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Le Callet, P.[Patrick],
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JIVP(2008), No. 2008, pp. xx-yy.
DOI Link
0903
BibRef
Earlier:
Using disparity for quality assessment of stereoscopic images,
ICIP08(389-392).
IEEE DOI
0810
BibRef
Moorthy, A.K.[Anush Krishna],
Su, C.C.[Che-Chun],
Mittal, A.[Anish],
Bovik, A.C.[Alan Conrad],
Subjective evaluation of stereoscopic image quality,
SP:IC(28), No. 8, 2013, pp. 870-883.
Elsevier DOI
1309
Stereoscopic quality
BibRef
Chen, M.J.[Ming-Jun],
Su, C.C.[Che-Chun],
Kwon, D.K.[Do-Kyoung],
Cormack, L.K.[Lawrence K.],
Bovik, A.C.[Alan C.],
Full-reference quality assessment of stereopairs accounting for
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SP:IC(28), No. 9, 2013, pp. 1143-1155.
Elsevier DOI
1310
Binocular rivalry
BibRef
Chen, M.J.[Ming-Jun],
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Bovik, A.C.[Alan C.],
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IP(22), No. 9, 2013, pp. 3379-3391.
IEEE DOI
1308
feature extraction
BibRef
Shao, F.[Feng],
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Lin, W.S.[Wei-Si],
Jiang, G.Y.[Gang-Yi],
Yu, M.[Mei],
Using Binocular Feature Combination for Blind Quality Assessment of
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SPLetters(22), No. 10, October 2015, pp. 1548-1551.
IEEE DOI
1506
feature extraction
See also Toward a Blind Quality Predictor for Screen Content Images.
See also Monocular and Binocular Interactions Oriented Deformable Convolutional Networks for Blind Quality Assessment of Stereoscopic Omnidirectional Images.
BibRef
Yang, J.C.[Jia-Chen],
Liu, Y.[Yun],
Gao, Z.Q.[Zhi-Qun],
Chu, R.R.[Rong-Rong],
Song, Z.J.[Zhan-Jie],
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for binocular combination behavior,
JVCIR(31), No. 1, 2015, pp. 138-145.
Elsevier DOI
1508
Binocular vision
BibRef
Yang, J.C.[Jia-Chen],
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Lu, W.[Wen],
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PRL(127), 2019, pp. 48-55.
Elsevier DOI
1911
Blind stereoscopic image quality assessment, Ocular dominance,
Adding channel, Subtracting channel
BibRef
Ding, Y.[Yong],
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Zhu, X.L.[Xiao-Lei],
Andrey, K.[Krylov],
Stereoscopic image quality assessment by analysing visual hierarchical
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IET-IPR(13), No. 10, 22 August 2019, pp. 1608-1615.
DOI Link
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BibRef
Ryu, S.C.[Seung-Chul],
Sohn, K.H.[Kwang-Hoon],
No-Reference Quality Assessment for Stereoscopic Images Based on
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CirSysVideo(24), No. 4, April 2014, pp. 591-602.
IEEE DOI
1405
computer vision
BibRef
Ryu, S.C.[Seung-Chul],
Kim, D.H.[Dong Hyun],
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Stereoscopic image quality metric based on binocular perception model,
ICIP12(609-612).
IEEE DOI
1302
BibRef
Ryu, S.C.[Seung-Chul],
Sohn, K.H.[Kwang-Hoon],
No-reference perceptual blur model based on inherent sharpness,
ICIP14(580-584)
IEEE DOI
1502
Computational modeling
BibRef
Ryu, S.C.[Seung-Chul],
Kim, S.[Seungryong],
Sohn, K.H.[Kwang-Hoon],
Synthesis quality prediction model based on distortion intolerance,
ICIP14(585-589)
IEEE DOI
1502
Color
BibRef
Ryu, S.C.[Seung-Chul],
Ham, B.[Bumsub],
Sohn, K.H.[Kwang-Hoon],
Contextual information based visual saliency model,
ICIP13(201-205)
IEEE DOI
1402
Computational modeling
BibRef
Zhou, W.,
Jiang, G.Y.[Gang-Yi],
Yu, M.[Mei],
Shao, F.[Feng],
Peng, Z.J.[Zong-Ju],
PMFS: A Perceptual Modulated Feature Similarity Metric for
Stereoscopic Image Quality Assessment,
SPLetters(21), No. 8, August 2014, pp. 1003-1006.
