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  2. 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.

  3. 9-slice scaling - Wikipedia

    en.wikipedia.org/wiki/9-slice_scaling

    9-slice scaling (also known as Scale 9 grid, 9-slicing or 9-patch) is a 2D image resizing technique to proportionally scale an image by splitting it in a grid of nine parts. [ 1 ] The key idea is to prevent image scaling distortion by protecting the pixels defined in 4 parts (corners) of the image and scaling or repeating the pixels in the ...

  4. Image scaling - Wikipedia

    en.wikipedia.org/wiki/Image_scaling

    An image scaled with nearest-neighbor scaling (left) and 2×SaI scaling (right) In computer graphics and digital imaging , image scaling refers to the resizing of a digital image. In video technology, the magnification of digital material is known as upscaling or resolution enhancement .

  5. Deep learning super sampling - Wikipedia

    en.wikipedia.org/wiki/Deep_learning_super_sampling

    Nvidia advertised DLSS as a key feature of the GeForce 20 series cards when they launched in September 2018. [5] At that time, the results were limited to a few video games, namely Battlefield V, [6] or Metro Exodus, because the algorithm had to be trained specifically on each game on which it was applied and the results were usually not as good as simple resolution upscaling.

  6. Pixel-art scaling algorithms - Wikipedia

    en.wikipedia.org/wiki/Pixel-art_scaling_algorithms

    This makes it useful for scaling the details in faces, and in particular eyes. xBRZ is optimized for multi-core CPUs and 64-bit architectures and shows 40–60% better performance than HQx even when running on a single CPU core only. [citation needed] It supports scaling images with an alpha channel, and scaling by integer factors from 2× up ...

  7. Wikipedia:Autosizing images - Wikipedia

    en.wikipedia.org/wiki/Wikipedia:Autosizing_images

    upright=0.8 – scales the image to approximately 80% of the user's default size (20% smaller) upright=1.2 – scales the image to approximately 120% of the user's default size (20% larger) left – shifts the image to the left margin; right – shifts the image to the right margin; center – shifts the image to center between left/right margins

  8. Deep learning anti-aliasing - Wikipedia

    en.wikipedia.org/wiki/Deep_learning_anti-aliasing

    Deep learning anti-aliasing (DLAA) is a form of spatial anti-aliasing created by Nvidia. [1] DLAA depends on and requires Tensor Cores available in Nvidia RTX cards. [1]DLAA is similar to deep learning super sampling (DLSS) in its anti-aliasing method, [2] with one important differentiation being that the goal of DLSS is to increase performance at the cost of image quality, [3] whereas the ...

  9. 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.