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REVIEW 3 major objections 9 minor 91 references

MVQA: Mamba with Unified Sampling for Efficient Video Quality Assessment

T0 review · 3 major / 9 minor · reviewed 2026-08-16 · deepseek-v4-flash

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Pith's one-line read MVQA claims that a state-space (Mamba) encoder with mask-fused sampling matches or beats existing VQA methods while running about twice as fast and using a fraction of the GPU memory.

desk verdict Useful sampling idea with real efficiency gains, but the accuracy advantage is within noise and the 'first Mamba VQA' claim is overreaching. read the letter →

arxiv 2504.16003 v1 pith:ISYRXZ3T submitted 2025-04-22 cs.CV

Yachun Mi , Yu Li , Weicheng Meng , Chaofeng Chen , Chen Hui , Shaohui Liu This is my paper · ORCID
classification cs.CV
keywords videoqualityassessmentno-referencestatespacemodelsMambaunifiedsamplingsemanticinformationfragmentlinearcomplexity
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

MVQA sets out to show that a video quality assessment network can be built entirely from Mamba/state-space layers, without CNN or Transformer backbones, and still match or beat existing methods. Its sampling module, USDS, fuses two views of each frame into one tensor: high-resolution grid fragments that preserve distortion, and a downsampled full-frame version that preserves semantic content, placed in the bottom-right quadrant of every 32x32 block. Because the fused tensor is the same size as the fragment-only input, the semantic information costs no extra compute. Reported results put MVQA-middle's average SROCC at 0.901 after fine-tuning, above the best single-branch (0.867) and multi-branch (0.886) comparators, with MVQA-tiny at 34 GFLOPs and 0.028 s. If correct, this means efficient VQA does not need a semantic side branch or expensive backbones.

What carries the argument

The central mechanism is USDS's fixed mask fusion. A binary mask M marks the bottom-right 16x16 quadrant of every 32x32 block; the low-resolution frame is expanded into a canvas E and written into those quadrants, while the high-resolution fragments fill the other three quadrants, giving V_hat = E·M + V_tilde·(1−M). This keeps the fused tensor at the fragment-only resolution, so semantic content is added at zero marginal compute. The second piece is the Vision Mamba encoder, a bidirectional selective state-space encoder whose linear complexity in sequence length, combined with spatial and temporal position embeddings and a regression token, produces the final quality score.

What would settle it

Retrain MVQA-tiny on LSVQ with the USDS mask inverted so the low-resolution semantic view occupies the top-left quadrant of each block instead of the bottom-right, keeping everything else identical, and compare SROCC on LSVQ test; if the gain over Fragments disappears or reverses, the reported accuracy advantage is tied to the specific mask placement rather than to unified semantic-and-distortion sampling.

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Extended reading notes

Core claim

The paper claims to be the first to apply state-space models successfully to VQA, and argues that prior efficient sampling fails because fragment-based sampling discards semantics while resize/crop discard distortion. USDS fixes this by sampling high-resolution fragments for distortion and a low-resolution full-frame copy for semantics, then fusing them with a binary mask that reserves the bottom-right quadrant of each block for the low-resolution view. The MVQA encoder is a bidirectional Vision Mamba with spatial-first, temporal-next scanning and a regression token whose output is mapped by an MLP to a quality score. On LSVQ and three small datasets, MVQA-middle reports SROCC 0.898 on LSVQ test and 0.901 average after fine-tuning, while MVQA-tiny reports 34 GFLOPs and 0.028 s per 150-frame clip, roughly twice as fast as FAST-VQA. The paper takes these results to show that a single state-space branch can carry both technical and semantic quality.

Load-bearing premise

The load-bearing assumption is that a fixed 3:1 blend, three quadrants of high-resolution fragments plus one bottom-right quadrant of downsampled full-frame content, captures both distortion and semantics well enough at every resolution, since that ratio and mask position are hand-chosen and never ablated.

Editorial extensions

If this is right

  • If the reported numbers hold, a single SSM branch can replace the multi-branch design that current top VQA models use, where a technical quality branch is paired with a separate semantic or aesthetic branch.
  • USDS is architecture-independent: the paper's cross-architecture test shows Video Swin Transformer also improves with USDS over Fragments, so the sampling scheme could be dropped into other VQA backbones.
  • Because Mamba's complexity is linear in sequence length, the efficiency gap should widen for longer and higher-resolution videos, making the approach more attractive for 2K/4K streaming quality control.
  • MVQA-tiny's fixed 34 GFLOPs and 0.028 s per 150-frame clip, independent of input resolution, put real-time or batch VQA screening on a single GPU within reach.

Reading between the lines

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

  • The paper leaves untested whether the 3:1 mask ratio is optimal; rotating the mask or varying the semantic fraction could show that the accuracy gain is specific to the bottom-right placement.
  • One extension the paper does not explore is making the semantic quadrant content-aware, for example weighting the low-resolution view by a CLIP affinity score, which could matter for videos where semantics dominate quality.
  • Because Mamba scales linearly with sequence length, a natural next test is longer clips than the 32-frame input; the efficiency advantage would likely grow, but accuracy at long temporal horizons is unmeasured.
  • The same mask-fusion idea might transfer to other quality-related tasks beyond VQA, such as image aesthetic scoring, since it provides a cheap global-context channel alongside local detail.
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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

3 major / 9 minor

Summary. The paper proposes MVQA, a no-reference video quality assessment (VQA) network built entirely from Mamba state-space layers, together with a new sampling method called Unified Semantic and Distortion Sampling (USDS). USDS fuses grid-sampled high-resolution fragments with a downsampled full-frame view via a fixed binary mask, so the fused tensor has the same size as the fragment-only tensor and adds no computational overhead. The authors pre-train MVQA on LSVQ and evaluate on LSVQ, KoNViD-1k, LIVE-VQC, and YouTube-UGC, reporting that MVQA-middle achieves SROCC at or above existing single-branch and multi-branch methods, while MVQA-tiny runs roughly 2x faster than FAST-VQA and uses much less GPU memory. The paper also reports ablations showing USDS improves over fragment sampling in both the proposed MVQA backbone and a Video Swin Transformer.

