Pith. sign in

REVIEW 3 major objections 4 minor 77 references

Binocular Rivalry Oriented Predictive Auto-Encoding Network for Blind Stereoscopic Image Quality Measurement

T0 review · 3 major / 4 minor · reviewed 2026-08-14 · deepseek-v4-flash

Challenge this review Re-run · record.json Download PDF Read on arXiv ↗ Review JSON

Pith's one-line read Modeling binocular rivalry as predictive coding gives a blind stereoscopic quality metric its edge, particularly on unequally distorted view pairs.

desk verdict A useful NR-SIQM architecture with open code, but the headline SOTA numbers rest on a content-leaky 80/20 split and should be re-run on content-disjoint splits before being trusted. read the letter →

arxiv 1909.01738 v3 pith:QP3VAU5L submitted 2019-09-04 cs.MM eess.IV

Jiahua Xu , Wei Zhou , Zhibo Chen , Suiyi Ling , Patrick Le Callet This is my paper · ORCID
classification cs.MMeess.IV
keywords stereoscopicimagequalityassessmentno-referencemetricbinocularrivalrypredictivecodingencoder-decodernetworkasymmetricdistortionSiameseregression
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

The paper tries to show that a no-reference stereoscopic image quality metric can be improved by building in a specific model of how the brain handles mismatched views: binocular rivalry as predictive coding. Instead of treating the left and right views as independent images to be averaged, the proposed PAD-Net reconstructs each view with an auto-encoder, converts reconstruction error into a likelihood map and high-level features into a prior map, then lets those maps compete through normalization. These rivalry maps, together with the distorted views, are fed to a quality regression network. On three benchmark databases the reported correlations with human opinion scores exceed those of the compared full-, reduced-, and no-reference metrics, with the clearest gains on asymmetrically distorted pairs.

What carries the argument

The load-bearing object is the pair of normalized rivalry maps derived from the Siamese encoder-decoder. The likelihood map is the squared difference between the input view and its reconstruction, inverted so small error means high likelihood; the prior map is the softplus-activated high-level encoder feature. Equations (7) and (8) normalize the left and right versions by their sum, and the left likelihood uses the right error (and vice versa), so each map expresses one eye's dominance relative to the other. A convolution-plus-GDN fusion layer combines the two views with the two likelihood and two prior maps into a three-channel input for a ResNet-18 regressor with max pooling and a fully connected layer. This fusion is what lets the network read which view is winning the rivalry, and the paper's ablation shows the maps and the fusion both contribute on the asymmetric-heavy LIVE Phase II database.

What would settle it

Re-run the LIVE Phase II and Waterloo IVC Phase I evaluations with training and test sets separated by reference scene, so no distorted version of any test scene appears in training, and compare asymmetric-pair SROCC to the reported 0.954 and 0.978; a large drop would show the rivalry maps are memorizing content rather than measuring quality.

Watch

Extended reading notes

Core claim

The central claim is that predictive coding offers a usable computational recipe for binocular rivalry in quality assessment. For each view, the encoder-decoder's reconstruction error defines a likelihood map (small error means high likelihood), while the compressed high-level representation defines a prior map; normalizing each map by the sum of the two views' maps turns them into explicit dominance probabilities. The paper reports that this design, trained with two 2D pre-training stages followed by joint optimization on stereo databases, outperforms prior metrics: SROCC of 0.973 on LIVE Phase I, 0.967 on LIVE Phase II, and 0.974 on Waterloo IVC Phase I, with the largest margin over rivals on asymmetric distortion subsets. It also reports better statistical significance in t-tests and better discrimination under the Krasula methodology on Waterloo IVC Phase I.

Load-bearing premise

The evaluation assumes that randomly splitting distorted stereo pairs into 80% training and 20% testing makes the sets independent, even though distorted versions of the same reference scene can appear in both, so the reported correlations could come partly from content memorization rather than quality understanding.

Editorial extensions

If this is right

  • On the three evaluated databases, the reported correlations with human scores exceed those of the compared metrics, so a rivalry-aware design appears to capture information that view-averaging or cyclopean-image approaches miss.
  • Asymmetric distortion handling improves: on the LIVE Phase II asymmetric pairs the reported SROCC is 0.954, clearly above the best compared method's 0.927.
  • The normalized likelihood and prior maps give a per-pixel account of which eye's signal dominates, making the model's predictions interpretable rather than a black-box score.
  • Transfer learning from large 2D quality databases to the small 3D databases is enough to train a competitive deep stereo metric, reducing the need for large 3D subjective datasets.
  • At 0.906 seconds per image, the method is fast enough for practical use, roughly nine times faster than the compared CNN baseline.

