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REVIEW 2 major objections 2 minor 37 references

Classical full-reference metrics like SSIM and DISTS predict perceptual prominence of super-resolution artifacts more reliably than no-reference methods or specialized detectors.

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T0 review · grok-4.3

2026-06-30 21:28 UTC pith:4C76CKFX

load-bearing objection The paper defines artifact prominence via crowdsourcing and finds SSIM/DISTS track it better than other methods, but the labels lack any reported stability checks. the 2 major comments →

arxiv 2605.14847 v1 pith:4C76CKFX submitted 2026-05-14 cs.CV

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

Ivan Molodetskikh , Kirill Malyshev , Mark Mirgaleev , Nikita Zagainov , Evgeney Bogatyrev , Dmitriy Vatolin This is my paper
classification cs.CV
keywords super-resolutionartifact prominencecrowdsourced evaluationperceptual image qualityfull-reference metricsSSIMDISTSartifact detection
verification ladder T0 review T1 audit T2 compute T3 formal T4 reserved

The pith

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The paper defines artifact prominence as the fraction of viewers who notice a defect in a highlighted region of a super-resolved image. It releases a crowdsourced dataset suite of 3,935 such annotated masks drawn from multiple sources, including a realistic setting without ground-truth references. Tests across the suite show that standard full-reference metrics supply localized signals that match human judgments of noticeability, while many other approaches do not hold up when the dataset or reference condition changes. The release includes an objective scoring protocol that lets new metrics be checked against the prominence labels without repeating human annotation. This changes evaluation from counting defects to weighing their visible effect on viewers.

Core claim

Artifact prominence is defined as the fraction of viewers who judge a highlighted region to contain a noticeable artifact. A crowdsourced protocol produces the SR-Prominence dataset suite with 3,935 masks from DeSRA, Open Images, Urban100, and a no-ground-truth Urban100-HR setting. Re-annotation of DeSRA shows 48.2 percent of its prior binary artifacts are not noticed by a majority of viewers. Classical full-reference metrics, especially SSIM and DISTS, provide strong localized prominence signals while no-reference IQA methods and specialized artifact detectors fail to generalize across datasets and reference settings.

What carries the argument

artifact prominence, defined as the fraction of viewers who judge a highlighted region to contain a noticeable artifact, used as the target measure for perceptual impact instead of binary defect presence

Load-bearing premise

The crowdsourced majority-vote prominence values remain stable when applied to new annotator pools and image sources beyond those used in the study.

What would settle it

A fresh crowdsourced annotation round on the same image regions with a different viewer pool produces prominence values that diverge substantially from the original labels and from the predictions of SSIM or DISTS.

Watch this falsifier — get emailed when new claim-graph text bears on it.

If this is right

  • 48.2 percent of binary artifacts previously labeled in DeSRA are not noticed by a majority of viewers under the new protocol.
  • SSIM and DISTS can supply localized predictions of where artifacts will stand out to people.
  • The released objective scoring protocol lets any new metric be benchmarked on the suite without further crowdsourcing.
  • Super-resolution methods can be compared on the basis of perceptual impact rather than binary defect counts.

Where Pith is reading between the lines

These are editorial extensions of the paper, not claims the author makes directly.

  • Training objectives for future super-resolution networks could use prominence estimates from SSIM or DISTS to suppress only the defects that most viewers notice.
  • The crowdsourced protocol could be applied to measure perceptual impact in related tasks such as image denoising or compression artifact evaluation.
  • The consistent failure of no-reference methods points to a need for metrics that better capture localized viewer attention without a clean reference image.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit.

