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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. 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.

  4. Pixel-art scaling algorithms - Wikipedia

    en.wikipedia.org/wiki/Pixel-art_scaling_algorithms

    Next, the rotated image is created with a nearest-neighbor scaling and rotation algorithm that simultaneously shrinks the big image back to its original size and rotates the image. Finally, overlooked single-pixel details are (optionally) restored if the corresponding pixel in the source image is different and the destination pixel has three ...

  5. Free Lossless Image Format - Wikipedia

    en.wikipedia.org/wiki/Free_Lossless_Image_Format

    Free Lossless Image Format (FLIF) is a lossless image format claiming to outperform PNG, lossless WebP, lossless BPG and lossless JPEG 2000 in terms of compression ratio on a variety of inputs. [ 4 ]

  6. List of open file formats - Wikipedia

    en.wikipedia.org/wiki/List_of_open_file_formats

    FLIF – Free Lossless Image Format. GBR – a 2D binary vector image file format, the de facto standard in the printed circuit board (PCB) industry GIF – CompuServe's Graphics Interchange Format (openly published specification, but patent-encumbered by a third party; became free when patents expired in 2004)

  7. Deep Learning Super Sampling - Wikipedia

    en.wikipedia.org/wiki/Deep_learning_super_sampling

    The second stage is an image upscaling step which uses the single raw, low-resolution frame to upscale the image to the desired output resolution. Using just a single frame for upscaling means the neural network itself must generate a large amount of new information to produce the high resolution output, this can result in slight hallucinations ...

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