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REVIEW 4 major objections 6 minor 2 cited by

ICME 2025 Grand Challenge on Video Super-Resolution for Video Conferencing

T0 review · 4 major / 6 minor · reviewed 2026-08-07 · deepseek-v4-flash

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Pith's one-line read The paper argues that for general-purpose and talking-head video super-resolution, PSNR and SSIM correlate weakly with subjective quality, so rankings should rely on subjective evaluation, while screen-content super-resolution is won by…

desk verdict A useful challenge report with a genuine new dataset, but the central metric-correlation claim is statistically thinner than it looks. read the letter →

arxiv 2506.12269 v2 pith:D5K7W2XG submitted 2025-06-13 eess.IV cs.CVcs.MM

Babak Naderi , Ross Cutler , Juhee Cho , Nabakumar Khongbantabam , Dejan Ivkovic This is my paper · ORCID
classification eess.IVcs.CVcs.MM
keywords videosuper-resolutionconferencingsubjectivequalityassessmentComparisonCategoryRatingCMOScharactererrorratescreencontentITU-TP.910
verification ladder T0 review T1 audit T2 compute T3 formal

The pith

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

The reading

This paper reports the organization and results of a grand challenge on video super-resolution for video conferencing, where low-resolution H.265-encoded video must be upscaled under a low-delay, causal constraint. Its central finding is that in the general-purpose and talking-head tracks, standard objective metrics such as PSNR and SSIM correlate weakly with subjective quality scores, so ranking models by these metrics would mislead; subjective evaluation is necessary. In the screen-content track, by contrast, PSNR and SSIM correlate strongly with subjective opinion, and the winning models combined super-resolution with OCR-based text recovery, evaluated using character error rate in addition to subjective scores. The paper also contributes new open datasets, including a screen-content dataset and an extension of the talking-head VCD dataset. A sympathetic reader would care because the result questions common practice in benchmarking video super-resolution models and points to content-dependent evaluation.

What carries the argument

The central mechanism is the crowdsourced implementation of ITU-T Rec. P.910 Comparison Category Rating (CCR), which produces CMOS scores by having raters compare each processed clip directly against the ground-truth source. For screen content, this is augmented by a composite challenge score that averages normalized CMOS with character error rate (CER), computed from OCR over approximately 210,000 annotated characters. The paper then uses model-level Pearson, Spearman, Kendall's Tau-b, and a confidence-interval-adjusted Tau-b95 to compare these subjective scores against PSNR, SSIM, VMAF, LPIPS, and CER.

What would settle it

Re-run the subjective evaluation with a larger panel of trained raters on the same test clips and compute the Spearman correlation between CMOS and PSNR or SSIM per track; if in Track 1 or Track 2 that correlation rises above about 0.8, the paper's claim that objective metrics rank poorly would be contradicted. Alternatively, if a held-out set of 50 or more models shows PSNR ranking matching subjective ranking, the result would not generalize.

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Extended reading notes

Core claim

The authors claim that objective metrics and subjective quality scores are only weakly correlated for general-purpose and talking-head video super-resolution, making subjective evaluation necessary for ranking models in these contexts. For Track 1, the Pearson correlation between CMOS and PSNR is -0.212 and with SSIM is -0.073, while LPIPS reaches -0.893 Pearson but only -0.800 Spearman; Track 2 shows a similar pattern, with PSNR Pearson at 0.232, SSIM at 0.316, and LPIPS at -0.732 Pearson and -0.720 Spearman. In contrast, for screen content, PSNR and SSIM show strong correlations with CMOS (Pearson 0.885 and 0.894, respectively), and CER correlates strongly as well. The paper also reports that in the general-purpose and talking-head tracks, pretrained image-based baseline models ranked first in subjective tests, while in the screen-content track, participating teams that used OCR and CER-based methods significantly outperformed all baselines.

Load-bearing premise

The conclusions assume the crowdsourced CCR subjective ratings are accurate enough on these 300-frame clips that model-level correlations with about ten models per track are meaningful, even though the paper reports no rater counts, inter-rater agreement, or per-clip confidence.

Editorial extensions

If this is right

  • Benchmarks that rank video super-resolution models by PSNR or SSIM on general or talking-head content will not reflect human preference; organizers should budget for subjective testing.
  • LPIPS is the strongest objective proxy in these tracks, but its Spearman correlation of about 0.8 still leaves enough disagreement that final rankings should be subjective.
  • For screen content, PSNR and SSIM are usable development metrics, and CER should be reported alongside them whenever text legibility matters.
  • Screen-content super-resolution systems should include OCR or text-recovery components; the top two teams used them and clearly separated from generic baselines.
  • The newly released screen-content and extended talking-head datasets allow future training and evaluation outside the challenge.

Reading between the lines

Editorial extensions of the paper, not claims the author makes directly.