Reading between the lines

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

  • If the rivalry maps are doing genuine perceptual work, they should predict the outcome of psychophysical binocular-rivalry experiments: the view with the larger normalized likelihood or prior sum should be the one subjects report seeing, which could be tested directly.
  • The same likelihood-prior construction could be lifted into a no-reference 2D quality model by treating the reconstruction-error map as a distortion mask, an extension the authors do not explore.
  • The content-split concern suggests a stronger evaluation: train on distortions from some reference scenes and test on entirely unseen scenes, because the existing cross-database test only partially isolates memorization from quality learning.
Share X Bluesky LinkedIn Reddit HN

Editorial analysis

A structured set of objections, weighed in public.

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

Referee Report

3 major / 4 minor

Summary. The manuscript proposes PAD-Net, an end-to-end no-reference stereoscopic image quality metric. It uses a Siamese encoder-decoder to reconstruct the left and right views, interprets the reconstruction error as a likelihood map and high-level encoder features as a prior map, and fuses these normalized maps with the original views before a ResNet-18 regressor predicts the MOS/DMOS. The network is pretrained on 2D databases and jointly fine-tuned on 3D databases. Experiments on LIVE Phase I, LIVE Phase II, and Waterloo IVC Phase I report state-of-the-art SROCC/PLCC, including improved performance on asymmetrically distorted pairs, together with ablation, cross-database, and complexity analyses.

Significance. If the reported results hold under a contamination-free protocol, the paper would be a solid contribution to blind SIQM: it builds a network around a biologically motivated rivalry model, releases source code, and reports strong correlation with subjective scores, including an asymmetric-distortion breakdown and a Krasula-based significance analysis. The architecture is described in detail, and the ablation partially isolates the effect of the likelihood/prior maps. However, the empirical claims currently rest on a train/test split that does not guarantee content independence, and the cross-database evidence is mixed and under-reported. The contribution is significant but needs validation under a content-disjoint protocol.

major comments (3)
  1. [IV-B (Tables IV, V, IX)] The random 80/20 split by distorted stereopairs does not guarantee content independence because each reference scene has many distorted versions (e.g., LIVE Phase II has 8 references and 360 images; Waterloo IVC Phase I has 6 references and 330 images). The same reference content therefore almost certainly appears in both training and testing, and the testing procedure uses heavily overlapping 256×256 crops with stride 192×104. This allows the regressor to memorize reference-specific content and distortion signatures, inflating the headline SROCC values (0.973, 0.967, 0.974) and the asymmetric-distortion advantage in Table IX. The 10-run t-test in Table VI only measures the variance of the same protocol. Please re-run the evaluation with a content-disjoint split (e.g., split by reference scene) and report SROCC, PLCC, and RMSE, including the symmetric/asymmetric breakdown; the central state-of-the-art claim is not supported until this is done.
  2. [III-D, Fig. 7] The ablation study does not cleanly establish that the likelihood/prior maps are the cause of the improvement. In the joint optimization stage, the encoder-decoder weights w1, the regression weights w2, and the newly introduced w3 are all updated with loss l3 on the target MOS labels (Eqs. 14-16). Adding the map-generation and fusion branches therefore adds trainable parameters that are directly supervised by the target scores, so the gain over the baseline could come from additional capacity rather than from the binocular-rivalry prior. To support the mechanistic claim, the maps should be generated by a frozen pretrained encoder-decoder or by an unsupervised objective, and the comparison repeated.
  3. [IV-E, Table X] The cross-database experiments, which are the main evidence that does not suffer from the same-content training and testing issue, are only reported in terms of PLCC and are mixed: trained on LIVE Phase II and tested on LIVE Phase I, PAD-Net (0.915) is below StereoQA-Net (0.932). Moreover, no SROCC or symmetric/asymmetric sub-scores are given, so the claim of superior generalization for asymmetric distortions is not established by this table. Please report complete correlation metrics and a per-condition breakdown for both cross-database directions.
minor comments (4)
  1. [Abstract] The word 'commutation' appears to be a typo; 'communication systems' is the likely intended phrase.
  2. [IV-B] The sentence 'which will be explained in Section III D' should refer to Section IV-D, where the symmetric/asymmetric evaluation is presented.
  3. [Table II] 'Normlization' is misspelled, and the labels 'Square7a'/'Square7b' are inconsistently formatted.
  4. [III-D] The testing-stage cropping rule is described only as a stride of 192 for width and 104 for height; please clarify how boundary crops are handled when the image dimensions are not exact multiples of the stride.

Circularity Check

0 steps flagged · score 0.0 of 10

No significant circularity: PAD-Net's headline results are supervised regression against external MOS labels, with predictive-coding/binocular-rivalry language serving as architectural motivation rather than as a derivation step.

full rationale

The paper's central claim is empirical: PAD-Net is trained with an l2 loss against MOS/DMOS values (Eqs. 11 and 14) and evaluated on held-out stereopairs from LIVE Phase I/II and Waterloo IVC Phase I, including cross-database tests (Table X). The likelihood and prior maps are defined from reconstruction error and high-level encoder features (Eqs. 6-8), not from the MOS labels, and are then used as additional inputs to a supervised regressor; this is feature engineering, not a derivation of the output from the input. Citations to predictive-coding theory [49] and to the authors' own [55] provide motivation and a conceptual framework, but no uniqueness theorem or fitted parameter is imported from [55] to force the benchmark scores. The random 80/20 split may raise content-leakage or generalization concerns, but that is a validation-protocol issue, not circularity of the derivation. No step in the paper reduces a predicted quantity to its own input by construction.