Significance. If the empirical results are correct, the paper makes a useful contribution by demonstrating that a pure state-space backbone is viable for VQA and by providing a simple, structurally efficient way to inject global semantic context into fragment-based sampling. The efficiency advantage of the tiny variant is structurally guaranteed because USDS does not increase the input tensor size, and the cross-architecture validation in Table 5 is a valuable consistency check. The paper does not ship code, but the method is described in enough detail to reimplement once the notation and hyperparameter omissions are fixed. The main weakness is statistical: the reported accuracy gains over strong baselines are not accompanied by error bars, multiple seeds, or significance tests, and some gains are within the typical Spearman standard error for the dataset sizes involved. The lack of an ablation of the hand-chosen 3:1 fusion ratio also leaves a gap between the proposed design and the evidence for it.

major comments (3)
  1. [Section 4.2, Tables 1 and 3] The central accuracy claim is not statistically supported. The paper reports single-run SROCC and PLCC values without error bars, multiple training seeds, or significance tests. For example, on KoNViD-1k (N=1200) the approximate Spearman standard error is about 0.029, so the reported +0.022 SROCC gain of MVQA-middle over the best multi-branch baseline (0.925 vs 0.903) is within one standard error; the gains on LIVE-VQC (N=585, SE approx 0.041) and YouTube-UGC (N=1067, SE approx 0.031) are likewise within one standard error. Please provide repeated-run statistics (at least mean and standard deviation over several seeds) or a statistical comparison against the strongest baselines before claiming state-of-the-art accuracy.
  2. [Section 4.1 and Table 3] The fine-tuning protocol is underspecified. The text states that the model is fine-tuned on KoNViD-1k, LIVE-VQC, and YouTube-UGC but does not give the train/test split ratio, the number of epochs, or the learning-rate schedule used for fine-tuning, and it does not state whether the same splits as those used by the cited baselines were employed. Since Table 3 computes 'improvements' against published numbers, differences in the evaluation split can change the ranking; please specify the exact protocol and, if possible, reuse the standard publicly available splits used in prior work.
  3. [Section 3.3, Eq. (10)-(11) and Algorithm 1] The USDS fusion ratio is a hand-chosen design parameter that is never ablated. The mask in Eq. (11) places high-resolution fragments in three quadrants of each 32x32 block and a low-resolution full-frame patch in the bottom-right quadrant (a 3:1 blend), and this specific ratio and mask placement are not varied in any experiment. The ablation in Table 6 compares USDS as a whole against resize, crop, MRET, and fragments, but does not isolate the effect of the fusion ratio or mask location (e.g., other quadrants, 2:2, or 1:3 blends). Without such an ablation, it is unclear whether the reported gains come from the specific fusion design or merely from adding any global low-resolution context; please add experiments varying the ratio and mask placement.
minor comments (9)
  1. [Eq. (13)] Eq. (13) appears to contain a typo: 'T_l = Vim(T_{l-1} + T_{l-1})' would double the input to the vision Mamba block. Presumably the intended residual form is T_l = Vim(T_{l-1}) + T_{l-1}; please correct this.
  2. [Eq. (12)] The dimension broadcasting in Eq. (12) is unclear: a spatial embedding of length (H/16 * W/16 + 1) and a temporal embedding of length t are added to a token sequence of length T * (H/16 * W/16) + 1. Please explain how these embeddings are broadcast across the T frames.
  3. [Section 4.3 and Table 4] Please state whether the reported FLOPs and runtimes include the USDS sampling and preprocessing steps or only the backbone forward pass. If the preprocessing is excluded, the end-to-end speed advantage may be smaller than the '2x faster' claim suggests.
  4. [Abstract and Fig. 1] The '1/5 GPU memory' claim should be supported by a measurement table or numeric annotations giving the measurement conditions (batch size, input resolution, and whether the number is peak activation memory or total training memory). Fig. 1 currently shows panels without numeric memory values.
  5. [Abstract and Section 1] The claim 'this is the first work to successfully apply state-space models in the VQA domain' is very strong. Given the rapid growth of SSM-based vision methods, please verify against concurrent literature and either provide a thorough literature search or soften the claim to 'to the best of our knowledge'.
  6. [Section 4.4, Figs. 2 and 6] The CLIP-based semantic analysis is purely qualitative. Adding a quantitative measure (e.g., average CLIP cosine similarity between the sampled frames and the original frames) would make the semantic-preservation claim more concrete.
  7. [Section 4.5, Fig. 7] The resolution ablation is presented only as plots without numerical values. Please include the actual SROCC/PLCC numbers in the text or in a table so that the magnitude of the resolution-dependent improvement can be assessed.
  8. [Algorithm 1 and Eq. (10)] The notation in Eq. (10) and Algorithm 1 is inconsistent: the block indices are introduced as (k', l') in Eq. (10) but as (i, j) in Algorithm 1, and R_{k,l} uses undefined indices. Please unify the notation.
  9. [Section 4.1 and Eq. (17)] Please report the numerical values of the loss weights alpha and beta in Eq. (17) and the number of frames T after sampling (Table 2 suggests T=32), as these are needed for reproducibility.

