Hi - I'm trying to do some zooms and I'm getting a lot of "sparklies" in areas where there's lots of detail (hopefully that descriptor makes sense). Adjusting the anti-aliasing parameters help a bit but the sparklies never really go away. Depth 2 or 3 seems to have the most effect but the render times become extremely long. Threshold of 0 vs. 0.1 vs 0.2 doesn't seem to have much effect. I've also tried changing iterations and that doesn't seem to have any effect.

Are there any tricks to avoiding the "sparklies" effect when doing zooms?

For reference, I get bad sparklies when I try to animate a zoom in the "Green Velvet" example.

Thanks in advance.

Hi - I'm trying to do some zooms and I'm getting a lot of "sparklies" in areas where there's lots of detail (hopefully that descriptor makes sense). Adjusting the anti-aliasing parameters help a bit but the sparklies never really go away. Depth 2 or 3 seems to have the most effect but the render times become extremely long. Threshold of 0 vs. 0.1 vs 0.2 doesn't seem to have much effect. I've also tried changing iterations and that doesn't seem to have any effect. Are there any tricks to avoiding the "sparklies" effect when doing zooms? For reference, I get bad sparklies when I try to animate a zoom in the "Green Velvet" example. Thanks in advance.
 
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From what you describe, this sounds like noise in areas with very high detail. Unfortunately, the only way to get a clean render of very detailed regions is to manually supersample. i.e. render the images larger than needed and then downscaling. I did a detailed comparison of render methods and and downscaling factors here:

https://www.ultrafractal.com/forum/index.php?u=/topic/722/anti-aliasing-fractals

Basically, you select .png as output format and render all frames of your animation individually, then you scale them down to your video resolution and convert them.

I still think UF could - and considering it's commercial, also should - do better here if the samples were to be selected differently. smile

From what you describe, this sounds like noise in areas with very high detail. Unfortunately, the only way to get a clean render of very detailed regions is to manually supersample. i.e. render the images larger than needed and then downscaling. I did a detailed comparison of render methods and and downscaling factors here: https://www.ultrafractal.com/forum/index.php?u=/topic/722/anti-aliasing-fractals Basically, you select .png as output format and render all frames of your animation individually, then you scale them down to your video resolution and convert them. I still think UF could - and considering it's commercial, also should - do better here if the samples were to be selected differently. :)
 
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OK thanks - yes, rendering at higher res and downsizing helps a bunch and is much faster than tweaking the AA. I found that 2x resolution with AA normal gets me about as good as I can get it. I tried 4x res with AA normal and didn't see a difference but render times were about 4x longer.

Cheers

OK thanks - yes, rendering at higher res and downsizing helps a bunch and is much faster than tweaking the AA. I found that 2x resolution with AA normal gets me about as good as I can get it. I tried 4x res with AA normal and didn't see a difference but render times were about 4x longer. Cheers
 
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You can also try a little bit of motion blur when rendering. Phillip, if you have any suggestions on how to improve this behavior without just supersampling (which is what the anti-aliasing feature does), I'm all ears!

You can also try a little bit of motion blur when rendering. Phillip, if you have any suggestions on how to improve this behavior without just supersampling (which is what the anti-aliasing feature does), I'm all ears!

Ultra Fractal author

 
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And in addition, since you posted that (very helpful) topic, I've improved the anti-aliasing behavior in version 6.05, so rendering with AA in UF should now not be slower than rendering at 2x the size and resizing externally. smile

And in addition, since you posted that (very helpful) topic, I've improved the anti-aliasing behavior in version 6.05, so rendering with AA in UF should now not be slower than rendering at 2x the size and resizing externally. :)

Ultra Fractal author

 
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Other than optimizing the locations of samples and temporal anti-aliasing, not really. If you want to discuss it, I'd be happy to do that. There is no magic tool for getting a perfect render, but looking at quality per render time I think picking the sampling locations correctly could help UF quite a bit, especially when it comes to noisy areas and fine lines. UF often refuses to make clean lines for me for some reason.

Feel free to email me if you prefer to discuss it in private. I have been working on a little demo on sampling, it'sPython but the principles are the same, i can share it when it's done.

How does UF pick the samples at the moment, if I may ask?

//edit: I just went over my old post on AA and noticed that the highest custom AA setting increased the render time from 0.04s to 129s, which means one could take a whopping 3000 samples per pixel in that time. Realistcally, even the nastiest regions should look OK at less than a tenth of that and really flawless with just a third, I think I can give some pointers on how to approach this.

