Problems with image resizing is a much deeper rabbit hole than this. Some important talking points:
1. The form of interpolation (this article).
2. The colorspace used for doing the arithmetic for interpolation. You most likely want a linear colorspace here.
3. Clipping. Resizing is typically done in two phases, once resizing in x then in y direction, not necessarily in this order. If the kernel used has values outside of the range [0, 1] (like Lanczos) and for intermediate results you only capture the range [0,1], then you might get clipping in the intermediate image, which can cause artifacts.
4. Quantization and dithering.
5. If you have an alpha channel, using pre-multiplied alpha for interpolation arithmetic.
I'm not trying to be exhaustive here. ImageWorsener's page has a nice reading list[1].
[1] https://entropymine.com/imageworsener/
1. The form of interpolation (this article).
2. The colorspace used for doing the arithmetic for interpolation. You most likely want a linear colorspace here.
3. Clipping. Resizing is typically done in two phases, once resizing in x then in y direction, not necessarily in this order. If the kernel used has values outside of the range [0, 1] (like Lanczos) and for intermediate results you only capture the range [0,1], then you might get clipping in the intermediate image, which can cause artifacts.
4. Quantization and dithering.
5. If you have an alpha channel, using pre-multiplied alpha for interpolation arithmetic.
I'm not trying to be exhaustive here. ImageWorsener's page has a nice reading list[1].
[1] https://entropymine.com/imageworsener/