Circularity Check

0 steps flagged · score 0.0 of 10

No significant circularity: MVQA's accuracy claims are tested against external benchmark labels with standard SROCC/PLCC metrics, and the USDS fusion is a fixed hand-designed sampling scheme rather than a fitted prediction target.

full rationale

MVQA's central accuracy claim is evaluated on external VQA benchmarks (LSVQ, KoNViD-1k, LIVE-VQC, YouTube-UGC) using SROCC and PLCC computed against human subjective scores, with baselines taken from the published literature. The USDS fusion in Eq. (11) is a fixed, hand-constructed mask blend of high-resolution grid fragments and a bilinearly downsampled frame; no parameter of the fusion mask or of the Mamba encoder is fitted to benchmark labels beyond ordinary supervised training, and no benchmark-derived quantity is relabeled as a prediction. The CLIP-based semantic check in Fig. 2 and Sec. 4.4 is an external zero-shot similarity probe used to illustrate information retention, not a term in the VQA loss, so it does not smuggle the target metric into the method. Citations to the authors' prior work, such as CLiF-VQA [48], support the general background point that semantic information is useful for VQA, but that point is not the load-bearing derivational premise for the reported SROCC/PLCC results. The claim of being 'the first work to successfully apply state-space models in the VQA domain' is a novelty assertion, not a derived prediction, and does not reduce to any fitted input. The absence of error bars or significance tests is a legitimate statistical robustness concern, but it is not circularity. Overall, the reported efficiency gains are structurally guaranteed by the fixed tensor size, and the accuracy comparisons are externally benchmarked, so the paper's derivation chain is self-contained with respect to its claims.

Assumptions & free parameters 3 free parameters · 4 assumptions · 0 invented entities

The central claims rest on standard deep-learning assumptions: fixed sampling hyperparameters (frames, patch size, mask ratio), loss weights that are not reported, and benchmark comparability to published baselines. No new physical or mathematical entities are introduced; the main unstated design choices are the USDS fusion ratio and the Mamba encoder configuration.

free parameters (3)
  • Loss weights alpha and beta (Eq. 17) = not specified
    The total loss L = alpha * L_mon + beta * L_lin requires two scalar weights that are presumably tuned but their values are not reported in the paper.
  • USDS low-resolution fusion ratio = 1/4 of each 2x2 block (bottom-right quadrant)
    The mask in Eq. (11) allocates 3/4 of each block to high-resolution fragments and 1/4 to low-resolution semantic content; this ratio is chosen by hand and no ablation studies it.
  • Input fragment grid and frame length = 32 frames, 224x224 spatial (14x14 patches of 16x16)
    The sampling target resolution and number of frames are fixed hyperparameters inherited from fragment-sampling baselines; their effect on the efficiency-accuracy trade-off is not ablated.
assumptions (4)
  • domain assumption Mamba state-space models provide linear-complexity long-range modeling that transfers to video quality assessment.
    Invoked in Section 1 and Section 3.2 to justify replacing CNN/Transformer encoders; the paper validates this empirically but does not prove it.
  • domain assumption The standard monotonicity and linearity losses (Eqs. 15-17) improve quality prediction accuracy.
    Taken from prior VQA literature in Section 3.5; adopted without re-derivation.
  • domain assumption CLIP zero-shot semantic similarity is a valid proxy for the semantic information retained by a sampling strategy.
    Used in Fig. 2 and Section 4.4 as validation that USDS preserves semantics; the metric is heuristic and not calibrated to VQA accuracy.
  • standard math Zero-Order Hold discretization of SSM (Eqs. 3-4) is standard and correct.
    Background formulation from Mamba/S4 literature in Section 3.1.

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Cite this review

Pith. "Pith review of MVQA: Mamba with Unified Sampling for Efficient Video Quality Assessment." pith.science (2026). https://pith.science/paper/ISYRXZ3T

@misc{pith2026250416003,
  author       = {Pith},
  title        = {Pith review of: MVQA: Mamba with Unified Sampling for Efficient Video Quality Assessment},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/ISYRXZ3T}},
  note         = {Machine review of arXiv:2504.16003}
}
abstract

The rapid growth of long-duration, high-definition videos has made efficient video quality assessment (VQA) a critical challenge. Existing research typically tackles this problem through two main strategies: reducing model parameters and resampling inputs. However, light-weight Convolution Neural Networks (CNN) and Transformers often struggle to balance efficiency with high performance due to the requirement of long-range modeling capabilities. Recently, the state-space model, particularly Mamba, has emerged as a promising alternative, offering linear complexity with respect to sequence length. Meanwhile, efficient VQA heavily depends on resampling long sequences to minimize computational costs, yet current resampling methods are often weak in preserving essential semantic information. In this work, we present MVQA, a Mamba-based model designed for efficient VQA along with a novel Unified Semantic and Distortion Sampling (USDS) approach. USDS combines semantic patch sampling from low-resolution videos and distortion patch sampling from original-resolution videos. The former captures semantically dense regions, while the latter retains critical distortion details. To prevent computation increase from dual inputs, we propose a fusion mechanism using pre-defined masks, enabling a unified sampling strategy that captures both semantic and quality information without additional computational burden. Experiments show that the proposed MVQA, equipped with USDS, achieve comparable performance to state-of-the-art methods while being $2\times$ as fast and requiring only $1/5$ GPU memory.

Figures

Figures reproduced from arXiv: 2504.16003 by the authors.

Figure 1
Figure 1. Performance and efficiency comparisons between FAST [PITH_FULL_IMAGE:figures/full_fig_p001_1.png] view at source ↗
Figure 2
Figure 2. Comparison of semantic information retained by USDS [PITH_FULL_IMAGE:figures/full_fig_p002_2.png] view at source ↗
Figure 3
Figure 3. The framework of our proposed method. (a) The USDS consists of three distinct but interrelated stages: distortion details [PITH_FULL_IMAGE:figures/full_fig_p004_3.png] view at source ↗
Figures from the paper (3 more)
Figure 4
Figure 4. Figure 4: Comparison of USDS with crop, resize, MRET [ [PITH_FULL_IMAGE:figures/full_fig_p005_4.png]
Figure 7
Figure 7. Figure 7: Ablation study on different resolutions. [PITH_FULL_IMAGE:figures/full_fig_p008_7.png]
Figure 6
Figure 6. Figure 6: Results of semantic analysis experiments. [PITH_FULL_IMAGE:figures/full_fig_p008_6.png]

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Reference graph

Works this paper leans on

91 extracted references · 64 canonical work pages

  1. [1]