Other than optimizing the locations of samples and temporal anti-aliasing, not really. If you want to discuss it, I'd be happy to do that. There is no magic tool for getting a perfect render, but looking at quality per render time I think picking the sampling locations correctly could help UF quite a bit, especially when it comes to noisy areas and fine lines. UF often refuses to make clean lines for me for some reason. Feel free to email me if you prefer to discuss it in private. I have been working on a little demo on sampling, it'sPython but the principles are the same, i can share it when it's done. How does UF pick the samples at the moment, if I may ask? //edit: I just went over my old post on AA and noticed that the highest custom AA setting increased the render time from 0.04s to 129s, which means one could take a whopping 3000 samples per pixel in that time. Realistcally, even the nastiest regions should look OK at less than a tenth of that and really flawless with just a third, I think I can give some pointers on how to approach this.
edited Feb 8 at 6:51 am
 
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Hello again,

I prepared a little jupyter notebook which demonstrates how I would approach AA. Here's a quick comparison for something that is UF's current greatest weakness in my opinion: fine lines

Please make sure to view this in full size! They are a side-to-side comparison of a simple gnarl render at 800x800 pixels (see end of post for parameters).
65cc7db4e8ff6.png

  1. UF normal, took 2.06 seconds
  2. UF custom depth=4, subdivisions=4, took 80.19 seconds
  3. UF custom depth=3, subdivisions=9, took 221.66 seconds
  4. Python code using numpy, 55 samples, took 104.53 seconds

In principle, UF has the option to do depth=4, subdivisions=9, but this takes over 20 minutes and no matter how much better it looks, this is just way too long for the quality it provides. The first 3 images were rendered on my 3900X in parallel, the last one is running on google colab, I don't know which CPU they use, but it is certainly not spread over 12 cores and 24 threads, and it is still done faster than the 3/4 example while showing less aliasing along the lines.

The jupyter notebook also contains examples for julia/mandelbrot code and a really nasty AA-test using cos(r²) on a relatively large part of R². As far as I know, UF is written in Delphi, but the methods demonstrated in the notebook are universal and not specific to python.

Link to the notebook: https://colab.research.google.com/drive/1SV0aZdecIr-D_zxFBFdndPI-v2k3MvH5?usp=sharing

For completeness' sake, here's the same image using higher settings:
65cc8f76899a2.png

  1. UF custom depth=4, subdivisions=9, took 1963.67 seconds
  2. Python code using numpy, 233 samples, took 445.87 seconds

Admittedly, this looks OK from UF's side, but it comes at a huge computational cost and the sharp lines still don't look very clean in some places. Rendering a 4k image this way would mean to let UF render for over 6 hours for a single layer basic gnarl image, which is really costly in my opinion. Note that the method I used produces slightly less sharp images, and sharpness can also be a matter of taste of course.

Running the same method in C++ using OpenMP parallelization on dynamic scheduling (easiest method with a single pragma line) reduces the runtime for 233 samples for the same fractal and resolution down to just 10.2 seconds, 55 samples take about 2.7 seconds.

So the takeaway message is that UF seems to require a huge amount of time to hit a good result, and I think choosing the samples well could help a lot here. Of course this ultimately depends on how many samples UF can churn out per second, but I would expect it to easily beat python. I'd be happy to help, either via this forum or also via mail/calls, if you are interested.

P.S.: Parameters, for anyone who wants to try this:

__simplest_gnarl_demo {
::Jg6Etin21J1yuxNMMw7Gw/DC6DwW2Jb2kUoDNX6lcIHaPbwaTbTE9CSaRz+3HK5kiWUUfiga
  4MD541IMnBzjtNCRmyGULnmSkNYwUeazBRz0Ca9Sxvol8u+elSsj02euWagrYMpHKjPHxFKn
  0yX2JjhCfZsf42+R14tSB4yEYIIRuN9VM12UnsK7MEyk3plPBzvuF9XcLShPAzU+qegFxi5d
  /i2exkpAk4htQIwMdMO6yYUr66VdCLs50jtNr+IDHqAswbUBxJlIgx5dc+VtfdVsSG0BWej/
  WZNTdXWtyyAMhxra5ffFSSRY6nAZKWq+1x8NFGYlHG7r1j6T9qCDc9Nl+slU332QuEtgH34I
  4SrsbMkDh4fYiwO7gZj8T5rm6rmNfR4FkpocOOYf2b8xnYQf3zhn4FIvLeGdb5deB8Xy/P5a
  b2iwCxKcc68WLXzR2PSIHwFWKRLfaFVJKFAbhI/ktTKSWvn/Jw5FkbBfTrOYhRqVf06mHe4j
  OD3d+85xhTsjOCzqm/DF/OqHPxQfHkw2TzO=
}

