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Lossless Image Compression through Super-Resolution

Image and Video Processing 2020-04-07 v1 Computer Vision and Pattern Recognition Machine Learning

Abstract

We introduce a simple and efficient lossless image compression algorithm. We store a low resolution version of an image as raw pixels, followed by several iterations of lossless super-resolution. For lossless super-resolution, we predict the probability of a high-resolution image, conditioned on the low-resolution input, and use entropy coding to compress this super-resolution operator. Super-Resolution based Compression (SReC) is able to achieve state-of-the-art compression rates with practical runtimes on large datasets. Code is available online at https://github.com/caoscott/SReC.

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Cite

@article{arxiv.2004.02872,
  title  = {Lossless Image Compression through Super-Resolution},
  author = {Sheng Cao and Chao-Yuan Wu and Philipp Krähenbühl},
  journal= {arXiv preprint arXiv:2004.02872},
  year   = {2020}
}

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