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REVIEW 4 major objections 6 minor 107 references

NTIRE 2025 Challenge on Short-form UGC Video Quality Assessment and Enhancement: Methods and Results

T0 review · 4 major / 6 minor · reviewed 2026-08-16 · deepseek-v4-flash

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Pith's one-line read This challenge report claims that for diffusion-based super-resolution of short-form user-generated content, the ranking preferred by human experts does not match the ranking given by standard objective quality metrics.

desk verdict A competent NTIRE challenge report whose only scientific claim — subjective/objective inconsistency in Track 2 — rests on a five-expert user study with no reliability statistics; the paper is worth refereeing but that conclusion needs scaling back or supporting. read the letter →

arxiv 2504.13131 v1 pith:7BZAGTZ6 submitted 2025-04-17 eess.IV cs.AIcs.CV

Xin Li , Kun Yuan , Bingchen Li , Fengbin Guan , Yizhen Shao , Zihao Yu , Xijun Wang , Yiting Lu
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Wei Luo Suhang Yao Ming Sun Chao Zhou Zhibo Chen Radu Timofte Yabin Zhang Ao-Xiang Zhang Tianwu Zhi Jianzhao Liu Yang Li Jingwen Xu Yiting Liao Yushen Zuo Mingyang Wu Renjie Li Shengyun Zhong Zhengzhong Tu Yufan Liu Xiangguang Chen Zuowei Cao Minhao Tang Shan Liu Kexin Zhang Jingfen Xie Yan Wang Kai Chen Shijie Zhao Yunchen Zhang Xiangkai Xu Hong Gao Ji Shi Yiming Bao Xiugang Dong Xiangsheng Zhou Yaofeng Tu Ying Liang Yiwen Wang Xinning Chai Yuxuan Zhang Zhengxue Cheng Yingsheng Qin Yucai Yang Rong Xie Li Song Wei Sun Kang Fu Linhan Cao Dandan Zhu Kaiwei Zhang Yucheng Zhu Zicheng Zhang Menghan Hu Xiongkuo Min Guangtao Zhai Zhi Jin Jiawei Wu Wei Wang Wenjian Zhang Yuhai Lan Gaoxiong Yi Hengyuan Na Wang Luo Di Wu MingYin Bai Jiawang Du Zilong Lu Zhenyu Jiang Hui Zeng Ziguan Cui Zongliang Gan Guijin Tang Xinglin Xie Kehuan Song Xiaoqiang Lu Licheng Jiao Fang Liu Xu Liu Puhua Chen Ha Thu Nguyen Katrien De Moor Seyed Ali Amirshahi Mohamed-Chaker Larabi Qi Tang Linfeng He Zhiyong Gao Zixuan Gao Guohua Zhang Zhiye Huang Yi Deng Qingmiao Jiang Lu Chen Yi Yang Xi Liao Nourine Mohammed Nadir Yuxuan Jiang Qiang Zhu Siyue Teng Fan Zhang Shuyuan Zhu Bing Zeng David Bull Meiqin Liu Chao Yao Yao Zhao
This is my paper · ORCID
classification eess.IVcs.AIcs.CV
keywords short-formUGCvideoqualityassessmentefficientVQAdiffusion-basedsuper-resolutionKwaiSRdatasetsubjectivepreferenceobjectiveperceptualmetricsbenchmarkchallenge
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 challenge report claims that two things can be done for short-form user-generated content (S-UGC): video quality can be assessed accurately by lightweight models, and diffusion-based super-resolution can improve the subjective quality of in-the-wild images. The paper's central finding, however, is that the two goals do not line up: in the super-resolution track, the ranking produced by standard objective metrics disagreed with the ranking produced by five expert viewers. A method that won the objective leaderboard was rated fourth by experts, while the experts' top choice ranked sixth objectively. If the user study is representative, leaderboard positions on PSNR, LPIPS, or MUSIQ should not be read as statements about which enhanced image people actually prefer.