Gnarls.ufm:simplest_gnarls {
; Original formula otto magus
; simplified to play around with by Phillip S.
::ELS1djn2t9UTrRMIQ07DM/HGSvkkdJkslepUhQ/jsYTshBMqYcBTY/xXNCtXqX03Hz8eiAb4
  w7IcQC6FHHVaEQIxp8JmeE0WrLpHt65EhXJ11HNIsXw8qc5EHTgTPXoR3A1Sbspe0drNbM7P
  pv/f6xieO/YSsu/KN00S7FqjT/v+XjK3XoBE+Sya7jc5fe88jxMEhZ13yH6MZgDalo6+9Ne1
  pVbBaxI96tKEWlx8aEvl+fTKT+Zdf3VqvrJLuYE3Qwp8sdmn4wuI5zJ9yVqEyk0FYrRU9ZpC
  V/mrYovc6SjoMznThwPA+J7ZYA==
}

phs.ucl:GnarlAlgo {
::U9uskin2tW1yuJOMU09Ix/wVwGCVlptLrKSlpa0spLqa7yRakbiTwSO2RJmZo81P+VcCGbTr
  6gEkw1XfO3X+YCjIud6EAKpckAaQitwa4qVKT586GKePcgTLkGn3Q2jpD+K+L/Ji0+NrglqF
  HWhw+zr2FveF8NjnDLXj2/7OBuxRkxcATick1Clzb0xp1rc0bdLmfAuUHdZqVIlDIfn2PlVY
  Md9GxsCSpdP3nzp82v/OseNMTwFIKkvFxqwy0AkPp4ZGcMBj63LAkAxuxjfHo2io+xFOO7ri
  HmOpkwQ0bDx/ra+fStzHxsKxWL3zJsCZnYNcfBm1REvLL5K818S7wOcezgzGVcD8S4HsiEQ4
  aULXIbgjyrssYIPKCXCfdailH26huNLLsy6cnA1Ky8ZZYOwMemdOkihw53okD97JCkes6d1G
  TRx+B3g2mRDaHjZqi3lRLtWijxMM7ZUBZXns8/RiBVKtEW8fIeiWK20WtrGzEqppRNc/wZAf
  wj+hRgUcMAv9PfSWU54xpUMyeE1Wh7EwLE9hkXkyA+00rMFDCKvah6rbEM7kxujcR1DiGPpk
  2cwpQRqwFFEqWUSV3ooOjktPEpSoNenml3NcWFvTOhL3kTywI5WgLR7om7xEEhsaL56nMULF
  eQxusDMb8dctoawqOZIzCg++KLuavMq5K3tZhv7XZMgZ7qhgy3eSxj1hSplGS5D+lhM7nETz
  xFtOVyJhSQACDeEN8se4x3Yzh+cFaQ7TM/M0AnO5fsDoYyL=
}
Hello again, I prepared a little jupyter notebook which demonstrates how I would approach AA. Here's a quick comparison for something that is UF's current greatest weakness in my opinion: fine lines Please make sure to view this in full size! They are a side-to-side comparison of a simple gnarl render at 800x800 pixels (see end of post for parameters). ![65cc7db4e8ff6.png](serve/attachment&path=65cc7db4e8ff6.png) 1. UF normal, took 2.06 seconds 2. UF custom depth=4, subdivisions=4, took 80.19 seconds 3. UF custom depth=3, subdivisions=9, took 221.66 seconds 4. Python code using numpy, 55 samples, took 104.53 seconds In principle, UF has the option to do depth=4, subdivisions=9, but this takes over 20 minutes and no matter how much better it looks, this is just way too long for the quality it provides. The first 3 images were rendered on my 3900X in parallel, the last one is running on google colab, I don't know which CPU they use, but it is certainly not spread over 12 cores and 24 threads, and it is still done faster than the 3/4 example while showing less aliasing along the lines. The jupyter notebook also contains examples for julia/mandelbrot code and a really nasty AA-test using cos(r²) on a relatively large part of R². As far as I know, UF is written in Delphi, but the methods demonstrated in the notebook are universal and not specific to python. Link to the notebook: https://colab.research.google.com/drive/1SV0aZdecIr-D_zxFBFdndPI-v2k3MvH5?usp=sharing For completeness' sake, here's the same image using higher settings: ![65cc8f76899a2.png](serve/attachment&path=65cc8f76899a2.png) 1. UF custom depth=4, subdivisions=9, took 1963.67 seconds 2. Python code using numpy, 233 samples, took 445.87 