Video Quality Assessment: A Comprehensive Survey

Alan C. Bovik; Chia-Ju Chen; Jiaming Liu; Leilei Huang; Qi Zheng; Shuo Xing; Tianyu Zhu; Xiongkuo Min; Yibo Fan; Zhengzhong Tu

REVIEW 3 major objections 4 minor 2 cited by

Video Quality Assessment: A Comprehensive Survey

T0 review · 3 major / 4 minor · reviewed 2026-08-11 · deepseek-v4-flash

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Pith's one-line read Video quality assessment has shifted from handcrafted statistical metrics to deep and multimodal models, and this survey benchmarks that shift on user-generated and AI-generated video, finding the learned models ahead except on temporal…

desk verdict Useful, current VQA survey, but the benchmark tables mix protocols and need provenance before the comparative claims can be trusted. read the letter →

arxiv 2412.04508 v2 pith:GLB73QR6 submitted 2024-12-04 eess.IV cs.CV

Qi Zheng , Yibo Fan , Leilei Huang , Tianyu Zhu , Jiaming Liu , Zhijian Hao , Shuo Xing , Chia-Ju Chen

show 3 more authors

Xiongkuo Min Alan C. Bovik Zhengzhong Tu

This is my paper · ORCID

classification eess.IVcs.CV

keywords videoqualityassessmentsubjectivestudiesfull-referenceVQAno-referencedeeplearninglargemultimodalmodelsuser-generatedcontentAI-generated

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 survey tries to establish a reliable map of the current video quality assessment field: how subjective databases are built, how full-reference and no-reference algorithms have evolved, and which models actually predict human judgments best on emerging content. Its central comparative claim is that deep learning and large multimodal models now outperform traditional handcrafted statistical metrics on user-generated and AI-generated video, while several handcrafted full-reference models remain competitive on temporal distortions such as frame-rate variation. If the map is accurate, it tells streaming platforms and codec designers where to place their bets: learned no-reference models for real-world UGC, multimodal models for AIGC, and bespoke temporal models for high-frame-rate and frame-interpolated content. The survey also argues that data scarcity and the cost of subjective studies are the main limits on further progress.

What carries the argument

The device that carries the survey's argument is a double taxonomy plus a comparative benchmark. The taxonomy separates subjective studies and databases from objective algorithms, and within objective algorithms separates full-reference from no-reference and knowledge-driven from deep learning-based; within deep models it separates temporal pooling, 3D CNNs, transformers, and large multimodality models. The benchmark then maps representative models onto databases with legacy, UGC, and AIGC content, using SROCC and PLCC, two rank and linear correlation measures against human mean opinion scores. This machinery lets the authors convert a literature review into a performance map, and it is also the point where the survey's assumptions are concentrated.

What would settle it

Re-run every model in Tables V and VI on the same databases under one fixed protocol: identical train and test splits, identical preprocessing and frame sampling, identical nonlinear mapping to MOS, and identical subject subsets. If the resulting SROCC and PLCC rankings differ materially from the tables, the survey's comparative insights and design recommendations are not supported.

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Extended reading notes

Core claim

In the paper's own framing, the field of video quality assessment has shifted from measuring predefined distortion properties to learning quality from human-labeled video, and its benchmark section is the evidence. The survey classifies objective models into knowledge-driven versus deep learning-based categories, then reports SROCC and PLCC correlations with human scores for representative full-reference and no-reference models on five FR databases and five NR databases, including user-generated and AI-generated content. On those tables, transformer-based and large-multimodality models such as FAST-VQA, DOVER, COVER, MaxVQA, Q-Align, and LMM-VQA show the highest correlations on UGC and AIGC, while knowledge-driven models such as VMAF and ST-GREED remain strong on traditional and temporal distortions. The paper's stated conclusion is that effective temporal modeling and the integration of multimodal priors are the two most productive directions for future VQA.

Load-bearing premise

The paper's comparative conclusions rest on the premise that the SROCC and PLCC numbers in its benchmark tables are directly comparable across models, even though they are taken from different papers with unstated differences in training splits and preprocessing.

Editorial extensions

If this is right

For user-generated content, the survey implies practical blind quality monitoring should use deep or transformer-based no-reference models rather than frame-level image metrics, since the benchmark shows knowledge-driven IQA models performing poorly on video.
For AI-generated video, multimodal models and prompt-based scoring are the only tested family that tracks human judgment across both fidelity and text alignment, so AIGC evaluation is likely to be built around them.
For high-frame-rate and frame-interpolated video, general-purpose metrics are insufficient; the survey's tables show bespoke temporal models like ST-GREED and FloLPIPS winning those databases, so codec and interpolation evaluation should use task-specific metrics.
Temporal aggregation is not optional: the survey's comparison indicates that simple frame score averaging fails, and memory-aware pooling or recurrent or temporal modules are needed to match human perception.
If these rankings hold, adopting the leading learned metrics as loss functions in coding and enhancement pipelines should improve perceptual optimization beyond what SSIM and VMAF-based losses achieve today.