What carries the argument

The argument is carried by the challenge's evaluation protocol and its new dataset. Track 1 uses the KVQ database with a coarse-to-fine scoring scheme and a hard limit of 120 GFLOPs, forcing contestants to trade accuracy against compute. Track 2 introduces the KwaiSR dataset (1,800 synthetic paired images and 1,900 real-world low-quality images, split 8:1:1) and evaluates the six objectively best submissions through a five-expert user study in which each expert spent about eight hours choosing the most realistic result. That human-preference step is the load-bearing mechanism: it produces the subjective rankings against which the objective metrics are compared.

What would settle it

Run the same pairwise user study with a larger and more diverse panel, say 50 or more non-expert viewers, and compute a majority ranking with agreement statistics. If the majority ranking matches the objective ordering, or if some standard metric such as LPIPS or MUSIQ correlates with the larger panel's preferences, then the claimed inconsistency is an artifact of the five-expert panel rather than a general property of current metrics.

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Extended reading notes

Core claim

On its own terms, the paper establishes a benchmark result and a negative result. The benchmark result is that efficient video quality assessment under a 120 GFLOPs limit is viable: the winning Track 1 model scored 0.922 on the combined metric while using 47.39 GFLOPs and 33.01M parameters, with the top five teams all above 0.90. The negative result is that, for diffusion-based super-resolution of short-form UGC images, subjective preference and objective quality metrics diverge. In the user study of the six teams shortlisted by objective performance, TACO SR had the highest expert winning rates (0.2775 on synthetic and 0.3529 on wild images) yet ranked sixth by objective measures, while the objectively top-ranked team, SYSU-FVL-Team, ranked fourth in expert preference. The paper states this as evidence that current perceptual metrics may not reliably reflect perceived quality in generative-model-based S-UGC super-resolution.

Load-bearing premise

The whole subjective-versus-objective conclusion rests on the assumption that five professional image-processing experts, each spending about eight hours, represent how viewers in general would rank the six super-resolution outputs; the paper reports winning rates but no inter-rater agreement, confidence intervals, or statistical test.

Editorial extensions

If this is right

  • Generative super-resolution leaderboards that rely on PSNR, SSIM, LPIPS, or MUSIQ may reward the wrong teams; future challenge reports should include a human preference stage or a metric calibrated to it.
  • Lightweight VQA is deployable at platform scale: the best Track 1 result was achieved at 47.39 GFLOPs with 33.01M parameters, well under the 120 GFLOPs ceiling.
  • The KwaiSR dataset gives the research community a shared test bed with both synthetic pairs and real low-quality images for short-form UGC super-resolution.
  • The top Track 1 teams used teacher-student pseudo-labeling or hybrid Mamba-attention designs to stay under the compute budget, so efficiency can come from training strategy rather than from shrinking a single network alone.

Reading between the lines

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

  • Editorial inference: the expert votes may be tracking a realism axis, namely texture plausibility and absence of generative artifacts, that fidelity-oriented metrics like PSNR and SSIM are structurally blind to; a metric that scores realism rather than reconstruction error would likely close part of the gap.
  • Editorial inference: if the inconsistency generalizes beyond five experts, the same divergence should appear in other generative restoration tasks such as face restoration, denoising, and video enhancement, where objective benchmarks similarly dominate.
  • Editorial inference: the six final outputs plus the expert preference judgments could be reused as training data for a lightweight reward model or ranking-based no-reference metric; whether such a metric beats MUSIQ on held-out expert choices is a direct, testable next step.
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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. This challenge report describes the NTIRE 2025 competition on short-form UGC video quality assessment and enhancement. Track 1 evaluates efficient no-reference VQA models on the KVQ dataset under a 120 GFLOPs constraint, and Track 2 introduces the KwaiSR dataset for diffusion-based single-image super-resolution, reporting objective quality metrics and a user study of the top six teams. The paper presents the leaderboard for Track 1, summarizes the architecture, training, and testing details of each of the 18 participating teams, and concludes that subjective preferences and objective metrics are noticeably inconsistent for generative SR on this content. The challenge repository is made publicly available, and the paper serves as a record of methods and results for the community.

