AR (1) approach.) Note the filter is also low pass, however, it is not yet ready for downsampling after filter operation, as it was not designed for signal decimation (spectrum expansion).



Note that the above filter has a twisted phase response.

- My prediction of the future: Presumably when Epic rolls around, Red will start looking at higher quality downsampling algorithms (e.g. being adaptive around edges to prevent ringing). Or if they don't, somebody else will (or should).

Some more complex solutions exist right now. The future is here.