What "Stepped Resize" does it divides the difference between the target resolution and the starting resolution by the number of steps and then applies multiple consecutive resizes, which differ by the calculated amount. (additionally it can denoise&sharpen after each step)
Using it with resizers that have a fixed scale factor is possible, but I would not recommend it.
Sure, in theory, I could add an option to all resizers that would take two different resize methods and average them by some weight.
But it's quite a bit of coding involved in that, since it would have to make sure those two resize options use the same color space. (Your 'resize_smooth' has problems there, it only works for p16 content, otherwise it will fail in the merge step, since fmtc.resample always outputs 16bit.) Also, handling of interlaced content would need to be tested and adjusted.
If more users think this is useful, since they would use it, I could look into implementing this, but atm. I am not planning to add support for this.
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As a general side note:
Your resize_smooth also better should take a gray scale mask (values 0.00-1.00) instead of a weight as parameter and use MaskedMerge, since that probably makes more sense than using a constant weight for the whole image.
Cu Selur
Using it with resizers that have a fixed scale factor is possible, but I would not recommend it.
Sure, in theory, I could add an option to all resizers that would take two different resize methods and average them by some weight.
But it's quite a bit of coding involved in that, since it would have to make sure those two resize options use the same color space. (Your 'resize_smooth' has problems there, it only works for p16 content, otherwise it will fail in the merge step, since fmtc.resample always outputs 16bit.) Also, handling of interlaced content would need to be tested and adjusted.
If more users think this is useful, since they would use it, I could look into implementing this, but atm. I am not planning to add support for this.
--
As a general side note:
Your resize_smooth also better should take a gray scale mask (values 0.00-1.00) instead of a weight as parameter and use MaskedMerge, since that probably makes more sense than using a constant weight for the whole image.
Cu Selur
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Dev versions are in the 'experimental'-folder of my GoogleDrive, which is linked on the download page.
Dev versions are in the 'experimental'-folder of my GoogleDrive, which is linked on the download page.