Assumptions & free parameters 4 free parameters · 5 assumptions · 0 invented entities

The central claim rests on the network weights and hyperparameters fitted to MOS labels, plus the domain assumption that MOS scores are reliable and that the random split is content-independent. No new physical entities are introduced; the likelihood and prior maps are computational feature maps rather than postulated world objects.

free parameters (4)
  • Encoder-decoder weights w1 = trained on Waterloo Exploration and fine-tuned on 3D databases
    Updated by minimizing reconstruction loss and then jointly optimized with quality loss; the maps these weights produce affect the final quality prediction.
  • ResNet-18 regression weights w2 = pretrained on ImageNet and LIVE 2D, fine-tuned on 3D databases
    Directly map fused features to MOS; this is the main fitted regressor.
  • Fusion and prior-generation weights w3 = trained from scratch on target 3D databases
    Control the 1x1 convolution fusion and prior map generation; these parameters are fit to the target MOS.
  • Hyperparameters (learning rates, epochs, stride U) = alpha1=1e-5, alpha2=5e-4, alpha3=1e-3, 300 epochs, U=192x104
    Chosen by hand; the split stride and learning-rate schedule affect results but are not systematically optimized.
assumptions (5)
  • domain assumption Subjective MOS/DMOS scores in LIVE and Waterloo databases are valid ground truth for perceptual quality.
    The paper uses these scores as regression labels without questioning their reliability.
  • domain assumption Predictive coding theory provides a valid account of binocular rivalry in the human visual system.
    Section II-B adopts the framework from [49] as motivation for the network design.
  • ad hoc to paper Reconstruction error and high-level encoder features are adequate proxies for likelihood and prior in rivalry.
    The paper states 'we are not going to calculate real probabilities' and defines these maps by Eqs. 6-8; this is a design choice specific to this work.
  • domain assumption Randomly splitting distorted stereo pairs yields independent training and test sets.
    Section IV-B; this assumption is questionable because multiple distorted versions of the same reference image can appear in both sets.
  • standard math Adam optimization converges to a solution that generalizes.
    Standard deep learning practice; no proof of convergence or global optimality is given.

how reviews work

0 comments
Cite this review

Pith. "Pith review of Binocular Rivalry Oriented Predictive Auto-Encoding Network for Blind Stereoscopic Image Quality Measurement." pith.science (2026). https://pith.science/paper/QP3VAU5L

@misc{pith2026190901738,
  author       = {Pith},
  title        = {Pith review of: Binocular Rivalry Oriented Predictive Auto-Encoding Network for Blind Stereoscopic Image Quality Measurement},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/QP3VAU5L}},
  note         = {Machine review of arXiv:1909.01738}
}
read the original abstract

Stereoscopic image quality measurement (SIQM) has become increasingly important for guiding stereo image processing and commutation systems due to the widespread usage of 3D contents. Compared with conventional methods which are relied on hand-crafted features, deep learning oriented measurements have achieved remarkable performance in recent years. However, most existing deep SIQM evaluators are not specifically built for stereoscopic contents and consider little prior domain knowledge of the 3D human visual system (HVS) in network design. In this paper, we develop a Predictive Auto-encoDing Network (PAD-Net) for blind/No-Reference stereoscopic image quality measurement. In the first stage, inspired by the predictive coding theory that the cognition system tries to match bottom-up visual signal with top-down predictions, we adopt the encoder-decoder architecture to reconstruct the distorted inputs. Besides, motivated by the binocular rivalry phenomenon, we leverage the likelihood and prior maps generated from the predictive coding process in the Siamese framework for assisting SIQM. In the second stage, quality regression network is applied to the fusion image for acquiring the perceptual quality prediction. The performance of PAD-Net has been extensively evaluated on three benchmark databases and the superiority has been well validated on both symmetrically and asymmetrically distorted stereoscopic images under various distortion types.

Figures

Figures reproduced from arXiv: 1909.01738 by the authors.