Significance. If the reported results are reliable, the paper offers a useful community resource: a new S-UGC super-resolution dataset (KwaiSR, detailed in a companion paper), a public challenge repository, and a structured overview of 18 submitted methods under a strict efficiency budget. The Track 1 leaderboard, showing strong performance within 120 GFLOPs, is of practical interest for lightweight VQA deployment. The claimed subjective/objective inconsistency in Track 2, if supported by proper statistical evidence, would be an important caution against reading PSNR, LPIPS, or MUSIQ as proxies for user preference when comparing generative SR outputs. The paper is transparent about per-team training protocols and makes participant fact sheets available, which is commendable. However, the evidence for the central interpretive claim is currently thin, and the Track 1 out-of-sample comparison is compromised by at least one team explicitly using test-set pseudo-labels in training.

major comments (4)
  1. [Section 4.3 (ZX-AIE-Vector)] The training procedure described for ZX-AIE-Vector includes generating pseudo-labels for the KVQ test set, merging them with the training and validation data, and then fine-tuning the lightweight model on the refined pseudo-labeled test data using two training phases. This is a transductive use of the test set and means the reported test performance is not a clean out-of-sample evaluation. The paper does not state whether this practice was permitted by the challenge rules. Because Table 1 is the central deliverable of Track 1, the authors must disclose the rule and, if the practice was not allowed, recompute the leaderboard without this team. At minimum, a sensitivity analysis showing the ranking with and without test-set adaptation is needed.
  2. [Section 3 and Table 2] The claim that "current perceptual metrics may not reliably reflect perceptual quality" rests entirely on a user study described in a single sentence: five professional image-processing experts spent about eight hours each comparing the top six teams' outputs. No number of test images, no stimulus presentation protocol, no aggregation rule, no inter-rater agreement measure, no confidence intervals, and no significance test are reported. With only five raters and six teams, the differences between adjacent winning rates are small (e.g., 0.2775 vs. 0.2640 on synthetic; 0.1540 vs. 0.0947 on wild), so a single expert's preference flips several rank orders. Moreover, the raters were asked to select "the most visually convincing and realistic result," an expert-centric criterion that does not necessarily represent the general viewer population of short-form UGC platforms. The authors should either provide the full user-study protocol with statistical analysis (e.g., Fleiss' kappa, confidence intervals, or a test against chance) or substantially weaken the conclusion to a hypothesis rather than a finding.
  3. [Section 2 and Table 1] The Track 1 leaderboard is presented as a "Final Score" combining SROCC, PLCC, Rank1, and Rank2, but the paper never defines how this score is computed. The challenge description mentions coarse-grained quality scoring and fine-grained rankings for difficult samples, yet no formula, weighting, or aggregation rule is given. Without this definition, readers cannot interpret the rankings or reproduce the leaderboard. Please add the exact computation of Final Score, including how the fine-grained Rank1 and Rank2 components enter the score.
  4. [Table 2] The column header "User Study Score (objective)" is ambiguous: the first numeric column (e.g., 0.2775/0.3529) appears to be user-study winning rates, while the following columns are conventional objective metrics. The "Ranking (Objective)" column is not tied to any stated aggregation of PSNR, SSIM, LPIPS, MUSIQ, ManIQA, or CLIPIQA, and the text says the top six teams were shortlisted by objective metrics, yet objective scores are listed for all nine teams. Please restructure the table to clearly separate user-study results from objective metrics, define the objective ranking criterion, and state how the six teams were selected for the user study.
minor comments (6)
  1. [Section 2] The text states that KVQ contains "nine primary content scenarios" but then lists eight categories (landscape, crowd, person, food, portrait, computer graphic, caption, and stage). Please correct the count or add the missing category.
  2. [Section 1] The phrase "might inevitability suffer" should be "might inevitably suffer."
  3. [Abstract and Section 1] The repository URL "https://github.com/lixinustc/KVQE- Challenge-CVPR-NTIRE2025" contains a space after the hyphen; please provide a single, correct URL.
  4. [Appendix A and B] The organizer team blocks are titled "NTIRE2024 Organizers" although this is the NTIRE 2025 challenge; the year should be corrected.
  5. [Equation (1), Section 5.1] The loss definitions contain spacing and font inconsistencies, e.g., "Iper" appears both italic and non-italic, and the L1 term is written as "L1(f(Ipsnr,I per),I GT)". Please unify the notation.
  6. [Section 3] The sentence "The teams with top performances including SharpMind, ZQE, ZX-AIE-Vector, ECNU-SJTU VQA Team, and TenVQA achieved excellent results..." is informal; please rephrase to a measured description of the numerical results.