    No-reference video quality assessment based on visual memory modeling

    Mehdi Banitalebi-Dehkordi, Abbas Ebrahimi-Moghadam, Morteza Khademi, and Hadi Hadizadeh. No-reference video quality assessment based on visual memory modeling. IEEE Trans. Broadcast., 66(3):676–689, 2019. 1

    2019

  2. [2]

    Learning generalized spatial-temporal deep feature representation for no-reference video quality as- sessment

    Baoliang Chen, Lingyu Zhu, Guo Li, Fangbo Lu, Hongfei Fan, and Shiqi Wang. Learning generalized spatial-temporal deep feature representation for no-reference video quality as- sessment. IEEE TCSVT, 32(4):1903–1916, 2021. 1, 2, 7

    1903

  3. [3]

    Video mamba suite: State space model as a ver- satile alternative for video understanding

    Guo Chen, Yifei Huang, Jilan Xu, Baoqi Pei, Zhe Chen, Zhiqi Li, Jiahao Wang, Kunchang Li, Tong Lu, and Limin Wang. Video mamba suite: State space model as a ver- satile alternative for video understanding. arXiv preprint arXiv:2403.09626, 2024. 3

    arXiv 2024

  4. [4]

    No-reference video quality assessment using natural spa- tiotemporal scene statistics

    Sathya Veera Reddy Dendi and Sumohana S Channappayya. No-reference video quality assessment using natural spa- tiotemporal scene statistics. IEEE TIP, 29:5612–5624, 2020. 1

    2020

  5. [5]

    An image is worth 16x16 words: Trans- formers for image recognition at scale

    Alexey Dosovitskiy, Lucas Beyer, Alexander Kolesnikov, Dirk Weissenborn, Xiaohua Zhai, Thomas Unterthiner, Mostafa Dehghani, Matthias Minderer, Georg Heigold, Syl- vain Gelly, et al. An image is worth 16x16 words: Trans- formers for image recognition at scale. arXiv preprint arXiv:2010.11929, 2020. 1, 2, 3

    arXiv 2010

  6. [6]

    Chipqa: No-reference video quality prediction via space-time chips

    Joshua Peter Ebenezer, Zaixi Shang, Yongjun Wu, Hai Wei, Sriram Sethuraman, and Alan C Bovik. Chipqa: No-reference video quality prediction via space-time chips. IEEE TIP, 30:8059–8074, 2021. 1

    2021

  7. [7]

    Hungry hungry hippos: Towards language modeling with state space mod- els

    Daniel Y Fu, Tri Dao, Khaled Kamal Saab, Armin W Thomas, Atri Rudra, and Christopher Re. Hungry hungry hippos: Towards language modeling with state space mod- els. In ICLR. 2, 3

  8. [8]

    Aesmamba: Universal image aesthetic assessment with state space models

    Fei Gao, Yuhao Lin, Jiaqi Shi, Maoying Qiao, and Nannan Wang. Aesmamba: Universal image aesthetic assessment with state space models. In ACM Multimedia 2024, 2024. 2

    2024

Show all 91 references
  1. [9]

    Learning enriched features via selective state spaces model for efficient image deblurring

    Hu Gao, Bowen Ma, Ying Zhang, Jingfan Yang, Jing Yang, and Depeng Dang. Learning enriched features via selective state spaces model for efficient image deblurring. In Pro- ceedings of the 32nd ACM International Conference on Mul- timedia, pages 710–718, 2024. 3

    2024

  2. [10]

    Konvid-150k: A dataset for no-reference video qual- ity assessment of videos in-the-wild

    Franz G ¨otz-Hahn, Vlad Hosu, Hanhe Lin, and Dietmar Saupe. Konvid-150k: A dataset for no-reference video qual- ity assessment of videos in-the-wild. IEEE Access, 9:72139– 72160, 2021. 2

    2021

  3. [11]

    Mamba: Linear-time sequence modeling with selective state spaces

    Albert Gu and Tri Dao. Mamba: Linear-time sequence modeling with selective state spaces. arXiv preprint arXiv:2312.00752, 2023. 2, 3

    2023 arXiv

  4. [12]

    Efficiently modeling long sequences with structured state spaces

    Albert Gu, Karan Goel, and Christopher R ´e. Efficiently modeling long sequences with structured state spaces. arXiv preprint arXiv:2111.00396, 2021. 2, 3

    2021 arXiv

  5. [13]

    Combining recurrent, convolutional, and continuous-time models with linear state space layers

    Albert Gu, Isys Johnson, Karan Goel, Khaled Saab, Tri Dao, Atri Rudra, and Christopher R ´e. Combining recurrent, convolutional, and continuous-time models with linear state space layers. NeurIPS, 34:572–585, 2021. 2, 3

    2021

  6. [14]

    Mambair: A simple baseline for image restoration with state-space model

    Hang Guo, Jinmin Li, Tao Dai, Zhihao Ouyang, Xudong Ren, and Shu-Tao Xia. Mambair: A simple baseline for image restoration with state-space model. In ECCV, pages 222–241. Springer, 2025. 2

    2025

  7. [15]

    Mambair: A simple baseline for image restoration with state-space model

    Hang Guo, Jinmin Li, Tao Dai, Zhihao Ouyang, Xudong Ren, and Shu-Tao Xia. Mambair: A simple baseline for image restoration with state-space model. In ECCV, pages 222–241. Springer, 2025. 3

    2025

  8. [16]

    Learn- ing spatio-temporal features with 3d residual networks for action recognition

    Kensho Hara, Hirokatsu Kataoka, and Yutaka Satoh. Learn- ing spatio-temporal features with 3d residual networks for action recognition. In ICCV Workshops, pages 3154–3160,

  9. [17]

    Can spatiotemporal 3d cnns retrace the history of 2d cnns and imagenet? In CVPR, pages 6546–6555, 2018

    Kensho Hara, Hirokatsu Kataoka, and Yutaka Satoh. Can spatiotemporal 3d cnns retrace the history of 2d cnns and imagenet? In CVPR, pages 6546–6555, 2018. 1, 2