IEEE DOI
1406
Filtering
BibRef
Zhou, W.[Wujie],
Jiang, G.Y.[Gang-Yi],
Yu, M.[Mei],
Shao, F.[Feng],
Peng, Z.J.[Zong-Ju],
Reduced-reference stereoscopic image quality assessment based on view
and disparity zero-watermarks,
SP:IC(29), No. 1, 2014, pp. 167-176.
Elsevier DOI
1402
Three dimensional TV
BibRef
Jiang, G.Y.[Gang-Yi],
He, M.L.[Mei-Ling],
Yu, M.[Mei],
Shao, F.[Feng],
Peng, Z.J.[Zong-Ju],
Perceptual stereoscopic image quality assessment method with tensor
decomposition and manifold learning,
IET-IPR(12), No. 5, May 2018, pp. 810-818.
DOI Link
1804
See also new tone-mapped image quality assessment approach for high dynamic range imaging system, A.
BibRef
Zhou, J.M.[Jun-Ming],
Jiang, G.Y.[Gang-Yi],
Mao, X.Y.[Xiang-Ying],
Yu, M.[Mei],
Shao, F.[Feng],
Peng, Z.J.[Zong-Ju],
Zhang, Y.[Yun],
Subjective quality analyses of stereoscopic images in 3DTV system,
VCIP11(1-4).
IEEE DOI
1201
BibRef
Shao, F.[Feng],
Li, K.,
Lin, W.,
Jiang, G.Y.[Gang-Yi],
Dai, Q.,
Learning Blind Quality Evaluator for Stereoscopic Images Using Joint
Sparse Representation,
MultMed(18), No. 10, October 2016, pp. 2104-2114.
IEEE DOI
1610
Computational modeling
BibRef
Yu, M.[Mei],
Zheng, K.[Kaihui],
Jiang, G.Y.[Gang-Yi],
Shao, F.[Feng],
Peng, Z.J.[Zong-Ju],
Binocular Perception Based Reduced-Reference Stereo Video Quality
Assessment Method,
JVCIR(38), No. 1, 2016, pp. 246-255.
Elsevier DOI
1605
Stereo video quality assessment
BibRef
Jiang, Q.P.[Qiu-Ping],
Shao, F.[Feng],
Jiang, G.Y.[Gang-Yi],
Yu, M.[Mei],
Peng, Z.J.[Zong-Ju],
Supervised Dictionary Learning for Blind Image Quality Assessment
Using Quality-Constraint Sparse Coding,
JVCIR(33), No. 1, 2015, pp. 123-133.
Elsevier DOI
1512
Award, JVCI, HM.
BibRef
Earlier:
Supervised Dictionary Learning for Blind Image Quality Assessment,
VCIP15(1-4)
IEEE DOI
1605
Blind image quality assessment (BIQA).
Databases
See also Learning Receptive Fields and Quality Lookups for Blind Quality Assessment of Stereoscopic Images.
BibRef
Shao, F.[Feng],
Gao, Y.,
Li, F.,
Jiang, G.Y.[Gang-Yi],
Toward a Blind Quality Predictor for Screen Content Images,
SMCS(48), No. 9, September 2018, pp. 1521-1530.
IEEE DOI
1809
feature extraction, image representation,
conduct global sparse representation, quality vectors,
sparse representation
See also Using Binocular Feature Combination for Blind Quality Assessment of Stereoscopic Images.