Referee Report

2 major / 2 minor

Summary. The paper introduces artifact prominence as the fraction of viewers who notice an artifact in a highlighted region of a super-resolved image. It presents a crowdsourced annotation protocol and the SR-Prominence dataset suite (3,935 masks from DeSRA, Open Images, Urban100, and no-ground-truth Urban100-HR), re-annotates DeSRA to find 48.2% of its binary artifacts unnoticed by a majority, audits detectors and metrics, and reports that classical full-reference metrics (especially SSIM and DISTS) yield strong localized prominence signals while no-reference IQA methods and specialized artifact detectors often fail to generalize. The suite is released with an objective scoring protocol for benchmarking without new crowdsourcing.

Significance. If the prominence labels are shown to be stable, the dataset and protocol would usefully shift SR artifact evaluation from binary presence to perceptual impact. The reported strength of SSIM and DISTS as localized signals is a concrete, falsifiable observation that could influence metric selection. The public release of the dataset together with a reproducible scoring protocol is a clear strength supporting community benchmarking.

major comments (2)
  1. [Section 3] Protocol description (Section 3 / Dataset Construction): no annotation instructions, inter-annotator agreement (Fleiss’ κ, Krippendorff’s α), bootstrap stability of majority votes, or cross-pool replication are reported. These checks are load-bearing for treating the prominence fractions as reliable ground truth, especially for the Urban100-HR no-GT subset.
  2. [Section 5] Metric audit results (Section 5): the claim that SSIM/DISTS provide strong localized signals while other methods fail to generalize rests directly on the crowdsourced prominence values as ground truth. Without the missing agreement and stability statistics, the reported superiority and generalization failures cannot be evaluated for robustness.
minor comments (2)
  1. [Abstract] Abstract and Section 4: the phrase 'objective scoring protocol' is used without a concrete description or pseudocode; adding a short formal definition would improve clarity.
  2. [Dataset release] Dataset release statement: confirming that raw per-annotator votes (not only aggregated prominence) are included would strengthen transparency and allow independent re-analysis.

Simulated Author's Rebuttal

2 responses · 0 unresolved

We thank the referee for their thoughtful review and for highlighting the importance of validating the crowdsourced protocol. We address each major comment below and commit to revisions that will strengthen the manuscript's claims regarding label reliability and metric evaluation.

read point-by-point responses

  1. Referee: [Section 3] Protocol description (Section 3 / Dataset Construction): no annotation instructions, inter-annotator agreement (Fleiss’ κ, Krippendorff’s α), bootstrap stability of majority votes, or cross-pool replication are reported. These checks are load-bearing for treating the prominence fractions as reliable ground truth, especially for the Urban100-HR no-GT subset.

    Authors: We agree these statistics are essential to substantiate the prominence labels as reliable ground truth. In the revised manuscript, we will include the complete annotation instructions in Section 3. We will compute and report inter-annotator agreement metrics including Fleiss’ κ and Krippendorff’s α. We will also add analyses of bootstrap stability for the majority votes and cross-pool replication results. For the Urban100-HR no-ground-truth subset, we will provide dedicated replication details to confirm consistency across annotation pools. revision: yes

  2. Referee: [Section 5] Metric audit results (Section 5): the claim that SSIM/DISTS provide strong localized signals while other methods fail to generalize rests directly on the crowdsourced prominence values as ground truth. Without the missing agreement and stability statistics, the reported superiority and generalization failures cannot be evaluated for robustness.

    Authors: We acknowledge that the metric audit results in Section 5 depend on the quality of the prominence ground truth. Incorporating the agreement, stability, and replication statistics as described in our response to the Section 3 comment will enable a more rigorous evaluation of the robustness of the findings on SSIM, DISTS, and the generalization failures of other methods. The revised manuscript will include these validations to support the claims. revision: yes

Circularity Check

0 steps flagged

No significant circularity

full rationale

The paper introduces a new crowdsourced protocol and dataset (SR-Prominence) for artifact prominence, defined directly from viewer annotations as the fraction noticing artifacts in highlighted regions. No equations, fitted parameters, or predictions are described that reduce to the same inputs by construction. Central claims about metric performance (SSIM/DISTS vs. others) rest on these independent new labels across multiple datasets, including no-GT settings, without self-definitional loops, load-bearing self-citations, or renaming of known results. The derivation chain is self-contained as empirical data collection followed by external benchmarking.

