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New protocol and dataset evaluate super-resolution artifacts by perceptual impact

By PulseAugur Editorial · [1 sources] ·

Researchers have introduced SR-Prominence, a new crowdsourced protocol and dataset designed to evaluate artifacts in image super-resolution (SR) methods based on perceptual impact. The protocol defines artifact prominence as the proportion of viewers who notice an artifact in a highlighted region. The accompanying dataset includes 3,935 artifact masks from various sources, annotated with prominence scores. Initial findings indicate that traditional metrics like SSIM and DISTS show strong correlation with perceived artifact prominence, while specialized artifact detectors and no-reference metrics struggle to generalize. AI

IMPACT Enables more accurate evaluation of super-resolution models by focusing on perceptual artifact impact rather than just presence.

RANK_REASON This is a research paper introducing a new protocol and dataset for evaluating image super-resolution artifacts. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.CV →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

New protocol and dataset evaluate super-resolution artifacts by perceptual impact

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This is a research paper introducing a new protocol and dataset for evaluating image super-resolution artifacts. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [1]

  1. arXiv cs.CV TIER_1 English(EN) · Ivan Molodetskikh, Kirill Malyshev, Mark Mirgaleev, Nikita Zagainov, Evgeney Bogatyrev, Dmitriy Vatolin ·

    SR-Prominence: A Crowdsourced Protocol and Dataset Suite for Perceptually-Weighted Super-Resolution Artifact Evaluation

    arXiv:2605.14847v2 Announce Type: replace Abstract: Modern image super-resolution methods generate detailed, visually appealing results, but they often introduce visual artifacts: unnatural patterns and texture distortions that degrade perceived quality. These defects vary widely…

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