  • Since every model's CMOS is negative in all three tracks, none of the submissions improved on the ground-truth source: the practical task was damage minimization, and the challenge measured which model degraded least, a framing the paper does not itself emphasize.
  • The low correlation in Tracks 1 and 2 may be partly an artifact of the narrow model sample, about ten pipelines per track, many of them off-the-shelf baselines, so the observed coefficients are estimates over a small, nonrandom set.
  • A natural testable extension is to add a CER-style loss or OCR post-processing to general-purpose and talking-head models, since the screen-content result suggests that training to preserve semantically identifiable structures may also improve perceptual quality.
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Editorial analysis

A structured set of objections, weighed in public.

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

Referee Report

4 major / 6 minor

Summary. The manuscript reports on the ICME 2025 Grand Challenge on Video Super-Resolution for Video Conferencing, covering three tracks (general-purpose, talking head, screen content). It describes the datasets, the causal low-delay setup, the crowdsourced CCR subjective evaluation, and the results, including baseline and participant models. The main findings are that objective metrics (PSNR, SSIM, VMAF, LPIPS) correlate only weakly with subjective CMOS for general-purpose and talking-head content, while for screen content the top-performing teams used OCR- and CER-aware approaches. The paper also open-sources a new screen-content dataset and an extension of the VCD talking-head dataset.

Significance. If the correlation finding is statistically robust, the paper would provide practical evidence that common objective metrics mislead model ranking for video-conferencing SR, and that screen-content SR is best evaluated with text-recovery measures. The release of the datasets is a valuable contribution. The paper also includes a sensible test-retest control for the CER measure (0.0024) and transparently discloses that one baseline violates the causal constraint. However, the correlation claim is currently supported only by small-sample point estimates without confidence intervals, and the subjective scoring procedure lacks rater-level statistics, so the significance of the headline finding is not yet established.

major comments (4)
  1. [III-A, Tables IV and V] The central claim that objective metrics correlate weakly with subjective quality in Tracks 1 and 2 rests on model-level correlation coefficients computed from only 9 models (Track 1) and 10 models (Tracks 2 and 3). No confidence intervals, significance tests, or leave-one-out sensitivity analyses are provided. Given the narrow CMOS range (e.g., -2.216 to -2.712 in Track 1), the point estimates are fragile; for instance, the Track 1 VMAF Pearson coefficient of 0.581 could plausibly include zero, and the LPIPS Pearson coefficient of -0.893 may be driven by the single lowest-CMOS model. The authors should supply bootstrap (or jackknife) confidence intervals for all coefficients in Tables IV and V, or at minimum temper the conclusion to acknowledge that the low correlations are not established beyond this small, narrow-range model set.
  2. [II-B] The reliability of the subjective scores is not documented. The paper reports per-model CMOS confidence intervals in Table II but no rater counts, no inter-rater agreement (e.g., ICC or Krippendorff's alpha), and no per-clip confidence values. This matters because the tie-ranking procedure in Section III depends on 'no significant difference between the distributions of CMOS values,' and because the correlation analysis in Section III-A relies on the precision of the CMOS values. Add the number of raters per clip, the rating design, and an inter-rater reliability statistic.
  3. [III] The rule for assigning tied ranks in Table II is not operationalized. The text states that two consecutive models are tied when 'there is no significant difference between the distributions of CMOS values,' but no statistical test is named, and no threshold is given. If the method is the CI-overlap approach described in Ref. [28], state that explicitly and describe how the 95% CIs are used. Without this, the ranks, on which several qualitative conclusions depend, are not reproducible.
  4. [II-B, Eq. (1)] The Track 3 challenge score in Eq. (1) is ambiguous as typeset. The text calls it a 'normalized average of CMOS and CER,' but the equation can be read either as (CMOS/-3) + (CER/2) or as CMOS/(-3 + CER/2). The ranking in Table II is consistent with the latter interpretation, but the equation should be typeset as a single unambiguous fraction (e.g., score = CMOS / (-3 + CER/2)) so that the scoring rule is reproducible from the text alone.
minor comments (6)
  1. [II-A, Table I] The Track 3 training-set counts (1520 GT, 9072 LR+H.265) do not match the expected six QP levels per clip (1520x6=9120). Please clarify whether this is due to encoding failures or to a different number of QPs for some clips.
  2. [II-A] The specific H.265 QP values used are not listed. Providing the QP numbers would improve reproducibility.
  3. [II-B] The CER measurement pipeline is under-specified; the OCR engine and the frame/text-region selection criteria should be stated, as CER is a key outcome for Track 3.
  4. [III-A, Table IV] The phrase 'Spearman correlation coefficient of ≤0.8' is confusing; in Track 1 the value is -0.800, so it should be phrased as 'an absolute value no greater than 0.8.'
  5. [III] The statement that participating teams 'significantly outperformed' baselines in Track 3 is not backed by a significance test; the non-overlapping CIs in Table II are suggestive, but the paper should either perform a formal test or refer to the CI-based tie-breaking rule.
  6. [Table VI] The FLOPs value for BVIVSR appears twice as 455.16; please verify whether the FLOPs are identical for Track 1 and Track 2, and fix the formatting of the table.