seconds Admittedly, this looks OK from UF's side, but it comes at a huge computational cost and the sharp lines still don't look very clean in some places. Rendering a 4k image this way would mean to let UF render for over 6 hours for a single layer basic gnarl image, which is really costly in my opinion. Note that the method I used produces slightly less sharp images, and sharpness can also be a matter of taste of course. Running the same method in C++ using OpenMP parallelization on dynamic scheduling (easiest method with a single pragma line) reduces the runtime for 233 samples for the same fractal and resolution down to just 10.2 seconds, 55 samples take about 2.7 seconds. So the takeaway message is that UF seems to require a huge amount of time to hit a good result, and I think choosing the samples well could help a lot here. Of course this ultimately depends on how many samples UF can churn out per second, but I would expect it to easily beat python. I'd be happy to help, either via this forum or also via mail/calls, if you are interested. P.S.: Parameters, for anyone who wants to try this: ```` __simplest_gnarl_demo { ::Jg6Etin21J1yuxNMMw7Gw/DC6DwW2Jb2kUoDNX6lcIHaPbwaTbTE9CSaRz+3HK5kiWUUfiga 4MD541IMnBzjtNCRmyGULnmSkNYwUeazBRz0Ca9Sxvol8u+elSsj02euWagrYMpHKjPHxFKn 0yX2JjhCfZsf42+R14tSB4yEYIIRuN9VM12UnsK7MEyk3plPBzvuF9XcLShPAzU+qegFxi5d /i2exkpAk4htQIwMdMO6yYUr66VdCLs50jtNr+IDHqAswbUBxJlIgx5dc+VtfdVsSG0BWej/ WZNTdXWtyyAMhxra5ffFSSRY6nAZKWq+1x8NFGYlHG7r1j6T9qCDc9Nl+slU332QuEtgH34I 4SrsbMkDh4fYiwO7gZj8T5rm6rmNfR4FkpocOOYf2b8xnYQf3zhn4FIvLeGdb5deB8Xy/P5a b2iwCxKcc68WLXzR2PSIHwFWKRLfaFVJKFAbhI/ktTKSWvn/Jw5FkbBfTrOYhRqVf06mHe4j OD3d+85xhTsjOCzqm/DF/OqHPxQfHkw2TzO= } Gnarls.ufm:simplest_gnarls { ; Original formula otto magus ; simplified to play around with by Phillip S. ::ELS1djn2t9UTrRMIQ07DM/HGSvkkdJkslepUhQ/jsYTshBMqYcBTY/xXNCtXqX03Hz8eiAb4 w7IcQC6FHHVaEQIxp8JmeE0WrLpHt65EhXJ11HNIsXw8qc5EHTgTPXoR3A1Sbspe0drNbM7P pv/f6xieO/YSsu/KN00S7FqjT/v+XjK3XoBE+Sya7jc5fe88jxMEhZ13yH6MZgDalo6+9Ne1 pVbBaxI96tKEWlx8aEvl+fTKT+Zdf3VqvrJLuYE3Qwp8sdmn4wuI5zJ9yVqEyk0FYrRU9ZpC V/mrYovc6SjoMznThwPA+J7ZYA== } phs.ucl:GnarlAlgo { ::U9uskin2tW1yuJOMU09Ix/wVwGCVlptLrKSlpa0spLqa7yRakbiTwSO2RJmZo81P+VcCGbTr 6gEkw1XfO3X+YCjIud6EAKpckAaQitwa4qVKT586GKePcgTLkGn3Q2jpD+K+L/Ji0+NrglqF HWhw+zr2FveF8NjnDLXj2/7OBuxRkxcATick1Clzb0xp1rc0bdLmfAuUHdZqVIlDIfn2PlVY Md9GxsCSpdP3nzp82v/OseNMTwFIKkvFxqwy0AkPp4ZGcMBj63LAkAxuxjfHo2io+xFOO7ri HmOpkwQ0bDx/ra+fStzHxsKxWL3zJsCZnYNcfBm1REvLL5K818S7wOcezgzGVcD8S4HsiEQ4 aULXIbgjyrssYIPKCXCfdailH26huNLLsy6cnA1Ky8ZZYOwMemdOkihw53okD97JCkes6d1G TRx+B3g2mRDaHjZqi3lRLtWijxMM7ZUBZXns8/RiBVKtEW8fIeiWK20WtrGzEqppRNc/wZAf wj+hRgUcMAv9PfSWU54xpUMyeE1Wh7EwLE9hkXkyA+00rMFDCKvah6rbEM7kxujcR1DiGPpk 2cwpQRqwFFEqWUSV3ooOjktPEpSoNenml3NcWFvTOhL3kTywI5WgLR7om7xEEhsaL56nMULF eQxusDMb8dctoawqOZIzCg++KLuavMq5K3tZhv7XZMgZ7qhgy3eSxj1hSplGS5D+lhM7nETz xFtOVyJhSQACDeEN8se4x3Yzh+cFaQ7TM/M0AnO5fsDoYyL= } ````
 
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