    2018

  10. [18]

    Image sharpness assessment based on local phase coherence

    Rania Hassen, Zhou Wang, and Magdy MA Salama. Image sharpness assessment based on local phase coherence. IEEE TIP, 22(7):2798–2810, 2013. 2

    2013

  11. [19]

    Deep residual learning for image recognition

    Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun. Deep residual learning for image recognition. In CVPR, pages 770–778, 2016. 1, 2, 3

    2016

  12. [20]

    Identity mappings in deep residual networks

    Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun. Identity mappings in deep residual networks. In ECCV, pages 630–645. Springer, 2016. 1, 2

    2016

  13. [21]

    The konstanz natural video database (konvid-1k)

    Vlad Hosu, Franz Hahn, Mohsen Jenadeleh, Hanhe Lin, Hui Men, Tam´as Szir ´anyi, Shujun Li, and Dietmar Saupe. The konstanz natural video database (konvid-1k). In QoMEX, pages 1–6. IEEE, 2017. 2, 6

    2017

  14. [22]

    Localmamba: Visual state space model with windowed selective scan

    Tao Huang, Xiaohuan Pei, Shan You, Fei Wang, Chen Qian, and Chang Xu. Localmamba: Visual state space model with windowed selective scan. arXiv preprint arXiv:2403.09338,

    arXiv

  15. [23]

    Efficient movie scene detection using state-space transformers

    Md Mohaiminul Islam, Mahmudul Hasan, Kishan Shamsun- dar Athrey, Tony Braskich, and Gedas Bertasius. Efficient movie scene detection using state-space transformers. In CVPR, pages 18749–18758, 2023. 3

    2023

  16. [24]

    A new approach to linear filtering and prediction problems

    Rudolph Emil Kalman. A new approach to linear filtering and prediction problems. 1960. 3

    1960

  17. [25]

    Convolu- tional neural networks for no-reference image quality assess- ment

    Le Kang, Peng Ye, Yi Li, and David Doermann. Convolu- tional neural networks for no-reference image quality assess- ment. In CVPR, pages 1733–1740, 2014. 2

    2014

  18. [26]

    Simultane- ous estimation of image quality and distortion via multi-task convolutional neural networks

    Le Kang, Peng Ye, Yi Li, and David Doermann. Simultane- ous estimation of image quality and distortion via multi-task convolutional neural networks. In ICIP, pages 2791–2795. IEEE, 2015. 2

    2015

  19. [27]

    The kinetics hu- man action video dataset

    Will Kay, Joao Carreira, Karen Simonyan, Brian Zhang, Chloe Hillier, Sudheendra Vijayanarasimhan, Fabio Viola, Tim Green, Trevor Back, Paul Natsev, et al. The kinetics hu- man action video dataset. arXiv preprint arXiv:1705.06950,

    arXiv

  20. [28]

    Musiq: Multi-scale image quality transformer

    Junjie Ke, Qifei Wang, Yilin Wang, Peyman Milanfar, and Feng Yang. Musiq: Multi-scale image quality transformer. In ICCV, pages 5148–5157, 2021. 2

    2021

  21. [29]

    Mret: Multi-resolution transformer for video quality assessment

    Junjie Ke, Tianhao Zhang, Yilin Wang, Peyman Milanfar, and Feng Yang. Mret: Multi-resolution transformer for video quality assessment. Frontiers in Signal Processing , 3:1137006, 2023. 3, 5, 8

    2023

  22. [30]

    Two-level approach for no-reference con- sumer video quality assessment

    Jari Korhonen. Two-level approach for no-reference con- sumer video quality assessment. IEEE TIP, 28(12):5923– 5938, 2019. 1, 2, 6, 7 9

    2019

  23. [31]

    Blind natural video quality prediction via statistical temporal features and deep spatial features

    Jari Korhonen, Yicheng Su, and Junyong You. Blind natural video quality prediction via statistical temporal features and deep spatial features. In ACM MM, pages 3311–3319, 2020. 7

    2020

  24. [32]

    No-reference quality assessment of tone- mapped hdr pictures

    Debarati Kundu, Deepti Ghadiyaram, Alan C Bovik, and Brian L Evans. No-reference quality assessment of tone- mapped hdr pictures. IEEE TIP , 26(6):2957–2971, 2017. 2

    2017

  25. [33]

    Blindly assess quality of in-the-wild videos via quality-aware pre-training and motion perception

    Bowen Li, Weixia Zhang, Meng Tian, Guangtao Zhai, and Xianpei Wang. Blindly assess quality of in-the-wild videos via quality-aware pre-training and motion perception. IEEE TCSVT, 32(9):5944–5958, 2022. 1, 2, 6, 7

    2022

  26. [34]

    Quality as- sessment of in-the-wild videos

    Dingquan Li, Tingting Jiang, and Ming Jiang. Quality as- sessment of in-the-wild videos. In ACM MM, pages 2351– 2359, 2019. 1, 2, 3, 6, 7

    2019

  27. [35]

    Videomamba: State space model for efficient video understanding

    Kunchang Li, Xinhao Li, Yi Wang, Yinan He, Yali Wang, Limin Wang, and Yu Qiao. Videomamba: State space model for efficient video understanding. In ECCV, pages 237–255. Springer, 2025. 2, 3, 5

    2025

  28. [36]

    Mamba- nd: Selective state space modeling for multi-dimensional data

    Shufan Li, Harkanwar Singh, and Aditya Grover. Mamba- nd: Selective state space modeling for multi-dimensional data. In ECCV, pages 75–92. Springer, 2025. 3

    2025

  29. [37]

    Ugc-video: perceptual quality assess- ment of user-generated videos

    Yang Li, Shengbin Meng, Xinfeng Zhang, Shiqi Wang, Yue Wang, and Siwei Ma. Ugc-video: perceptual quality assess- ment of user-generated videos. InMIPR, pages 35–38. IEEE,