Reading between the lines

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

The benchmark's cross-paper numbers leave an open question: because SROCC and PLCC values are drawn from original papers where training splits and preprocessing differ, the exact ordering of models is less certain than the broad split between handcrafted and learned approaches; a unified re-evaluation could shift positions within each family.
A natural testable extension would be an AIGC benchmark that holds the generator, prompt set, and frame rate fixed while varying only the VQA model, to isolate how much of the large-multimodal-model advantage comes from text alignment versus raw fidelity scoring.
The survey's emphasis on temporal distortion suggests that future gains on UGC and AIGC may come from explicit motion and memory modules rather than from larger spatial backbones, a hypothesis one could test by ablating temporal modules while holding backbone size fixed.
The dual demands of AIGC quality, perceptual fidelity and prompt-video alignment, may require VQA to split into two scores rather than one mean opinion score, extending the aesthetic and technical decomposition the survey documents in DOVER.

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Editorial analysis

A structured set of objections, weighed in public.

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

Desk Editor's Note

read the letter

Colleague — here is my take.

The paper is a genuinely useful survey. Its scope is current and broad: it covers subjective databases across legacy, UGC, and AIGC content, organizes FR and NR algorithms by architecture and temporal-modeling strategy, includes recent LMM-based methods, and discusses applications like transcoding and live streaming. The taxonomy diagrams and dataset tables are a convenient entry point. For a survey, the absence of a new algorithm or dataset is not a flaw; its stated contribution is synthesis, and the synthesis is mostly coherent.

The soft spots are real, and I agree with the stress-test that the benchmark section is the weakest link. Tables V and VI are the only new empirical content, yet the protocol is under-specified. The paper says performance data was taken from original papers when available and otherwise computed by the authors, but it does not say which cells came from where, nor report train/test splits, fine-tuning, frame sampling, temporal pooling, or the logistic fit used for PLCC. With numbers mixed across protocols, the comparative gaps (e.g., DeepQA at 0.0815 SROCC on LIVE-YT-HFR while LPIPS scores 0.6920; SAMA at 0.0136 on T2VQA-DB while DOVER scores 0.7609) cannot be read as pure model-quality differences. That does not make the benchmark fraudulent, but it does make the design insights in Section V less reliable than the paper implies. This is fixable with disclosed provenance and released evaluation code.

There are also smaller reliability problems. Citation numbering is inconsistent (SSIM appears as [13] and [164], VMAF as [171] and [173]), and typos are scattered through the text. For a reference survey, that erodes trust.

What holds up: the taxonomy is coherent, the dataset catalog is informative, and I found no sign of fabricated results. The overall direction of the comparison—transformers and LMMs lead on UGC/AIGC, knowledge-driven FR models remain competitive on temporal distortions—is plausible, just not yet reproducible.

I would send this to peer review. The right referee ask would be to disclose the provenance of every number in Tables V and VI and to clean up the reference numbering. After that, I would be happy to cite it as a survey and hand it to a student.

Referee Report

3 major / 4 minor

Summary. The manuscript is a survey of video quality assessment (VQA), covering subjective study methodology and VQA databases, full-reference and no-reference objective models, deep learning architectures, loss functions, benchmark comparisons on legacy/UGC/AIGC content, and applications and challenges. Its stated central claim is that it provides a comprehensive survey of recent progress in VQA algorithms and of the benchmarking studies and databases that support them. The only novel empirical contribution is the performance comparison in Tables V and VI, which reports SROCC/PLCC for representative FR and NR image/video quality models across five FR databases and five NR databases.

Significance. If the taxonomy and the benchmark comparison are reliable, this would be a useful reference for the VQA community: the paper covers a very broad literature, including recent transformer-based and large-multimodality-model methods, summarizes a large number of subjective databases including AIGC databases, and provides design-oriented observations. It also makes a public GitHub repository available. However, the survey's reliability is currently weakened by two load-bearing problems: the evaluation protocol underlying Tables V and VI is not specified, and the citation numbering is internally inconsistent in ways that make it difficult to trace claims to sources. The paper does not ship machine-checked proofs or reproducible evaluation code for the benchmark, so the comparison must be assessed on the strength of the protocol description, which is currently insufficient.

major comments (3)