Figure 1
Figure 1. The architecture of our proposed PAD-Net. It consists of a Siamese encoder-decoder module and a quality regression [PITH_FULL_IMAGE:figures/full_fig_p002_1.png] view at source ↗
Figure 2
Figure 2. Predictive coding theory based hierarchical represen [PITH_FULL_IMAGE:figures/full_fig_p004_2.png] view at source ↗
Figure 3
Figure 3. Simplified schematic of the binocular rivalry mech [PITH_FULL_IMAGE:figures/full_fig_p004_3.png] view at source ↗
Figures from the paper (4 more)
Figure 4
Figure 4. Figure 4: Crop in a slight overlapping manner during the testing [PITH_FULL_IMAGE:figures/full_fig_p008_4.png]
Figure 5
Figure 5. Figure 5: Scatter plots of DMOS values against predictions by [PITH_FULL_IMAGE:figures/full_fig_p009_5.png]
Figure 6
Figure 6. Figure 6: Fusion maps for symmetrically and asymmetrically [PITH_FULL_IMAGE:figures/full_fig_p010_6.png]
Figure 7
Figure 7. Figure 7: Ablation Study on LIVE Phase I and Phase II [PITH_FULL_IMAGE:figures/full_fig_p011_7.png]

Discussion (0). Continue with ORCID to comment.

Reference graph

Works this paper leans on

77 extracted references · 61 canonical work pages

  1. [34]

    Dual-stream interactive networks for no- reference stereoscopic image quality assessment,

    W. Zhou, Z. Chen, and W. Li, “Dual-stream interactive networks for no- reference stereoscopic image quality assessment,” IEEE Transactions on Image Processing, 2019

    work page 2019

  2. [55]

    Stereoscopic omnidirectional image quality assessment based on predictive coding theory,

    Z. Chen, J. Xu, C. Lin, and W. Zhou, “Stereoscopic omnidirectional image quality assessment based on predictive coding theory,” IEEE Journal of Selected Topics in Signal Processing , 2020

    work page 2020

  3. [1]

    Stereoscopy

    “Stereoscopy.” [Online]. Available: https://en.wikipedia.org/wiki/ Stereoscopy

    work page

  4. [2]

    Matching and 3-d reconstruction of multibubbles based on virtual stereo vision,

    T. Xue, L. Qu, and B. Wu, “Matching and 3-d reconstruction of multibubbles based on virtual stereo vision,” IEEE Transactions on Instrumentation and Measurement, vol. 63, no. 6, pp. 1639–1647, 2013

    work page 2013

  5. [3]

    A comparison be- tween stereo-vision techniques for the reconstruction of 3-d coordinates of objects,

    R. Anchini, C. Liguori, V . Paciello, and A. Paolillo, “A comparison be- tween stereo-vision techniques for the reconstruction of 3-d coordinates of objects,” IEEE Transactions on Instrumentation and Measurement , vol. 55, no. 5, pp. 1459–1466, 2006. IEEE TRANSACTIONS ON INSTRUMENTATION AND MEASUREMENT 12

    work page 2006

  6. [4]

    Depth estimation and image restoration using defocused stereo pairs,

    A. Rajagopalan, S. Chaudhuri, and U. Mudenagudi, “Depth estimation and image restoration using defocused stereo pairs,” IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 26, no. 11, pp. 1521– 1525, 2004

    work page 2004

  7. [5]

    Binocular depth discrimina- tion and estimation beyond interaction space,

    R. S. Allison, B. J. Gillam, and E. Vecellio, “Binocular depth discrimina- tion and estimation beyond interaction space,” Journal of Vision, vol. 9, no. 1, pp. 10–10, 2009

    work page 2009

  8. [6]

    An integrated framework for 3-d modeling, object detection, and pose estimation from point-clouds,

    Y . Guo, M. Bennamoun, F. Sohel, M. Lu, and J. Wan, “An integrated framework for 3-d modeling, object detection, and pose estimation from point-clouds,” IEEE Transactions on Instrumentation and Measurement, vol. 64, no. 3, pp. 683–693, 2014

    work page 2014

Show all 77 references
  1. [7]

    A vector approach for image quality assessment and some metrological considera- tions,

    A. De Angelis, A. Moschitta, F. Russo, and P. Carbone, “A vector approach for image quality assessment and some metrological considera- tions,” IEEE Transactions on Instrumentation and Measurement, vol. 58, no. 1, pp. 14–25, 2008

    2008

  2. [8]

    An internet protocol packet delay variation estimator for reliable quality assessment of video-streaming services,

    L. Angrisani, D. Capriglione, L. Ferrigno, and G. Miele, “An internet protocol packet delay variation estimator for reliable quality assessment of video-streaming services,” IEEE Transactions on Instrumentation and Measurement, vol. 62, no. 5, pp. 914–923, 2013

    2013

  3. [9]

    Effective and efficient blind quality evaluator for contrast distorted images,

    G. Yue, C. Hou, T. Zhou, and X. Zhang, “Effective and efficient blind quality evaluator for contrast distorted images,” IEEE Transactions on Instrumentation and Measurement, vol. 68, no. 8, pp. 2733–2741, 2018

    2018

  4. [10]

    Blind image quality measurement by exploiting high order statistics with deep dictionary encoding network,

    Q. Jiang, W. Gao, S. Wang, G. Yue, F. Shao, Y .-S. Ho, and S. Kwong, “Blind image quality measurement by exploiting high order statistics with deep dictionary encoding network,” IEEE Transactions on Instru- mentation and Measurement , 2020

