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Old 13th March 2017, 00:30   #81  |  Link
Wilbert
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Here http://avisynth.nl/index.php/Resampl...line_resampler
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Old 13th March 2017, 18:53   #82  |  Link
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Unfortunately, that's not noticeably more legible.
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Old 13th March 2017, 19:17   #83  |  Link
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Unfortunately, that's not noticeably more legible.
Increase the font size in your browser settings.
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Old 13th March 2017, 23:37   #84  |  Link
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A friend once said in relation to an algorithm he was trying to explain, "I can say it again with more words, but I can't make it easier to understand".
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Old 14th March 2017, 02:16   #85  |  Link
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Quote:
Originally Posted by turbojet
I see a noticeable improvement of spline144 over spline100. Would spline196 or even spline256 bring further improvement?
Not necessarily in terms of visual quality; Spline64Resize is fine for almost any sources quality wise. Having more sample points doesn't necessarily mean more visual quality even 'cause, depending on the sources, it may be really barely noticeable.

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Unfortunately, that's not noticeably more legible.
Maybe you could try to tell us what you don't get of that explanation.

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Originally Posted by Groucho2004
Increase the font size in your browser settings.
Rotfl.

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A friend once said in relation to an algorithm he was trying to explain, "I can say it again with more words, but I can't make it easier to understand".
True.
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Old 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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Old 21st March 2017, 03:03   #87  |  Link
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sinc is the most mathematically accurate resampling filter
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Old 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.
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Old 21st March 2017, 12:26   #89  |  Link
luquinhas0021
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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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Old 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.
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Old 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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Old 10th April 2017, 06:17   #92  |  Link
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A friend once said in relation to an algorithm he was trying to explain, "I can say it again with more words, but I can't make it easier to understand".
I don't need more words, I just need less math... unless those words are an explanation of what the variables mean in the math. For example, "s(x) = sum_n s(n*T) * sinc((x-n*T)/T" might make more sense if someone explained what s, sum_n, n, and T were supposed to mean.

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Not necessarily in terms of visual quality; Spline64Resize is fine for almost any sources quality wise. Having more sample points doesn't necessarily mean more visual quality even 'cause, depending on the sources, it may be really barely noticeable.
It's not always even a matter of "barely noticeable". Some algorithms start to look WORSE when the number of taps goes above a certain number. High-tap windowed sinc filters, for example, just add more/worse ringing without increasing sharpness. In the synthetic benchmarks that I ran a few weeks ago, 4-tap Lanczos outperformed both 3-tap and 5-tap, and 3-tap outperformed 5-tap.

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Maybe you could try to tell us what you don't get of that explanation.
If I had to pick one thing to start with, I'd start with an explanation of how a function can be cubic when it uses more than four control points. I read somewhere that any function going through N control points can see X raised to an exponent as high as N-1...

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Is the most accurate when the sample satisfies the Nyquist-Shannon sampling condition.
...which, when dealing with images, is never
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Old 10th April 2017, 21:26   #93  |  Link
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Originally Posted by Katie Boundary View Post
I don't need more words, I just need less math... unless those words are an explanation of what the variables mean in the math. For example, "s(x) = sum_n s(n*T) * sinc((x-n*T)/T" might make more sense if someone explained what s, sum_n, n, and T were supposed to mean.
Did you click and read the wikipedia link that says where the maths come from?

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The symbol T = 1/fs is customarily used to represent the interval between samples and is called the sample period or sampling interval. And the samples of function x(t) are commonly denoted by x[n] = x(nT) (alternatively "xn" in older signal processing literature), for all integer values of n. A mathematically ideal way to interpolate the sequence involves the use of sinc functions. Each sample in the sequence is replaced by a sinc function, centered on the time axis at the original location of the sample, nT, with the amplitude of the sinc function scaled to the sample value, x[n]
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Originally Posted by Katie Boundary View Post
If I had to pick one thing to start with, I'd start with an explanation of how a function can be cubic when it uses more than four control points. I read somewhere that any function going through N control points can see X raised to an exponent as high as N-1...
You probably misunderstood what you read then, because the order of the polynomials and the number of control points have nothing to do with each other. The cubic spline is called cubic because each piece (the curve between one control point and the next) is expressed as a third order polynomial. http://mathworld.wolfram.com/CubicSpline.html

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