[Section V-B, Tables V and VI] The only empirical contribution of the paper is the cross-model comparison in Tables V and VI, and the conclusions in Sections V-C and V-D (e.g., that transformer-based and LMM-based models "demonstrate the most excellent performance") depend directly on the comparability of the SROCC/PLCC values. Section V-B states only that "if available, performance data was taken from the original papers, otherwise we conducted the evaluation," without specifying train/test splits, whether image models were used off-the-shelf or fine-tuned, frame sampling and temporal pooling, preprocessing, or the logistic fitting used for PLCC. Some entries in the tables are themselves implausible under any single protocol: for example, Table V reports DeepQA at 0.0815 SROCC on LIVE-YT-HFR while LPIPS reports 0.6920, and Table VI reports SAMA at 0.0136 SROCC on T2VQA-DB while DOVER reports 0.7609. These gaps are far more likely to reflect different evaluation protocols than model quality. The authors should provide a per-entry provenance for every number in Tables V and VI, release the evaluation code and checkpoints, and either rerun all models under one protocol or clearly mark the provenance and add caveats where numbers are not comparable.
[Throughout; e.g., Sections II-A, II-B, IV-A and Fig. 7] The citation numbering is internally inconsistent, which is load-bearing for a survey whose purpose is to let readers trace claims. SSIM is cited as [13] in Section II-A but as [164] in Section IV-A; MS-SSIM is cited as [13] in Section II-A and as [14] in Fig. 7; VMAF is cited as [43] in Section II-B, as [171] in Section IV-A Type iv, and as [173] in the same subsection; ST-GREED is cited as [137] in Table V and as [138] in Section IV-A; MC-SSIM is cited as [166] in the text and as [171] in Fig. 7; 3D-SSIM is cited as [167] in the text and as [172] in Fig. 7. A reader cannot reliably identify which reference a number or claim belongs to, so the "comprehensive survey" claim is weakened. The manuscript needs a systematic reference audit before it can be considered publication-ready.
[Section V-C and V-D] The design insights drawn from Tables V and VI go beyond what the data can support given the protocol described. For example, the observation that "deep learning-based IQA models perform reasonably well on general distortion datasets" and the advice to prefer temporal NN modules are based on a small, non-random selection of models and on numbers whose protocol is opaque. Even if the protocol problem were fixed, the paper should temper these statements by noting the small number of databases, the content overlap among them, and the fact that many entries are copied from papers with different training regimes.

minor comments (4)

[Section IV-C, Eq. (5)] Equation (5) writes the monotonicity loss as a double sum over i only (both summation indices are i), whereas the pair term L_ij^rank in Eq. (3) requires two distinct indices; the second index should be j.
[Section V-A, Eq. (12)] The logistic function for nonlinear regression appears malformed: the expression exp{(-x + beta_3/|beta_4|}) has mismatched parentheses and the placement of the fraction is unclear. Please rewrite it in standard form, e.g., f(x) = beta_2 + (beta_1 - beta_2)/(1 + exp((x - beta_3)/|beta_4|)).
[Throughout] There are numerous typographical errors that should be corrected in a final pass, including "percieved" (Introduction), "seventies" for "severities" (Section III-A1), "sucecesses" (Section II-D), "correspondance" (Section II-E), "insterest" (Section IV-B1), "histgram" (Fig. 7), "Pre-processig" (Table I), "A VC" for "AVC" (Section III-B3), and "spaital" for "spatial" (Table III). These do not block the scientific content but they do reduce the professionalism of the manuscript.
[Section V-B] Please define the meaning of "-/-" entries in Tables V and VI (not evaluated, not reported in the original paper, or not applicable) and add a legend explaining that italic and orthographic fonts distinguish IQA from VQA models, since the table captions currently rely on the reader inferring this convention.

Circularity Check

0 steps flagged · score 0.0 of 10

No circularity: the survey synthesizes external literature and its only novel contribution, the benchmark tables, is explicitly grounded in external reported results rather than in any self-derived or self-fitted quantity.

full rationale

This paper is a comprehensive survey of video quality assessment algorithms, databases, and loss functions. It introduces no predictive model, no fitted parameter, and no derived quantity of its own. Its organizational claims (taxonomies in Figures 2 and 7, method descriptions in Section IV, loss formulations in Section IV-C) are restatements or summaries of published external work, with standard equation definitions (L1, L2, ranking loss, SROCC/PLCC formulas) that are textbook definitions rather than derivation outcomes. The central comparative contribution, Tables V and VI in Section V, is explicitly described as collecting performance data from original papers with the authors conducting evaluation only when numbers were not available ('If available, performance data was taken from the original papers, otherwise we conducted the evaluation'). Gathering and tabulating reported SROCC/PLCC values is not a derivation, and no claim reduces by construction to an input. The paper's conclusions (e.g., transformer-based and LMM-based models perform strongly on UGC and AIGC content) are empirical summaries of those tabulated external numbers, not predictions manufactured from the paper's own definitions. Authors do cite their own prior works (RAPIQUE, VIQE, STS-QA, COVER, temporal pooling studies), but these citations are contextual references to the literature being surveyed and are not load-bearing justifications of any derivation or uniqueness claim; the survey's structure does not depend on any single self-citation being accepted. The skeptical concern about protocol comparability of mixed benchmark numbers is a validity or reproducibility issue, not a circularity issue, since the numbers are not produced by the surveyed paper's own fitted parameters. Accordingly, there is no circular step to quote and no step where an equation reduces to its own input.