    2020

  5. [11]

    A full- reference stereoscopic image quality measurement via hierarchical deep feature degradation fusion,

    Q. Jiang, W. Zhou, X. Chai, G. Yue, F. Shao, and Z. Chen, “A full- reference stereoscopic image quality measurement via hierarchical deep feature degradation fusion,” IEEE Transactions on Instrumentation and Measurement, 2020

    2020

  6. [12]

    Automatic enhancement of noisy images using objective evaluation of image quality,

    F. Russo, “Automatic enhancement of noisy images using objective evaluation of image quality,” IEEE transactions on Instrumentation and Measurement, vol. 54, no. 4, pp. 1600–1606, 2005

    2005

  7. [13]

    Exposure measurement and fusion via adaptive multiscale edge-preserving smoothing,

    Y . Que, Y . Yang, and H. J. Lee, “Exposure measurement and fusion via adaptive multiscale edge-preserving smoothing,” IEEE Transactions on Instrumentation and Measurement , vol. 68, no. 12, pp. 4663–4674, 2019

    2019

  8. [14]

    Subjective methods for the assessment of stereoscopic 3dtv systems,

    B. Series, “Subjective methods for the assessment of stereoscopic 3dtv systems,” 2012

    2012

  9. [15]

    Blind stereoscopic video quality assess- ment: From depth perception to overall experience,

    Z. Chen, W. Zhou, and W. Li, “Blind stereoscopic video quality assess- ment: From depth perception to overall experience,” IEEE Transactions on Image Processing , vol. 27, no. 2, pp. 721–734, 2017

    2017

  10. [16]

    Toward a blind deep quality evaluator for stereoscopic images based on monocular and binocular interactions,

    F. Shao, W. Tian, W. Lin, G. Jiang, and Q. Dai, “Toward a blind deep quality evaluator for stereoscopic images based on monocular and binocular interactions,”IEEE Transactions on Image Processing, vol. 25, no. 5, pp. 2059–2074, 2016

    2016

  11. [17]

    3d-hevc visual quality as- sessment: Database and bitstream model,

    W. Zhou, N. Liao, Z. Chen, and W. Li, “3d-hevc visual quality as- sessment: Database and bitstream model,” in 2016 Eighth International Conference on Quality of Multimedia Experience (QoMEX) . IEEE, 2016, pp. 1–6

    2016

  12. [18]

    Perceptual depth quality in distorted stereoscopic images,

    J. Wang, S. Wang, K. Ma, and Z. Wang, “Perceptual depth quality in distorted stereoscopic images,” IEEE Transactions on Image Processing, vol. 26, no. 3, pp. 1202–1215, 2016

    2016

  13. [19]

    Visual discomfort prediction on stereoscopic 3d images without explicit disparities,

    J. Chen, J. Zhou, J. Sun, and A. C. Bovik, “Visual discomfort prediction on stereoscopic 3d images without explicit disparities,” Signal Process- ing: Image Communication , vol. 51, pp. 50–60, 2017

    2017

  14. [20]

    Quality assess- ment of stereoscopic images,

    A. Benoit, P. Le Callet, P. Campisi, and R. Cousseau, “Quality assess- ment of stereoscopic images,” EURASIP journal on image and video processing, vol. 2008, no. 1, p. 659024, 2009

    2008

  15. [21]

    Perceptual quality assessment for stereoscopic images based on 2d image quality metrics and disparity analysis,

    J. You, L. Xing, A. Perkis, and X. Wang, “Perceptual quality assessment for stereoscopic images based on 2d image quality metrics and disparity analysis,” in Proc. Int. Workshop Video Process. Quality Metrics Con- sum. Electron, vol. 9, 2010, pp. 1–6

    2010

  16. [22]

    Stereoscopic image quality metrics and compression,

    P. Gorley and N. Holliman, “Stereoscopic image quality metrics and compression,” in Stereoscopic Displays and Applications XIX, vol. 6803. International Society for Optics and Photonics, 2008, p. 680305

    2008

  17. [23]

    Full-reference quality assessment of stereopairs accounting for rivalry,

    M.-J. Chen, C.-C. Su, D.-K. Kwon, L. K. Cormack, and A. C. Bovik, “Full-reference quality assessment of stereopairs accounting for rivalry,” Signal Processing: Image Communication , vol. 28, no. 9, pp. 1143– 1155, 2013

    2013

  18. [24]

    Quality assessment of stereoscopic 3d image compression by binocular integration behaviors,

    Y .-H. Lin and J.-L. Wu, “Quality assessment of stereoscopic 3d image compression by binocular integration behaviors,” IEEE transactions on Image Processing, vol. 23, no. 4, pp. 1527–1542, 2014

    2014

  19. [25]

    Reduced reference stereoscopic image quality assessment based on binocular perceptual information,

    F. Qi, D. Zhao, and W. Gao, “Reduced reference stereoscopic image quality assessment based on binocular perceptual information,” IEEE Transactions on multimedia , vol. 17, no. 12, pp. 2338–2344, 2015

