This takes the 2x2 area of pixels around the point where your ideal sample would be, and calculates a new value based on how close its position is to each of the 4 pixels. By the end of the list you shouldn't be able to tell the difference between resizing to 1499 or 1500. As you get higher in quality, the exact ratio of input size to output size makes less of a difference. Here's a list of common ones, from lowest quality to highest. Thanks for your attention and please excuse the contrived presentation. I ask them here because I've seen related ones with good answers. But I don't even know here to ask these questions. People have devised all sorts of complex algorithms, they must take this somehow in consideration. These issues must have been studied by someone. Where the original size (both X and Y) were not a multiple of the denominator, the results are expected to be poorer, tho I have no proof of that.
Intuitively, 1/n resizes where n is a factor of the original size would yield the best results, followed by n/m where the numbers were the lowest possible.
But what about 3/4? Is it better than 1/2? It certainly can hold more detail, but will part of it not become irretrievably fuzzy? Is there a metric for the 'incurred fuzziness' which can be offset against the actual resolution?įor instance, I suppose such a metric would clearly show 3000 -> 1501 to be worse than 3000 -> 1500, more than is gained by 1501 > 1500. But I have no solid proof, and the reason I'd like to have proof is that it could help me decide less obvious cases.įor instance, reducing it to 1000 px (one third) will also presumably work ok. I suppose that will be so regardless of the algorithm used. I know (at least I think I do) that if I downsize it to be 1500 px wide (that is, 50%), the result will be better than if I resize it to be 1499 or 1501 px wide. Let's say I have an image that is 3000 px wide.
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