BibRef
Bai, Y.Q.[Yong-Qiang],
Zhu, Z.J.[Zhong-Jie],
Jiang, G.Y.[Gang-Yi],
Sun, H.F.[Hui-Fang],
Blind Quality Assessment of Screen Content Images Via Macro-Micro
Modeling of Tensor Domain Dictionary,
MultMed(23), 2021, pp. 4259-4271.
IEEE DOI
2112
Feature extraction, Tensors, Dictionaries, Image color analysis,
Image quality, Image coding, Mathematical model,
dictionary learning
BibRef
Zheng, K.[Kaihui],
Yu, M.[Mei],
Jin, X.[Xin],
Jiang, G.Y.[Gang-Yi],
Peng, Z.J.[Zong-Ju],
Shao, F.[Fen],
New reduced-reference objective stereo image quality assessment model
based on human visual system,
3DTV-CON14(1-4)
IEEE DOI
1409
Gaussian distribution
BibRef
Du, B.Z.[Bao-Zhen],
Yu, M.[Mei],
Jiang, G.Y.[Gang-Yi],
Zhang, Y.[Yun],
Shao, F.[Feng],
Peng, Z.J.[Zong-Ju],
Zhu, T.Z.[Tian-Zhi],
Novel visibility threshold model for asymmetrically distorted
stereoscopic images,
VCIP16(1-4)
IEEE DOI
1701
Complexity theory
BibRef
Md, S.K.[Sameeulla Khan],
Appina, B.[Balasubramanyam],
Channappayya, S.S.[Sumohana S.],
Full-Reference Stereo Image Quality Assessment Using Natural Stereo
Scene Statistics,
SPLetters(22), No. 11, November 2015, pp. 1985-1989.
IEEE DOI
1509
Gaussian distribution
BibRef
Manasa, K.,
Channappayya, S.S.[Sumohana S.],
An Optical Flow-Based Full Reference Video Quality Assessment
Algorithm,
IP(25), No. 6, June 2016, pp. 2480-2492.
IEEE DOI
1605
BibRef
Earlier:
An optical flow-based no-reference video quality assessment algorithm,
ICIP16(2400-2404)
IEEE DOI
1610
Databases
eigenvalues and eigenfunctions
BibRef
Appina, B.,
Jalli, A.,
Battula, S.S.,
Channappayya, S.S.,
No-Reference Stereoscopic Video Quality Assessment Algorithm Using
Joint Motion and Depth Statistics,
ICIP18(2800-2804)
IEEE DOI
1809
Computational modeling, Databases, Feature extraction,
Video sequences, Training, Quality assessment, Estimation,
Natural Scene Statistics
BibRef
Appina, B.[Balasubramanyam],
Channappayya, S.S.[Sumohana S.],
Full-Reference 3-D Video Quality Assessment Using Scene Component
Statistical Dependencies,
SPLetters(25), No. 6, June 2018, pp. 823-827.
IEEE DOI
1806
Gaussian distribution, covariance matrices,
eigenvalues and eigenfunctions, statistical analysis,
stereo video
See also No-Reference Video Quality Assessment Using Natural Spatiotemporal Scene Statistics.
BibRef
Appina, B.[Balasubramanyam],
Khan, S.[Sameeulla],
Channappayya, S.S.[Sumohana S.],
No-reference Stereoscopic Image Quality Assessment Using Natural
Scene Statistics,
SP:IC(43), No. 1, 2016, pp. 1-14.
Elsevier DOI
1604
Natural scene statistics
BibRef
Khan, S.[Sameeulla],
Channappayya, S.S.[Sumohana S.],
Estimating Depth-Salient Edges and Its Application to Stereoscopic
Image Quality Assessment,
IP(27), No. 12, December 2018, pp. 5892-5903.
IEEE DOI
1810
Image edge detection,
Quality assessment, Databases,
image gradient
BibRef
Zhang, Y.[Yi],
Chandler, D.M.,
3D-MAD: A Full Reference Stereoscopic Image Quality Estimator Based
on Binocular Lightness and Contrast Perception,
IP(24), No. 11, November 2015, pp. 3810-3825.