Circularity Check

0 steps flagged · score 2.0 of 10

No significant circularity: the central correlation and ranking claims are empirical, and the organizers' self-citations are instrumental rather than load-bearing.

full rationale

The paper's central claims are empirical observations from a challenge: CMOS subjective scores are correlated with objective metrics, and model rankings are reported. The CMOS scores come from a crowdsourced CCR implementation of ITU-T P.910 [23], which is a measurement procedure that produces independent human judgments, not a mathematical consequence of the paper's definitions. The objective metrics (PSNR, SSIM, VMAF, LPIPS, CER) are standard computed quantities. Equation 1 defines the Track 3 challenge score as a weighted combination of CMOS and CER, but this is an evaluation rule chosen by the organizers, not a derived prediction; the subsequent correlation analysis treats CMOS and CER as separate measured variables. The self-citations to the VCD dataset [22] and the crowdsourcing method [23] are relevant because the organizers built those resources, but neither supplies a conclusion by construction: the subjective ratings are external human measurements, and the datasets are test material. No fitted parameter is renamed as a prediction, no uniqueness theorem is imported from the authors' prior work, and no result is derived from an assumption that already contains the conclusion. Concerns about small sample size, narrow quality range, or the statistical strength of the correlations are legitimate robustness or correctness issues, but they are not circularity. Therefore the derivation chain is self-contained, and the low circularity score reflects only the presence of non-load-bearing self-citations.

Assumptions & free parameters 1 free parameters · 4 assumptions · 0 invented entities

The central evaluation rests on the reliability of crowdsourced subjective ratings and OCR-derived CER as ground truth, plus the representativeness of H.265 fixed-QP degradations. Eq. 1's coefficient is a hand-chosen design parameter, not fitted to data, but it affects the Track 3 ranking.

free parameters (1)
  • Track 3 challenge score coefficients = CER coefficient 0.5; CMOS normalized by -3
    Eq. 1 combines CMOS and CER with hand-chosen coefficients of 1/(-3) and 1/2. No justification or ablation is given, and the typeset formula's direction is ambiguous, yet these weights affect the official Track 3 ranking.
assumptions (4)
  • domain assumption Crowdsourced CCR (ITU-T P.910) ratings yield valid, unbiased quality labels despite 300-frame clips and worker variability.
    Section II-B defines evaluation by this method; no inter-rater agreement or worker exclusion details are reported.
  • domain assumption OCR-based CER measured on annotated frames is a valid proxy for text readability in screen content.
    Section II-B and Table III; GT CER of 0.0024 gives test-retest support, but the criterion that text must be readable in the source at target resolution is qualitative.
  • domain assumption Encoding LR inputs with H.265 at six fixed QPs simulates realistic conferencing compression.
    Section II-A; QP levels are fixed but no evidence is provided that this matches deployed conferencing codec behavior.
  • domain assumption Roughly ten models per track is sufficient for rank-correlation inference between subjective and objective metrics.
    Section III-A, Tables IV-V; Pearson, Spearman, and Kendall correlations on N=10 without confidence intervals are statistically fragile.

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Cite this review

Pith. "Pith review of ICME 2025 Grand Challenge on Video Super-Resolution for Video Conferencing." pith.science (2026). https://pith.science/paper/D5K7W2XG

@misc{pith2026250612269,
  author       = {Pith},
  title        = {Pith review of: ICME 2025 Grand Challenge on Video Super-Resolution for Video Conferencing},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/D5K7W2XG}},
  note         = {Machine review of arXiv:2506.12269}
}
read the original abstract

Super-Resolution (SR) is a critical task in computer vision, focusing on reconstructing high-resolution (HR) images from low-resolution (LR) inputs. The field has seen significant progress through various challenges, particularly in single-image SR. Video Super-Resolution (VSR) extends this to the temporal domain, aiming to enhance video quality using methods like local, uni-, bi-directional propagation, or traditional upscaling followed by restoration. This challenge addresses VSR for conferencing, where LR videos are encoded with H.265 at fixed QPs. The goal is to upscale videos by a specific factor, providing HR outputs with enhanced perceptual quality under a low-delay scenario using causal models. The challenge included three tracks: general-purpose videos, talking head videos, and screen content videos, with separate datasets provided by the organizers for training, validation, and testing. We open-sourced a new screen content dataset for the SR task in this challenge. Submissions were evaluated through subjective tests using a crowdsourced implementation of the ITU-T Rec P.910.

Figures

Figures reproduced from arXiv: 2506.12269 by the authors.

Figure 1
Figure 1. Visual comparisons of models evaluated in the challenge for Track 1 (A), Track 2 (B), and Track 3 (C). [PITH_FULL_IMAGE:figures/full_fig_p003_1.png] view at source ↗
Figure 2
Figure 2. Rate-distortion plots for Track 1 (A), Track 2 (B) and [PITH_FULL_IMAGE:figures/full_fig_p005_2.png] view at source ↗

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Forward citations

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Reference graph

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