Circularity Check

1 steps flagged · score 4.0 of 10

One team's Track 1 test score is partly built from the test set itself via pseudo-label retraining; the paper's main subjective/objective inconsistency claim is empirical, not circular.

  1. fitted input called prediction [Section 4.3, ZX-AIE-Vector, Training details]
    "Next, they fine-tune a large-scale model on the KVQ dataset [51] (merging the official training and validation sets), and use it to generate pseudo-labels for the test set. These pseudo-labels are then merged with the original KVQ training and validation data to construct an augmented dataset. ... In the second-phase fine-tuning, they use the full KVQ training and validation sets along with the refined pseudo-labeled test data to retrain their lightweight model for an additional 30 epochs. ... During testing, they evaluate their final lightweight model on the KVQ test set."

    The team trains on the test set itself (via pseudo-labels) and then reports a 'test' score. The final evaluation on the KVQ test set is therefore not an out-of-sample prediction: the test inputs and their pseudo-labels were already seen during training, so the reported rank-3 final score (0.912) is partly constructed from the test data rather than independently predicted. The paper discloses this strategy, but the leaderboard entry is still a fitted input renamed as a prediction.

full rationale

The paper is primarily a competition report, so most results are measured benchmark outcomes rather than derived claims. The central interpretive statement in Section 3 and Table 2—that there is a noticeable inconsistency between subjective preferences and objective metrics—is an empirical comparison between an independent five-expert user study and objective scores; it is not forced by definition, and no equation makes the subjective ranking equal to the objective metrics. Its weakness is statistical (five experts, no inter-rater agreement or significance testing), not circular. Self-citations to KVQ and KwaiSR are dataset-provenance citations, and the datasets are public artifacts rather than load-bearing derivations. The one genuine circularity is ZX-AIE-Vector's use of pseudo-labeled test data in training followed by reporting of test performance, which makes that specific prediction reduce to a fitted input. Because this affects one team's leaderboard row rather than the paper's main scientific conclusion, the overall circularity score is moderate rather than high.

Assumptions & free parameters 0 free parameters · 2 assumptions · 0 invented entities

The paper introduces no mathematical model, fitted parameters, or new postulated entities. Its claims depend on the validity of the KVQ labels and the small user study, both listed as domain assumptions.

assumptions (2)
  • domain assumption KVQ ground-truth quality scores, annotated by professional researchers, are valid and representative for short-form UGC.
    All Track 1 leaderboard numbers (SROCC, PLCC) are correlations against these labels; if the labels are biased, the rankings are biased. Used throughout Section 3.
  • domain assumption The five-expert user study provides a reliable perceptual quality ranking.
    Track 2 subjective ranking and the inconsistency conclusion in Section 3 depend on this small panel being representative.

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Cite this review

Pith. "Pith review of NTIRE 2025 Challenge on Short-form UGC Video Quality Assessment and Enhancement: Methods and Results." pith.science (2026). https://pith.science/paper/7BZAGTZ6

@misc{pith2026250413131,
  author       = {Pith},
  title        = {Pith review of: NTIRE 2025 Challenge on Short-form UGC Video Quality Assessment and Enhancement: Methods and Results},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/7BZAGTZ6}},
  note         = {Machine review of arXiv:2504.13131}
}
read the original abstract

This paper presents a review for the NTIRE 2025 Challenge on Short-form UGC Video Quality Assessment and Enhancement. The challenge comprises two tracks: (i) Efficient Video Quality Assessment (KVQ), and (ii) Diffusion-based Image Super-Resolution (KwaiSR). Track 1 aims to advance the development of lightweight and efficient video quality assessment (VQA) models, with an emphasis on eliminating reliance on model ensembles, redundant weights, and other computationally expensive components in the previous IQA/VQA competitions. Track 2 introduces a new short-form UGC dataset tailored for single image super-resolution, i.e., the KwaiSR dataset. It consists of 1,800 synthetically generated S-UGC image pairs and 1,900 real-world S-UGC images, which are split into training, validation, and test sets using a ratio of 8:1:1. The primary objective of the challenge is to drive research that benefits the user experience of short-form UGC platforms such as Kwai and TikTok. This challenge attracted 266 participants and received 18 valid final submissions with corresponding fact sheets, significantly contributing to the progress of short-form UGC VQA and image superresolution. The project is publicly available at https://github.com/lixinustc/KVQE- ChallengeCVPR-NTIRE2025.

