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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

TaskDatasetResultRank
Lossless CompressionKodak
Bits per Byte9.1
31
Lossless Image CompressionDIV2K
bpp8.47
29
Lossless Image CompressionKodak sRGB 8-bit (test)
Encoding Time (sec)0.58
28
Lossless Image CompressionKodak (test)
bpsp9.1
25
Lossless Image CompressionCLIC mobile
BPD7.32
24
Lossless Image CompressionUrban100
BPP9.92
12
Lossless Image CompressionAdobe Portrait
Bits Per Pixel (BPP)5.77
12
Lossless Image CompressionCityscapes
BPP (Bits Per Pixel)6.05
11
Lossless Image CompressionImages 768 x 512 resolution
Encoding Time (sec)0.58
11
Lossless Image CompressionImages 2048 x 1536 resolution
Encoding Time (s)4.11
10
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