Lossless Image Compression through Super-Resolution
About
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.
Sheng Cao, Chao-Yuan Wu, Philipp Kr\"ahenb\"uhl• 2020
Related benchmarks
| Task | Dataset | Result | Rank | |
|---|---|---|---|---|
| Lossless Compression | Kodak | Bits per Byte9.1 | 31 | |
| Lossless Image Compression | DIV2K | bpp8.47 | 29 | |
| Lossless Image Compression | Kodak sRGB 8-bit (test) | Encoding Time (sec)0.58 | 28 | |
| Lossless Image Compression | Kodak (test) | bpsp9.1 | 25 | |
| Lossless Image Compression | CLIC mobile | BPD7.32 | 24 | |
| Lossless Image Compression | Urban100 | BPP9.92 | 12 | |
| Lossless Image Compression | Adobe Portrait | Bits Per Pixel (BPP)5.77 | 12 | |
| Lossless Image Compression | Cityscapes | BPP (Bits Per Pixel)6.05 | 11 | |
| Lossless Image Compression | Images 768 x 512 resolution | Encoding Time (sec)0.58 | 11 | |
| Lossless Image Compression | Images 2048 x 1536 resolution | Encoding Time (s)4.11 | 10 |
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