Papers › The 2018 PIRM Challenge on Perceptual Image Super-resolution
The 2018 PIRM Challenge on Perceptual Image Super-resolution
Yochai Blau, Roey Mechrez, Radu Timofte, Tomer Michaeli, Lihi Zelnik-Manor
This paper reports on the 2018 PIRM challenge on perceptual super-resolution (SR), held in conjunction with the Perceptual Image Restoration and Manipulation (PIRM) workshop at ECCV 2018. In contrast to previous SR challenges, our evaluation methodology jointly quantifies accuracy and perceptual quality, therefore enabling perceptual-driven methods to compete alongside algorithms that target PSNR maximization. Twenty-one participating teams introduced algorithms which well-improved upon the existing state-of-the-art methods in perceptual SR, as confirmed by a human opinion study. We also analyze popular image quality measures and draw conclusions regarding which of them correlates best with human opinion scores. We conclude with an analysis of the current trends in perceptual SR, as reflected from the leading submissions.
Code
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Datasets
Introduced by this paper, per the archive.
Results from the paper archive 2025-07-28
| Task | Dataset | Model | Metric | Value | Rank at snapshot | Leaderboard | Report |
|---|---|---|---|---|---|---|---|
| Video Quality Assessment | MSU SR-QA Dataset | PI | KLCC | 0.39101 | #33 of 60 | Archive leaderboard | report |
| Video Quality Assessment | MSU SR-QA Dataset | PI | PLCC | 0.53178 | #33 of 60 | Archive leaderboard | report |
| Video Quality Assessment | MSU SR-QA Dataset | PI | SROCC | 0.52319 | #33 of 60 | Archive leaderboard | report |
| Video Quality Assessment | MSU SR-QA Dataset | PI | Type | NR | #33 of 60 | Archive leaderboard | report |
Ranks are positions in the archive's leaderboards as they stood at the 2025-07-28 snapshot. Results published since then are not among these rows, so a rank here is not a current standing.
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