[luquinhas0021]

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)?