9/9/2023 0 Comments Rescale image pythonI've described exactly how this algorithm works in one of my previous articles. The result of this convolution-based sampling is even smoother The drawback of this approach is the increased calculation complexity. A positive side of convolution resampling is the reliable mathematics, which makes this method applicable for both upscaling and downscaling. This algorithm is applied in professional software and many image processing libraries, including ImageMagick and GIMP. It helps you avoid local geometry distortions. Сonvolution-based resampling is a way to calculate output pixels while taking into account as many input pixels as you need. For upscaling, box filter gives a result similar to the nearest neighbor algorithm (which is poor).Box filter works well enough with integer scaling.In some cases, this may lead to moiré, an odd pattern of curvy lines, on the resulting image. “Find the average value” doesn't sound precise, and this isn’t exactly the way digital signal processing works.The result is much better than in the previous case. The result of box sampling is smoother than after using the nearest neighbor technique To do so using the box filter technique, we take 9 pixels of the source image (a box of 3×3 pixels, that's it!) and simply find the average value of them. Imagine that we need to downscale an image three times. For the output image, this algorithm picks the average value for a group of pixels. The most obvious solution to the previous issue is box filter or box sampling. While it’s super fast, the result is far from being perfect, as it usually ends up with jagged edges. Say, when you reduce a picture three times, the algorithm picks just one pixel out of nine neighboring pixels. If you downscale, the algorithm calculates the nearest neighboring pixel in the source image and uses it in the output. If you upscale, it duplicates and reuses some of the pixels from the source image. Nearest neighbor is the fastest and easiest algorithm to implement. Here are the three basic techniques I considered when I was trying to speed up image processing at Uploadcare. There are plenty of algorithms for image resizing. It does resizing, changes colors, converts JPEG into WebP, and more - just like a food processor but for images. Today, Pillow-SIMD is used for most operations with images processed by Uploadcare. Unlike Pillow, it uses the SIMD extensions of x86 processors to speed up the most common operations, such as resizing. We’d been using it for Uploadcare in its early days until I developed the Pillow-SIMD fork. Pillow is an open-source Python imaging library. In this blog post, I’ll tell you about the mistakes you may come across while trying to speed up image resizing, and how I solved these issues in the latest Pillow release and Uploadcare. The faster the process, the better the user experience. This is what happens (hopefully) in milliseconds before a visitor sees the actual webpage. With so many devices on the market, it’s a challenge to make on-site images match each screen size. First published on March 20, 2020, in Insights Everything Insights Engineering News Product Culture Compliance How to accelerate image resizing without screwing up
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