    2015

  20. [26]

    Reorganized dct-based image representation for reduced reference stereoscopic image quality assessment,

    L. Ma, X. Wang, Q. Liu, and K. N. Ngan, “Reorganized dct-based image representation for reduced reference stereoscopic image quality assessment,” Neurocomputing, vol. 215, pp. 21–31, 2016

    2016

  21. [27]

    Reduced-reference stereoscopic image quality assessment using natural scene statistics and structural degradation,

    J. Ma, P. An, L. Shen, and K. Li, “Reduced-reference stereoscopic image quality assessment using natural scene statistics and structural degradation,” IEEE Access, vol. 6, pp. 2768–2780, 2017

    2017

  22. [28]

    No-reference stereoscopic image quality assessment,

    R. Akhter, Z. P. Sazzad, Y . Horita, and J. Baltes, “No-reference stereoscopic image quality assessment,” in Stereoscopic Displays and Applications XXI , vol. 7524. International Society for Optics and Photonics, 2010, p. 75240T

    2010

  23. [29]

    Objective no-reference stereoscopic image quality prediction based on 2d image features and relative disparity,

    Z. Sazzad, R. Akhter, J. Baltes, and Y . Horita, “Objective no-reference stereoscopic image quality prediction based on 2d image features and relative disparity,” Advances in Multimedia , vol. 2012, p. 8, 2012

    2012

  24. [30]

    No-reference quality assessment of natural stereopairs,

    M.-J. Chen, L. K. Cormack, and A. C. Bovik, “No-reference quality assessment of natural stereopairs,” IEEE Transactions on Image Pro- cessing, vol. 22, no. 9, pp. 3379–3391, 2013

    2013

  25. [31]

    Oriented correlation models of distorted natural images with application to natural stereopair quality evaluation,

    C.-C. Su, L. K. Cormack, and A. C. Bovik, “Oriented correlation models of distorted natural images with application to natural stereopair quality evaluation,” IEEE Transactions on image processing , vol. 24, no. 5, pp. 1685–1699, 2015

    2015

  26. [32]

    Blind deep s3d image quality evaluation via local to global feature aggregation,

    H. Oh, S. Ahn, J. Kim, and S. Lee, “Blind deep s3d image quality evaluation via local to global feature aggregation,” IEEE Transactions on Image Processing , vol. 26, no. 10, pp. 4923–4936, 2017

    2017

  27. [33]

    Blind assess- ment for stereo images considering binocular characteristics and deep perception map based on deep belief network,

    J. Yang, Y . Zhao, Y . Zhu, H. Xu, W. Lu, and Q. Meng, “Blind assess- ment for stereo images considering binocular characteristics and deep perception map based on deep belief network,” Information Sciences , vol. 474, pp. 1–17, 2019

    2019

  28. [35]

    Image quality assessment: from error visibility to structural similarity,

    Z. Wang, A. C. Bovik, H. R. Sheikh, E. P. Simoncelli et al. , “Image quality assessment: from error visibility to structural similarity,” IEEE transactions on image processing , vol. 13, no. 4, pp. 600–612, 2004

    2004

  29. [36]

    A universal image quality index,

    Z. Wang and A. C. Bovik, “A universal image quality index,” IEEE signal processing letters , vol. 9, no. 3, pp. 81–84, 2002

    2002

  30. [37]

    An image quality assessment method based on perception of structural information,

    M. Carnec, P. Le Callet, and D. Barba, “An image quality assessment method based on perception of structural information,” in Proceed- ings 2003 International Conference on Image Processing (Cat. No. 03CH37429), vol. 3. IEEE, 2003, pp. III–185

    2003

  31. [38]

    Reduced-reference image quality assess- ment using a wavelet-domain natural image statistic model,

    Z. Wang and E. P. Simoncelli, “Reduced-reference image quality assess- ment using a wavelet-domain natural image statistic model,” in Human Vision and Electronic Imaging X , vol. 5666. International Society for Optics and Photonics, 2005, pp. 149–159

    2005

  32. [39]

    Stereoscopic images quality assessment,

    P. Campisi, P. Le Callet, and E. Marini, “Stereoscopic images quality assessment,” in 2007 15th European Signal Processing Conference . IEEE, 2007, pp. 2110–2114

    2007

  33. [40]

    Very deep convolutional networks for large-scale image recognition,

    K. Simonyan and A. Zisserman, “Very deep convolutional networks for large-scale image recognition,” arXiv preprint arXiv:1409.1556 , 2014

    2014 arXiv

  34. [41]

    Deep residual learning for image recognition,

    K. He, X. Zhang, S. Ren, and J. Sun, “Deep residual learning for image recognition,” in Proceedings of the IEEE conference on computer vision and pattern recognition , 2016, pp. 770–778

    2016

  35. [42]