  30. [38]

    Exploring the ef- fectiveness of video perceptual representation in blind video quality assessment

    Liang Liao, Kangmin Xu, Haoning Wu, Chaofeng Chen, Wenxiu Sun, Qiong Yan, and Weisi Lin. Exploring the ef- fectiveness of video perceptual representation in blind video quality assessment. In ACM MM, pages 837–846, 2022. 1

    2022

  31. [39]

    Swin-umamba: Mamba-based unet with imagenet-based pretraining

    Jiarun Liu, Hao Yang, Hong-Yu Zhou, Yan Xi, Lequan Yu, Cheng Li, Yong Liang, Guangming Shi, Yizhou Yu, Shaot- ing Zhang, et al. Swin-umamba: Mamba-based unet with imagenet-based pretraining. In MICCAI, pages 615–625. Springer, 2024. 3

    2024

  32. [40]

    End- to-end blind quality assessment of compressed videos using deep neural networks

    Wentao Liu, Zhengfang Duanmu, and Zhou Wang. End- to-end blind quality assessment of compressed videos using deep neural networks. In ACM MM, pages 546–554, 2018. 1, 2, 3

    2018

  33. [41]

    Scaling and masking: A new paradigm of data sampling for image and video quality assessment

    Yongxu Liu, Yinghui Quan, Guoyao Xiao, Aobo Li, and Jin- jian Wu. Scaling and masking: A new paradigm of data sampling for image and video quality assessment. In AAAI, pages 3792–3801, 2024. 3

    2024

  34. [42]

    Vmamba: Visual state space model

    Yue Liu, Yunjie Tian, Yuzhong Zhao, Hongtian Yu, Lingxi Xie, Yaowei Wang, Qixiang Ye, and Yunfan Liu. Vmamba: Visual state space model. arXiv preprint arXiv:2401.10166,

    arXiv

  35. [43]

    Swin transformer: Hierarchical vision transformer using shifted windows

    Ze Liu, Yutong Lin, Yue Cao, Han Hu, Yixuan Wei, Zheng Zhang, Stephen Lin, and Baining Guo. Swin transformer: Hierarchical vision transformer using shifted windows. In ICCV, pages 10012–10022, 2021. 1, 2

    2021

  36. [44]

    Video swin transformer

    Ze Liu, Jia Ning, Yue Cao, Yixuan Wei, Zheng Zhang, Stephen Lin, and Han Hu. Video swin transformer. InCVPR, pages 3202–3211, 2022. 1, 2, 3

    2022

  37. [45]

    U-mamba: Enhancing long-range dependency for biomedical image segmentation

    Jun Ma, Feifei Li, and Bo Wang. U-mamba: Enhancing long-range dependency for biomedical image segmentation. arXiv preprint arXiv:2401.04722, 2024. 3

    2024 arXiv

  38. [46]

    Long range language modeling via gated state spaces

    Harsh Mehta, Ankit Gupta, Ashok Cutkosky, and Behnam Neyshabur. Long range language modeling via gated state spaces. In ICLR, 2023. 2, 3

    2023

  39. [47]

    ZE-FESG: A zero-shot feature extraction method based on semantic guid- ance for no-reference video quality assessment

    Yachun Mi, Yu Li, Yan Shu, and Shaohui Liu. ZE-FESG: A zero-shot feature extraction method based on semantic guid- ance for no-reference video quality assessment. In ICASSP, pages 3640–3644, 2024. 2

    2024

  40. [48]

    CLiF-VQA: Enhancing video quality assess- ment by incorporating high-level semantic information re- lated to human feelings

    Yachun Mi, Yan Shu, Yu Li, Chen Hui, Puchao Zhou, and Shaohui Liu. CLiF-VQA: Enhancing video quality assess- ment by incorporating high-level semantic information re- lated to human feelings. In ACM MM , page 9989–9998,

  41. [49]

    No-reference image quality assessment in the spa- tial domain

    Anish Mittal, Anush Krishna Moorthy, and Alan Conrad Bovik. No-reference image quality assessment in the spa- tial domain. IEEE TIP, 21(12):4695–4708, 2012. 2, 6

    2012

  42. [50]

    completely blind

    Anish Mittal, Rajiv Soundararajan, and Alan C Bovik. Mak- ing a “completely blind” image quality analyzer. IEEE SPL, 20(3):209–212, 2012. 2

    2012

  43. [51]

    A com- pletely blind video integrity oracle

    Anish Mittal, Michele A Saad, and Alan C Bovik. A com- pletely blind video integrity oracle. IEEE TIP, 25(1):289– 300, 2015. 1, 2

    2015

  44. [52]

    Hummuss: Human motion understanding using state space models

    Arnab Mondal, Stefano Alletto, and Denis Tome. Hummuss: Human motion understanding using state space models. In CVPR, pages 2318–2330, 2024. 3

    2024

  45. [53]

    CVD2014—A database for evaluating no-reference video quality assess- ment algorithms

    Mikko Nuutinen, Toni Virtanen, Mikko Vaahteranoksa, Tero Vuori, Pirkko Oittinen, and Jukka H¨akkinen. CVD2014—A database for evaluating no-reference video quality assess- ment algorithms. IEEE TIP, 25(7):3073–3086, 2016. 2

    2016

  46. [54]

    Videomamba: Spatio-temporal se- lective state space model

    Jinyoung Park, Hee-Seon Kim, Kangwook Ko, Minbeom Kim, and Changick Kim. Videomamba: Spatio-temporal se- lective state space model. In ECCV, pages 1–18. Springer,

  47. [55]

    Learn- ing transferable visual models from natural language super- vision

    Alec Radford, Jong Wook Kim, Chris Hallacy, Aditya Ramesh, Gabriel Goh, Sandhini Agarwal, Girish Sastry, Amanda Askell, Pamela Mishkin, Jack Clark, et al. Learn- ing transferable visual models from natural language super- vision. In ICML, pages 8748–8763. PMLR, 2021. 2

