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13th March 2017, 19:17 | #83 | Link |
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Groucho's Avisynth Stuff |
14th March 2017, 02:16 | #85 | Link | ||||
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20th March 2017, 20:10 | #86 | Link |
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Neither spline resize neither Avisynth Bicubic resize are accurate, mathematically, like Bicubic interpolation explained in https://en.wikipedia.org/wiki/Bicubic_interpolation, if I am not wrong, due to, in Bicubic interpolation, to be computed the first and second image derivatives.
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Searching for great solutions Last edited by luquinhas0021; 21st March 2017 at 12:39. |
21st March 2017, 09:17 | #88 | Link |
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This all depends a bit on which task we're talking about. But if it's image upscaling, then let me say:
The problem with linear filters is that they don't treat high-contrast edges any differently than smooth areas. If you downscale a "groundtruth" image, then upscale it again, using linear filters, and if you then use PSSR or SSIM to compare the down+upscaled image to the original image, all linear filters produce *very* bad results. So no offense, but IMHO no linear filter is even remotely mathematically accurate, at least when talking about upscaling. If you want accurate results, you need an algorithm which adapts to high contrast edges. |
21st March 2017, 12:26 | #89 | Link |
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sinc is the most mathematically accurate resampling filter
Is the most accurate when the sample satisfies the Nyquist-Shannon sampling condition. This all depends a bit on which task we're talking about. But if it's image upscaling, then let me say: The problem with linear filters is that they don't treat high-contrast edges any differently than smooth areas. If you downscale a "groundtruth" image, then upscale it again, using linear filters, and if you then use PSSR or SSIM to compare the down+upscaled image to the original image, all linear filters produce *very* bad results. So no offense, but IMHO no linear filter is even remotely mathematically accurate, at least when talking about upscaling. If you want accurate results, you need an algorithm which adapts to high contrast edges. The comparative, by P.S.N.R and S.S.I.M, between an original H.R image and an upscaled downscaled original H.R image only makes sense if the downscaler that was used be good, near perfect. Whatever be the upscaling, the goodest it be, it can not recover an image that was destroyed by a bad quality downscaler. Madshi, "high contrast edges" and "smooth regions" can means the local contrast that a pixel has with it m x n neighborhood, means the high and low frequencies that an image has when is applied to it a frequency domain operator or can it means the pixel derivatives (Central operator)?
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Searching for great solutions Last edited by luquinhas0021; 21st March 2017 at 12:45. |
21st March 2017, 12:55 | #90 | Link |
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What I mean is that when you apply linear resampling "across" a strong edge, linear filtering will either blur & bloat (make fatter) the edge or add ringing artifacts, or both, depending on which linear resampling filter you're using. sinc helps avoiding blur and bloating, but adds an atrocious amount of ringing. Resamplers like spline add much less ringing artifacts, but still some, and add blur & bloat.
My explanation is probably less scientific than you may be looking for. I prefer looking at the practical output of algos, instead of theorizing over frequency domain stuff. |
21st March 2017, 13:04 | #91 | Link |
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Your explanation helped me.
By "linear upscaling operator", do you want mean those operators that are derived from the solving of a set of p x q linear equations (Or it derived discrete form)?
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10th April 2017, 06:17 | #92 | Link | |||
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...which, when dealing with images, is never
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10th April 2017, 21:26 | #93 | Link | |||
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21st September 2022, 17:24 | #94 | Link | |||
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21st September 2022, 22:57 | #95 | Link | |
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21st September 2022, 23:59 | #96 | Link |
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Hmmm. I can't find the quoted passage in that article.
EDIT: I've been able to compare different filter shapes using Desmos graphing calculator, and I'm convinced that 2-tap Lanczos approximates Catrom more closely than any other 2-tap windowed sinc filter (Blackman, Hann, Hamming etc). I'd like to put it up against Spline16 and 2-tap Lagrange, but I have no idea what formulae they use.
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I ask unusual questions but always give proper thanks to those who give correct and useful answers. Last edited by Katie Boundary; 22nd September 2022 at 06:58. |
5th October 2022, 00:15 | #97 | Link |
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I'm running some side-by-size PSNR tests. There's DEFINITELY something wrong with Spline100. It's performing much worse than literally everything except Linear.
EDIT: something is also wrong with Spline144, though it's not as bad as Spline100. Someone needs to triple-check that the code and coefficients are correct. EDIT 2: I did a visual check. Spline100 is much blurrier than Spline64 or Spline144.
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I ask unusual questions but always give proper thanks to those who give correct and useful answers. Last edited by Katie Boundary; 6th October 2022 at 04:38. |
20th June 2023, 15:19 | #98 | Link |
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In retrospect, the fact that Spline36 is sharper than Spline64 should have warned us that something was very wrong with the entire Spline theory/methodology to begin with. Spline100 and Spline144 just made that fact more obvious. I don't think it's a problem with the code being written incorrectly or the coefficients being calculated incorrectly. It's a Spline problem.
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