Axiom & Free-Parameter Ledger

0 free parameters · 0 axioms · 0 invented entities

The work rests on the empirical assumption that crowdsourced majority votes reliably capture perceptual noticeability; no mathematical axioms, free parameters, or invented physical entities are invoked.

pith-pipeline@v0.9.1-grok · 5833 in / 1098 out tokens · 22919 ms · 2026-06-30T21:28:01.208705+00:00 · methodology

0 comments
read the original 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 in perceptual impact--some are barely noticeable, while others are highly disturbing--yet existing detection methods treat them equally. We propose artifact prominence as an evaluative target, defined as the fraction of viewers who judge a highlighted region to contain a noticeable artifact. We design a crowdsourced annotation protocol and construct SR-Prominence, a dataset suite containing 3,935 artifact masks from DeSRA, Open Images, Urban100, and a realistic no-ground-truth Urban100-HR setting, annotated with prominence. Re-annotating DeSRA reveals that 48.2% of its in-lab binary artifacts are not noticed by a majority of viewers. Across the suite, we audit SR artifact detectors, image-quality metrics, and SR methods. We find that classical full-reference metrics, especially SSIM and DISTS, provide surprisingly strong localized prominence signals, whereas no-reference IQA methods and specialized artifact detectors often fail to generalize across datasets and reference settings. SR-Prominence is released with an objective scoring protocol that allows new metrics to be benchmarked on our suite without further crowdsourcing. Together, the data and protocols enable SR artifact evaluation to move from binary defect presence toward perceptual impact. SR-Prominence is available at https://huggingface.co/datasets/imolodetskikh/sr-artifact-prominence.

Figures

Figures reproduced from arXiv: 2605.14847 by Dmitriy Vatolin, Evgeney Bogatyrev, Ivan Molodetskikh, Kirill Malyshev, Mark Mirgaleev, Nikita Zagainov.

Figure 1
Figure 1. Figure 1: SR-Prominence artifact examples. Rows show Open Images (top) and DeSRA (bottom) [PITH_FULL_IMAGE:figures/full_fig_p002_1.png] view at source ↗
Figure 2
Figure 2. Figure 2: Viewer interface for subjective data collection. [PITH_FULL_IMAGE:figures/full_fig_p004_2.png] view at source ↗
Figure 3
Figure 3. Figure 3: Example of mask preprocessing for human visual assessment. An artifact-detection method should output a tight mask around an artifact, since such masks are more useful for subsequent analysis and downstream tasks such as automatic correction. However, tight masks make it harder to visually judge whether the masked area contains an artifact. Additionally, the raw output from some methods is sparse, making i… view at source ↗
Figure 4
Figure 4. Figure 4: Bootstrap-analysis results for an image with a highly prominent artifact (left) and barely [PITH_FULL_IMAGE:figures/full_fig_p013_4.png] view at source ↗
Figure 5
Figure 5. Figure 5: Architecture of the reference artifact-prominence baseline. The input image is upscaled [PITH_FULL_IMAGE:figures/full_fig_p015_5.png] view at source ↗
Figure 6
Figure 6. Figure 6: Example artifacts detected by the baseline. (a): low-resolution input image; (b): target SR [PITH_FULL_IMAGE:figures/full_fig_p018_6.png] view at source ↗
Figure 7
Figure 7. Figure 7: Example false detections by the baseline. (a): low-resolution input image; (b): target SR [PITH_FULL_IMAGE:figures/full_fig_p019_7.png] view at source ↗
Figure 8
Figure 8. Figure 8: Example false detections by the baseline due to inaccurate restoration from pseudo-GT [PITH_FULL_IMAGE:figures/full_fig_p019_8.png] view at source ↗

discussion (0)

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