    2021

  48. [56]

    Blind prediction of natural video quality

    Michele A Saad, Alan C Bovik, and Christophe Charrier. Blind prediction of natural video quality. IEEE TIP, 23(3): 1352–1365, 2014. 1, 2

    2014

  49. [57]

    Very deep convo- lutional networks for large-scale image recognition

    Karen Simonyan and Andrew Zisserman. Very deep convo- lutional networks for large-scale image recognition. arXiv preprint arXiv:1409.1556, 2014. 1, 2, 3

    2014 arXiv

  50. [58]

    Large-scale study of perceptual video quality

    Zeina Sinno and Alan Conrad Bovik. Large-scale study of perceptual video quality. IEEE TIP, 28(2):612–627, 2018. 2, 6

    2018

  51. [59]

    Simplified state space layers for sequence modeling

    Jimmy TH Smith, Andrew Warrington, and Scott Linder- man. Simplified state space layers for sequence modeling. In ICLR. 2, 3

  52. [60]

    A deep learning based no-reference quality assessment model for ugc videos

    Wei Sun, Xiongkuo Min, Wei Lu, and Guangtao Zhai. A deep learning based no-reference quality assessment model for ugc videos. In ACM MM, pages 856–865, 2022. 1, 2, 3

    2022

  53. [61]

    Efficientnet: Rethinking model scaling for convolutional neural networks

    Mingxing Tan and Quoc Le. Efficientnet: Rethinking model scaling for convolutional neural networks. In ICML, pages 6105–6114. PMLR, 2019. 1, 2

    2019

  54. [62]

    Efficientnetv2: Smaller models and faster training

    Mingxing Tan and Quoc Le. Efficientnetv2: Smaller models and faster training. In ICML, pages 10096–10106. PMLR, 2021. 10

    2021

  55. [63]

    Learning spatiotemporal features with 3d convolutional networks

    Du Tran, Lubomir Bourdev, Rob Fergus, Lorenzo Torre- sani, and Manohar Paluri. Learning spatiotemporal features with 3d convolutional networks. InICCV, pages 4489–4497,

  56. [64]

    UGC-VQA: benchmarking blind video quality assessment for user generated content.IEEE TIP, 30: 4449–4464, 2021

    Zhengzhong Tu, Yilin Wang, Neil Birkbeck, Balu Adsumilli, and Alan C Bovik. UGC-VQA: benchmarking blind video quality assessment for user generated content.IEEE TIP, 30: 4449–4464, 2021. 1, 2, 6, 7

    2021

  57. [65]

    Rapique: Rapid and accurate video quality prediction of user generated content

    Zhengzhong Tu, Xiangxu Yu, Yilin Wang, Neil Birkbeck, Balu Adsumilli, and Alan C Bovik. Rapique: Rapid and accurate video quality prediction of user generated content. IEEE OJSP, 2:425–440, 2021. 7

    2021

  58. [66]

    Selective structured state-spaces for long-form video understanding

    Jue Wang, Wentao Zhu, Pichao Wang, Xiang Yu, Linda Liu, Mohamed Omar, and Raffay Hamid. Selective structured state-spaces for long-form video understanding. In CVPR, pages 6387–6397, 2023. 2

    2023

  59. [67]

    Youtube ugc dataset for video compression research

    Yilin Wang, Sasi Inguva, and Balu Adsumilli. Youtube ugc dataset for video compression research. In MMSP, pages 1–

  60. [68]

    Rich features for perceptual quality assessment of ugc videos

    Yilin Wang, Junjie Ke, Hossein Talebi, Joong Gon Yim, Neil Birkbeck, Balu Adsumilli, Peyman Milanfar, and Feng Yang. Rich features for perceptual quality assessment of ugc videos. In CVPR, pages 13435–13444, 2021. 2, 3, 7

    2021

  61. [69]

    Modular blind video quality assess- ment

    Wen Wen, Mu Li, Yabin Zhang, Yiting Liao, Junlin Li, Li Zhang, and Kede Ma. Modular blind video quality assess- ment. In CVPR, pages 2763–2772, 2024. 6, 7

    2024

  62. [70]

    Fast- vqa: Efficient end-to-end video quality assessment with frag- ment sampling

    Haoning Wu, Chaofeng Chen, Jingwen Hou, Liang Liao, Annan Wang, Wenxiu Sun, Qiong Yan, and Weisi Lin. Fast- vqa: Efficient end-to-end video quality assessment with frag- ment sampling. In ECCV, pages 538–554. Springer, 2022. 1, 2, 3, 5, 6, 7, 8

    2022

  63. [71]

    Neigh- bourhood representative sampling for efficient end-to-end video quality assessment

    Haoning Wu, Chaofeng Chen, Liang Liao, Jingwen Hou, Wenxiu Sun, Qiong Yan, Jinwei Gu, and Weisi Lin. Neigh- bourhood representative sampling for efficient end-to-end video quality assessment. IEEE TPAMI, 2023. 3, 6, 7

    2023

  64. [72]

    Discovqa: Temporal distortion-content transformers for video quality assessment

    Haoning Wu, Chaofeng Chen, Liang Liao, Jingwen Hou, Wenxiu Sun, Qiong Yan, and Weisi Lin. Discovqa: Temporal distortion-content transformers for video quality assessment. IEEE TCSVT, 2023. 2, 3

    2023

  65. [73]

    Exploring opinion-unaware video quality assessment with semantic affinity criterion

    Haoning Wu, Liang Liao, Jingwen Hou, Chaofeng Chen, Erli Zhang, Annan Wang, Wenxiu Sun, Qiong Yan, and Weisi Lin. Exploring opinion-unaware video quality assessment with semantic affinity criterion. ICME, 2023

    2023

  66. [74]

    Towards robust text-prompted semantic criterion for in-the-wild video quality assessment

