Pith. sign in

REVIEW 1 cited by

Lossless Image Compression through Super-Resolution

Not yet reviewed by Pith; the record is open.

Re-run · record.json Download PDF Read on arXiv ↗

This paper has not been read by Pith yet. Machine review is queued; the pith claim, tier, and objections will appear here once it completes.

SPECIMEN: schema-true, not a live event

T0 review · schema-true

One-sentence machine reading of the paper's core claim.

pith:XXXXXXXX · record.json · timestamp

arxiv 2004.02872 v1 pith:KJMJDE6D submitted 2020-04-06 eess.IV cs.CVcs.LG

Sheng Cao , Chao-Yuan Wu , Philipp Krähenbühl This is my paper · ORCID
classification eess.IVcs.CVcs.LG
keywords super-resolutioncompressionimagelosslesssrecableachievealgorithm
verification ladder T0 review T1 audit T2 compute T3 formal
0 comments
read the original 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.

Discussion (0). Continue with ORCID to comment.

Forward citations

Cited by 1 Pith paper

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. Stay-Positive: A Case for Ignoring Real Image Features in Fake Image Detection

    cs.CV 2025-02 conditional novelty 6.0 of 10

    Retraining only the last layer of a fake image detector with non-negative weights, so it ignores features linked to real images, improves robustness to post-processing and detection of inpainted images.

Pith tools