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

This is a review paper, so there are no free parameters or invented entities. The main implicit assumption is that the external results cited, and the authors' own recomputations, are correct and directly comparable across different studies.

assumptions (1)

domain assumption The cited publications' reported results and the authors' own benchmark evaluations are accurate and comparable.
The survey's central value as a reference depends on the trustworthiness of the numbers it compiles in Tables V and VI. If these numbers are inaccurate or not comparable, the survey's guidance is misleading.

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Cite this review

Pith. "Pith review of Video Quality Assessment: A Comprehensive Survey." pith.science (2026). https://pith.science/paper/GLB73QR6

@misc{pith2026241204508,
  author       = {Pith},
  title        = {Pith review of: Video Quality Assessment: A Comprehensive Survey},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/GLB73QR6}},
  note         = {Machine review of arXiv:2412.04508}
}

read the original abstract

Video quality assessment (VQA) is an important processing task, aiming at predicting the quality of videos in a manner highly consistent with human judgments of perceived quality. Traditional VQA models based on natural image and/or video statistics, which are inspired both by models of projected images of the real world and by dual models of the human visual system, deliver only limited prediction performances on real-world user-generated content (UGC), as exemplified in recent large-scale VQA databases containing large numbers of diverse video contents crawled from the web. Fortunately, recent advances in deep neural networks and Large Multimodality Models (LMMs) have enabled significant progress in solving this problem, yielding better results than prior handcrafted models. Numerous deep learning-based VQA models have been developed, with progress in this direction driven by the creation of content-diverse, large-scale human-labeled databases that supply ground truth psychometric video quality data. Here, we present a comprehensive survey of recent progress in the development of VQA algorithms and the benchmarking studies and databases that make them possible. We also analyze open research directions on study design and VQA algorithm architectures. Github link: https://github.com/taco-group/Video-Quality-Assessment-A-Comprehensive-Survey.

Figures

Figures reproduced from arXiv: 2412.04508 by the authors.

**Figure 2.** Taxonomy of existing subjective and objective video quality assessment methods. [PITH_FULL_IMAGE:figures/full_fig_p003_2.png] view at source ↗

**Figure 3.** Example of a visual interface used when playing video. [PITH_FULL_IMAGE:figures/full_fig_p005_3.png] view at source ↗

Figures from the paper (8 more)

**Figure 6.** Figure 6: Samples of video contents selected from legacy datasets ((a)-(f)), UGC datasets ((g)-(l)), and AIGC datasets ((m)-(r)). [PITH_FULL_IMAGE:figures/full_fig_p008_6.png]

**Figure 7.** Figure 7: Classification and evolution of objective quality assessment models. The left figure presents framework diagrams for [PITH_FULL_IMAGE:figures/full_fig_p011_7.png]

**Figure 8.** Figure 8: Three types of IQA framework based on how they extract and employ multi-scale features: the parallel, bottom-up and [PITH_FULL_IMAGE:figures/full_fig_p013_8.png]

**Figure 9.** Figure 9: The framework of LPIPS [80]. to weight similarities across space and channels. Unlike the above bottom-up paradigm, TOPIQ [178] adopts a top-down approach for both FR and NR image quality assessment. Multi-scale features from the first five layers of ResNet-50 [193] ar…

**Figure 10.** Figure 10: The framework of CLIP-IQA [100]. quality-aware contrastive loss. Type vi: Large multimodality model-based IQA. CLIPIQA [100] assesses image quality by leveraging the vision-language prior in CLIP [8]. It uses cosine similarity between image embeddings and text embedd…

**Figure 11.** Figure 11: The framework of VSFA [74]. video-level quality predictions. • Varga et al. [73] employs AlexNet [189], InceptionV3 [268], and Inception-ResNet-V2 [269] pre-trained on KoNViD-10K for transfer learning. Frame-level features are extracted and processed through a two-la…

**Figure 12.** Figure 12: The framework of FAST-VQA [95]. between labeled source videos and unlabeled target videos. • Shen et al. [262] captures multi-scale motion through a spatiotemporal feature pyramid, with pyramid attention modules for channel and spatial selective sensitivity. Type iv:…

**Figure 13.** Figure 13: Application overview of video quality assessment. [PITH_FULL_IMAGE:figures/full_fig_p025_13.png]

Discussion (0). Continue with ORCID to comment.

Forward citations

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An agentic pipeline that plans and executes image restoration from a toolbox of pretrained models to upscale arbitrary images to 4K, reporting state-of-the-art results on many benchmarks.
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