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Even with slight compression you will not be able to see the difference in qualityĪs a summary - stop worrying about image exact size. JPEG compression will harm you image much more than the difference in visual quality between 1500 pix image and 1499.So if you downscale it from 3000 width to 1499, then you will not be able to choose an integer for image height to keep the original aspect ratio. The last one is obvious, but I will mention it: try to keep the original aspect ratio when you resize the image.So if you want to print it on 10 inch paper, leave it at least 3000 pixels. If you intend to print your image, better have a high resolution.
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Otherwise the display process will make another resampling and you will not be able to observe the true beauty of your image. If you display your image on a computer screen, try to match its size to the size of your screen.Though quality will not improve, your browsing experience will be better. The reason is that some hardware deal faster with images with size divisible by 4.
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For example 1501 is a bad size, 1500 or 1504 is better. Friendly advice: keep the size of your image (both height and width) divisible by 4.Each time you resize the image, some information is lost Resizing an image from 3000 to 2000 and than to 1500 will produce slightly worse result than direct resize from 3000 to 1500. Re-scaling from 3000 pix to 1600 will give you visually better results than re-scaling to 1500. The larger the output image size the better results you will get but the file will take more megabytes. Less important factor is round division.Some are better for natural images, other for computer graphic generated image If you are using photoshop or other advanced resizing tool you may chose the algorithm. Unfortunately there is no best algorithm. For example, bi-cubic interpolation will not work well if you re-size by factor > 2 and do not apply smoothing. Most important factor is choosing a good re-sizing algorithm.You may also notice the difference between theory and practice when you see ringing artifacts in the output.
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This is theoretically perfect, but it requires processing a large chunk of input for every pixel output so it's very slow. The size and speed of the filter varies with the resizing ratio. This takes a number of pixels from the input and runs them through a modified version of Sinc that attempts to retain as much of the detail as possible while keeping the calculations tractable. If this isn't done with an integer multiple of input to output you'll be averaging different amounts each time and the results will be uneven. Similar to Bilinear but using a 3x3 area with a more complex formula to get sharper results. It doesn't work well if you're reducing below 2:1 because it starts to resemble nearest neighbor. 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.