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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.
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 ...
The feature was first unveiled during CES 2023 as RTX Video Super Resolution. [3] The feature uses the on-board Tensor Cores to upscale browser video content in real time. [4] The feature is currently only available on RTX 30 and 40 series gpus with support for 20 series gpus coming in the future. [5]
Video super-resolution (VSR) is the process of generating high-resolution video frames from the given low-resolution video frames. Unlike single-image super-resolution (SISR) , the main goal is not only to restore more fine details while saving coarse ones, but also to preserve motion consistency.
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.
It is not related to hqx, but is rather a generic upscaling-smoothing algorithm. [8] hqx has also been implemented in ffmpeg for upscaling images and videos 2x, 3x, or 4x. An account of the production of the translation for ffmpeg is here and usage may be something like: ffmpeg -i %1 -filter_complex hqx=2 hqx2-%1 to produce a 2x image or video.
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