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  2. Wikipedia:Autosizing images - Wikipedia

    en.wikipedia.org/wiki/Wikipedia:Autosizing_images

    Within an image link, the options are as follows: thumb – displays the image as a framed thumbnail at the user's default size; frameless – displays the image as an unframed at the user's default size; upright – scales the image to approximately 75% of the user's default size (25% smaller)

  3. Comparison gallery of image scaling algorithms - Wikipedia

    en.wikipedia.org/wiki/Comparison_gallery_of...

    The resulting image is larger than the original, and preserves all the original detail, but has (possibly undesirable) jaggedness. The diagonal lines of the "W", for example, now show the "stairway" shape characteristic of nearest-neighbor interpolation. Other scaling methods below are better at preserving smooth contours in the image.

  4. Image scaling - Wikipedia

    en.wikipedia.org/wiki/Image_scaling

    Image scaling can be interpreted as a form of image resampling or image reconstruction from the view of the Nyquist sampling theorem.According to the theorem, downsampling to a smaller image from a higher-resolution original can only be carried out after applying a suitable 2D anti-aliasing filter to prevent aliasing artifacts.

  5. Wikipedia:Extended image syntax - Wikipedia

    en.wikipedia.org/wiki/Wikipedia:Extended_image...

    Scale the image to be no greater than the given width or height, keeping its aspect ratio. Scaling up (i.e. stretching the image to a greater size) is disabled when the image is framed. Link Link the image to a different resource, or to nothing. Alt Specify the alt text for the image. This is intended for visually impaired readers.

  6. Seam carving - Wikipedia

    en.wikipedia.org/wiki/Seam_carving

    Original image to be made narrower Scaling is undesirable because the castle is distorted. Cropping is undesirable because part of the castle is removed. Seam carving. Seam carving (or liquid rescaling) is an algorithm for content-aware image resizing, developed by Shai Avidan, of Mitsubishi Electric Research Laboratories (MERL), and Ariel Shamir, of the Interdisciplinary Center and MERL.

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