    Blind quality as- sessment for image superresolution using deep two-stream convolutional networks,

    W. Zhou, Q. Jiang, Y . Wang, Z. Chen, and W. Li, “Blind quality as- sessment for image superresolution using deep two-stream convolutional networks,” Information Sciences, 2020

    2020

  36. [43]

    A blind stereo- scopic image quality evaluator with segmented stacked autoencoders considering the whole visual perception route,

    J. Yang, K. Sim, X. Gao, W. Lu, Q. Meng, and B. Li, “A blind stereo- scopic image quality evaluator with segmented stacked autoencoders considering the whole visual perception route,” IEEE Transactions on Image Processing, vol. 28, no. 3, pp. 1314–1328, 2018

    2018

  37. [44]

    I. P. Howard, B. J. Rogers et al. , Binocular vision and stereopsis . Oxford University Press, USA, 1995

    1995

  38. [45]

    Quality prediction of asymmetrically distorted stereoscopic 3d images,

    J. Wang, A. Rehman, K. Zeng, S. Wang, and Z. Wang, “Quality prediction of asymmetrically distorted stereoscopic 3d images,” IEEE Transactions on Image Processing, vol. 24, no. 11, pp. 3400–3414, 2015

    2015

  39. [46]

    Mechanisms of stereoscopic vision: the disparity energy model,

    I. Ohzawa, “Mechanisms of stereoscopic vision: the disparity energy model,” Current opinion in neurobiology , vol. 8, no. 4, pp. 509–515, 1998

    1998

  40. [47]

    On binocular rivalry,

    W. J. Levelt, “On binocular rivalry,” Ph.D. dissertation, Van Gorcum Assen, 1965

    1965

  41. [48]

    A hierarchical model of binocular rivalry,

    P. Dayan, “A hierarchical model of binocular rivalry,” Neural Computa- tion, vol. 10, no. 5, pp. 1119–1135, 1998

    1998

  42. [49]

    Predictive coding explains binocular rivalry: An epistemological review,

    J. Hohwy, A. Roepstorff, and K. Friston, “Predictive coding explains binocular rivalry: An epistemological review,”Cognition, vol. 108, no. 3, pp. 687–701, 2008

    2008

  43. [50]

    A review of predictive coding algorithms,

    M. W. Spratling, “A review of predictive coding algorithms,” Brain and cognition, vol. 112, pp. 92–97, 2017

    2017

  44. [51]

    Hume, A treatise of human nature

    D. Hume, A treatise of human nature . Courier Corporation, 2003. IEEE TRANSACTIONS ON INSTRUMENTATION AND MEASUREMENT 13

    2003

  45. [52]

    Predictive coding in the visual cortex: a functional interpretation of some extra-classical receptive-field effects,

    R. P. Rao and D. H. Ballard, “Predictive coding in the visual cortex: a functional interpretation of some extra-classical receptive-field effects,” Nature neuroscience, vol. 2, no. 1, p. 79, 1999

    1999

  46. [53]

    Linear prediction: A tutorial review,

    J. Makhoul, “Linear prediction: A tutorial review,” Proceedings of the IEEE, vol. 63, no. 4, pp. 561–580, 1975

    1975

  47. [54]

    Activity changes in early visual cortex reflect monkeys’ percepts during binocular rivalry,

    D. A. Leopold and N. K. Logothetis, “Activity changes in early visual cortex reflect monkeys’ percepts during binocular rivalry,” Nature, vol. 379, no. 6565, pp. 549–553, 1996

    1996

  48. [56]

    Functional integration and inference in the brain,

    K. Friston, “Functional integration and inference in the brain,” Progress in neurobiology, vol. 68, no. 2, pp. 113–143, 2002

    2002

  49. [57]

    Object perception as bayesian inference,

    D. Kersten, P. Mamassian, and A. Yuille, “Object perception as bayesian inference,” Annu. Rev. Psychol., vol. 55, pp. 271–304, 2004

    2004

  50. [58]

    Deep image prior,

    D. Ulyanov, A. Vedaldi, and V . Lempitsky, “Deep image prior,” in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2018, pp. 9446–9454

    2018

  51. [59]

    End-to- end blind image quality assessment using deep neural networks,

    K. Ma, W. Liu, K. Zhang, Z. Duanmu, Z. Wang, and W. Zuo, “End-to- end blind image quality assessment using deep neural networks,” IEEE Transactions on Image Processing, vol. 27, no. 3, pp. 1202–1213, 2017

    2017

  52. [60]

    Waterloo exploration database: New challenges for image quality assessment models,

    K. Ma, Z. Duanmu, Q. Wu, Z. Wang, H. Yong, H. Li, and L. Zhang, “Waterloo exploration database: New challenges for image quality assessment models,” IEEE Transactions on Image Processing , vol. 26, no. 2, pp. 1004–1016, 2016

    2016

  53. [61]

    A statistical evaluation of recent full reference image quality assessment algorithms,