    Haoning Wu, Liang Liao, Annan Wang, Chaofeng Chen, Jingwen Hou, Wenxiu Sun, Qiong Yan, and Weisi Lin. Towards robust text-prompted semantic criterion for in-the-wild video quality assessment. arXiv preprint arXiv:2304.14672, 2023. 1, 2

    2023 arXiv

  67. [75]

    Towards explainable in-the-wild video quality assess- ment: A database and a language-prompted approach

    Haoning Wu, Erli Zhang, Liang Liao, Chaofeng Chen, Jing- wen Hou, Annan Wang, Wenxiu Sun, Qiong Yan, and Weisi Lin. Towards explainable in-the-wild video quality assess- ment: A database and a language-prompted approach. In ACM MM, page 1045–1054, 2023. 3, 7

    2023

  68. [76]

    Exploring video quality assessment on user generated contents from aesthetic and technical perspectives

    Haoning Wu, Erli Zhang, Liang Liao, Chaofeng Chen, Jing- wen Hou, Annan Wang, Wenxiu Sun, Qiong Yan, and Weisi Lin. Exploring video quality assessment on user generated contents from aesthetic and technical perspectives. In ICCV, pages 20144–20154, 2023. 3, 6, 7

    2023

  69. [77]

    Rainmamba: Enhanced locality learning with state space models for video deraining

    Hongtao Wu, Yijun Yang, Huihui Xu, Weiming Wang, Jinni Zhou, and Lei Zhu. Rainmamba: Enhanced locality learning with state space models for video deraining. In ACM MM, pages 7881–7890, 2024. 3

    2024

  70. [78]

    Q-align: teaching lmms for visual scoring via discrete text-defined levels

    Haoning Wu, Zicheng Zhang, Weixia Zhang, Chaofeng Chen, Liang Liao, Chunyi Li, Yixuan Gao, Annan Wang, Erli Zhang, Wenxiu Sun, et al. Q-align: teaching lmms for visual scoring via discrete text-defined levels. In ICML, pages 54015–54029, 2024. 6

    2024

  71. [79]

    No- reference video quality assessment via feature learning

    Jingtao Xu, Peng Ye, Yong Liu, and David Doermann. No- reference video quality assessment via feature learning. In ICIP, pages 491–495. IEEE, 2014. 2

    2014

  72. [80]

    Perceptual quality assessment of internet videos

    Jiahua Xu, Jing Li, Xingguang Zhou, Wei Zhou, Baichao Wang, and Zhibo Chen. Perceptual quality assessment of internet videos. In ACM MM, pages 1248–1257, 2021. 1, 2

    2021

  73. [81]

    Blind image quality assessment using joint statistics of gradient magnitude and laplacian features

    Wufeng Xue, Xuanqin Mou, Lei Zhang, Alan C Bovik, and Xiangchu Feng. Blind image quality assessment using joint statistics of gradient magnitude and laplacian features. IEEE TIP, 23(11):4850–4862, 2014. 2

    2014

  74. [82]

    Un- supervised feature learning framework for no-reference im- age quality assessment

    Peng Ye, Jayant Kumar, Le Kang, and David Doermann. Un- supervised feature learning framework for no-reference im- age quality assessment. In CVPR, pages 1098–1105. IEEE,

  75. [83]

    Patch-vq:’patching up’the video quality problem

    Zhenqiang Ying, Maniratnam Mandal, Deepti Ghadiyaram, and Alan Bovik. Patch-vq:’patching up’the video quality problem. In CVPR, pages 14019–14029, 2021. 1, 2, 3, 6, 7

    2021

  76. [84]

    Deep neural networks for no-reference video quality assessment

    Junyong You and Jari Korhonen. Deep neural networks for no-reference video quality assessment. In ICIP, pages 2349–

  77. [85]

    Mambaout: Do we really need mamba for vision? arXiv preprint arXiv:2405.07992,

    Weihao Yu and Xinchao Wang. Mambaout: Do we really need mamba for vision? arXiv preprint arXiv:2405.07992,

    arXiv

  78. [86]

    Md-vqa: Multi-dimensional quality assessment for ugc live videos

    Zicheng Zhang, Wei Wu, Wei Sun, Danyang Tu, Wei Lu, Xiongkuo Min, Ying Chen, and Guangtao Zhai. Md-vqa: Multi-dimensional quality assessment for ugc live videos. In CVPR, pages 1746–1755, 2023. 1, 2, 3

    2023

  79. [87]

    Motion mamba: Efficient and long sequence motion generation

    Zeyu Zhang, Akide Liu, Ian Reid, Richard Hartley, Bo- han Zhuang, and Hao Tang. Motion mamba: Efficient and long sequence motion generation. In ECCV, pages 265–282. Springer, 2025. 3

    2025

  80. [88]

    Zoom-vqa: Patches, frames and clips integration for video quality as- sessment

    Kai Zhao, Kun Yuan, Ming Sun, and Xing Wen. Zoom-vqa: Patches, frames and clips integration for video quality as- sessment. In CVPR, pages 1302–1310, 2023. 1, 2, 3, 6

    2023

  81. [89]

    Learning spatiotemporal interactions for user- generated video quality assessment

    Hanwei Zhu, Baoliang Chen, Lingyu Zhu, and Shiqi Wang. Learning spatiotemporal interactions for user- generated video quality assessment. IEEE TCSVT , 33(3): 1031–1042, 2022. 1, 2

    2022

  82. [90]

    Vision mamba: Efficient visual representation learning with bidirectional state space model

    Lianghui Zhu, Bencheng Liao, Qian Zhang, Xinlong Wang, Wenyu Liu, and Xinggang Wang. Vision mamba: Efficient visual representation learning with bidirectional state space model. arXiv preprint arXiv:2401.09417, 2024. 2, 3, 4, 5

    2024 arXiv

  83. [91]

    State space models for event cameras

    Nikola Zubic, Mathias Gehrig, and Davide Scaramuzza. State space models for event cameras. InCVPR, pages 5819– 5828, 2024. 3 11

    2024

Pith tools

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