Figures

Figures reproduced from arXiv: 2504.13131 by the authors.

Figure 1
Figure 1. Enhancement results by Kwai-LPM (Large Processing Model), which is a diffusion-based SR method. 3. Challenge Results The challenge results are presented in [PITH_FULL_IMAGE:figures/full_fig_p003_1.png] view at source ↗
Figure 2
Figure 2. A comparison of the subjective quality between six teams on the synthetic dataset part: Example 1. [PITH_FULL_IMAGE:figures/full_fig_p005_2.png] view at source ↗
Figure 3
Figure 3. A comparison of the subjective quality between six teams on the synthetic dataset part: Example 2. [PITH_FULL_IMAGE:figures/full_fig_p006_3.png] view at source ↗
Figures from the paper (13 more)
Figure 4
Figure 4. Figure 4: A comparison of the subjective quality between six teams on the wild dataset part: Example 1. [PITH_FULL_IMAGE:figures/full_fig_p007_4.png]
Figure 5
Figure 5. Figure 5: A comparison of the subjective quality between six teams on the wild dataset part: Example 2. [PITH_FULL_IMAGE:figures/full_fig_p008_5.png]
Figure 7
Figure 7. Figure 7: The overall framework of Team ZQE. they generate 2,000 originals from the web-sourced videos and compress them using H.264 at 6 levels [51]. RQ-VQA is used to generate pseudo-labels for all videos. Finally, they pretrain E-VQA on this dataset using fidelity loss [75] a…
Figure 10
Figure 10. Figure 10: The overall framework of Team GoldenChef. [PITH_FULL_IMAGE:figures/full_fig_p009_10.png]
Figure 9
Figure 9. Figure 9: The overall framework of Team ECNU-SJTU. [PITH_FULL_IMAGE:figures/full_fig_p009_9.png]
Figure 11
Figure 11. Figure 11: The overall framework of Team DAIQAM. from each frame, a projector module is applied to reduce the feature dimensionality. The features from all frames are then concatenated and passed through a multilayer percep￾tron (MLP) for quality regression. Training details Dur…
Figure 13
Figure 13. Figure 13: The overall framework of Team PiNAFusion-SR. [PITH_FULL_IMAGE:figures/full_fig_p011_13.png]
Figure 12
Figure 12. Figure 12: The overall framework of Team Nourayn. Their model combines spatial feature extraction with a pre￾trained ResNet-50 backbone and Faster-RNN, alongside temporal modeling using a Bidirectional LSTM (BiLSTM). The overall design aims to predict the Mean Opinion Score (MOS…
Figure 14
Figure 14. Figure 14: The framework proposed by Team RealismDiff. [PITH_FULL_IMAGE:figures/full_fig_p012_14.png]
Figure 15
Figure 15. Figure 15: Overall Pipeline of the solution of Team SRlab. [PITH_FULL_IMAGE:figures/full_fig_p012_15.png]
Figure 17
Figure 17. Figure 17: The framework of Team ZigZagSeeSR [PITH_FULL_IMAGE:figures/full_fig_p014_17.png]
Figure 16
Figure 16. Figure 16: The overall pipeline of the method proposed by Team [PITH_FULL_IMAGE:figures/full_fig_p014_16.png]
Figure 18
Figure 18. Figure 18: The framework proposed by Team BVIVSR. encoder Eφ is responsible for extracting deep latent fea￾tures from the input low-resolution image. Dρ consists of a multi-scale hierarchical encoding module, multiple multi￾head linear attention blocks, and MLPs. Its hierarchica…

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    Kai Zhao, Kun Yuan, Ming Sun, Mading Li, and Xing Wen. Quality-aware pre-trained models for blind image quality assessment. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pages 22302– 22313, 2023. 2

    2023

Pith tools

Reviewed August 16, 2026 · model on record in the stance chip above.