    H. R. Sheikh, M. F. Sabir, and A. C. Bovik, “A statistical evaluation of recent full reference image quality assessment algorithms,” IEEE Transactions on image processing, vol. 15, no. 11, pp. 3440–3451, 2006

    2006

  54. [62]

    Subjective evaluation of stereoscopic image quality,

    A. K. Moorthy, C.-C. Su, A. Mittal, and A. C. Bovik, “Subjective evaluation of stereoscopic image quality,” Signal Processing: Image Communication, vol. 28, no. 8, pp. 870–883, 2013

    2013

  55. [63]

    Neural bases of binocular rivalry,

    F. Tong, M. Meng, and R. Blake, “Neural bases of binocular rivalry,” Trends in cognitive sciences , vol. 10, no. 11, pp. 502–511, 2006

    2006

  56. [64]

    End-to-end optimized image compression,

    J. Ball ´e, V . Laparra, and E. P. Simoncelli, “End-to-end optimized image compression,” arXiv preprint arXiv:1611.01704 , 2016

    2016 arXiv

  57. [65]

    Vari- ational image compression with a scale hyperprior,

    J. Ball ´e, D. Minnen, S. Singh, S. J. Hwang, and N. Johnston, “Vari- ational image compression with a scale hyperprior,” arXiv preprint arXiv:1802.01436, 2018

    2018 arXiv

  58. [66]

    Unsupervised representation learning with deep convolutional generative adversarial networks,

    A. Radford, L. Metz, and S. Chintala, “Unsupervised representation learning with deep convolutional generative adversarial networks,”arXiv preprint arXiv:1511.06434, 2015

    2015 arXiv

  59. [67]

    Density modeling of images using a generalized normalization transformation,

    J. Ball ´e, V . Laparra, and E. P. Simoncelli, “Density modeling of images using a generalized normalization transformation,”arXiv preprint arXiv:1511.06281, 2015

    2015 arXiv

  60. [68]

    Mistaking a house for a face: neural correlates of misperception in healthy humans,

    C. Summerfield, T. Egner, J. Mangels, and J. Hirsch, “Mistaking a house for a face: neural correlates of misperception in healthy humans,” Cerebral Cortex, vol. 16, no. 4, pp. 500–508, 2005

    2005

  61. [69]

    Deep sparse rectifier neural networks,

    X. Glorot, A. Bordes, and Y . Bengio, “Deep sparse rectifier neural networks,” in Proceedings of the fourteenth international conference on artificial intelligence and statistics , 2011, pp. 315–323

    2011

  62. [70]

    Rectified linear units improve restricted boltz- mann machines,

    V . Nair and G. E. Hinton, “Rectified linear units improve restricted boltz- mann machines,” in Proceedings of the 27th international conference on machine learning (ICML-10) , 2010, pp. 807–814

    2010

  63. [71]

    Identity mappings in deep residual networks,

    K. He, X. Zhang, S. Ren, and J. Sun, “Identity mappings in deep residual networks,” in European conference on computer vision . Netherlands: Springer, 2016, pp. 630–645

    2016

  64. [72]

    A survey on transfer learning,

    S. J. Pan and Q. Yang, “A survey on transfer learning,” IEEE Trans- actions on knowledge and data engineering , vol. 22, no. 10, pp. 1345– 1359, 2009

    2009

  65. [73]

    Final report from the video quality experts group on the validation of objective models of video quality assessment, phase ii,

    Video Quality Experts Group et al., “Final report from the video quality experts group on the validation of objective models of video quality assessment, phase ii,” 2003 VQEG, 2003

    2003

  66. [74]

    On the accuracy of objective image and video quality models: New methodology for performance evaluation,

    L. Krasula, K. Fliegel, P. Le Callet, and M. Kl ´ıma, “On the accuracy of objective image and video quality models: New methodology for performance evaluation,” in 2016 Eighth International Conference on Quality of Multimedia Experience (QoMEX) . IEEE, 2016, pp. 1–6

    2016

  67. [75]

    Data augmentation for improving deep learning in image classification problem,

    A. Mikołajczyk and M. Grochowski, “Data augmentation for improving deep learning in image classification problem,” in 2018 international interdisciplinary PhD workshop (IIPhDW) . IEEE, 2018, pp. 117–122

    2018

  68. [76]

    Convolutional neural networks for no-reference image quality assessment,

    L. Kang, P. Ye, Y . Li, and D. Doermann, “Convolutional neural networks for no-reference image quality assessment,” in Proceedings of the IEEE conference on computer vision and pattern recognition, 2014, pp. 1733– 1740

    2014

  69. [77]

    Deep predictive coding net- works for video prediction and unsupervised learning,

    W. Lotter, G. Kreiman, and D. Cox, “Deep predictive coding net- works for video prediction and unsupervised learning,” arXiv preprint arXiv:1605.08104, 2016

    2016 arXiv

Pith tools

Reviewed August 14, 2026 · model on record in the stance chip above.