IEEE DOI
1509
statistical analysis
BibRef
Wang, J.H.[Ji-Heng],
Rehman, A.,
Zeng, K.[Kai],
Wang, S.Q.[Shi-Qi],
Wang, Z.[Zhou],
Quality Prediction of Asymmetrically Distorted Stereoscopic 3D Images,
IP(24), No. 11, November 2015, pp. 3400-3414.
IEEE DOI
1509
distortion
BibRef
Wang, J.H.[Ji-Heng],
Wang, S.Q.[Shi-Qi],
Ma, K.,
Wang, Z.[Zhou],
Perceptual Depth Quality in Distorted Stereoscopic Images,
IP(26), No. 3, March 2017, pp. 1202-1215.
IEEE DOI
1703
stereo image processing
BibRef
Qi, F.,
Zhao, D.,
Gao, W.,
Reduced Reference Stereoscopic Image Quality Assessment Based on
Binocular Perceptual Information,
MultMed(17), No. 12, December 2015, pp. 2338-2344.
IEEE DOI
1512
Databases
BibRef
Xiang, S.,
Yu, L.,
Chen, C.W.,
No-Reference Depth Assessment Based on Edge Misalignment Errors for T+D
Images,
IP(25), No. 3, March 2016, pp. 1479-1494.
IEEE DOI
1602
Distortion. Quality assessment of depth images.
BibRef
Zhou, W.,
Yu, L.,
Binocular Responses for No-Reference 3D Image Quality Assessment,
MultMed(18), No. 6, June 2016, pp. 1077-1084.
IEEE DOI
1605
Bit error rate
BibRef
Jung, Y.J.,
Kim, H.G.,
Ro, Y.M.,
Critical Binocular Asymmetry Measure for the Perceptual Quality
Assessment of Synthesized Stereo 3D Images in View Synthesis,
CirSysVideo(26), No. 7, July 2016, pp. 1201-1214.
IEEE DOI
1608
extrapolation
BibRef
Zhang, W.[Wei],
Qu, C.[Chenfei],
Ma, L.[Lin],
Guan, J.W.[Jing-Wei],
Huang, R.[Rui],
Learning structure of stereoscopic image for no-reference quality
assessment with convolutional neural network,
PR(59), No. 1, 2016, pp. 176-187.
Elsevier DOI
1609
Stereoscopic image
BibRef
Lv, Y.Q.[Ya-Qi],
Yu, M.[Mei],
Jiang, G.Y.[Gang-Yi],
Shao, F.[Feng],
Peng, Z.J.[Zong-Ju],
Chen, F.[Fen],
No-reference Stereoscopic Image Quality Assessment Using Binocular
Self-similarity and Deep Neural Network,
SP:IC(47), No. 1, 2016, pp. 346-357.
Elsevier DOI
1610
Stereoscopic image quality assessment
BibRef
Tamboli, R.R.[Roopak R.],
Appina, B.[Balasubramanyam],
Channappayya, S.S.[Sumohana S.],
Jana, S.[Soumya],
Super-multiview content with high angular resolution: 3D quality
assessment on horizontal-parallax lightfield display,
SP:IC(47), No. 1, 2016, pp. 42-55.
Elsevier DOI
1610
Full-reference 3D Quality Assessment
BibRef
Tamboli, R.R.[Roopak R.],
Appina, B.[Balasubramanyam],
Channappayya, S.S.[Sumohana S.],
Jana, S.[Soumya],
Achieving high angular resolution via view synthesis:
Quality assessment of 3D content on super multiview lightfield display,
IC3D17(1-8)
IEEE DOI
1804
image sequences, stereo image processing, visual perception,
View Synthesis
BibRef
Geng, X.Q.[Xian-Qiu],
Shen, L.Q.[Li-Quan],
Li, K.[Kai],
An, P.[Ping],
A stereoscopic image quality assessment model based on independent
component analysis and binocular fusion property,
SP:IC(52), No. 1, 2017, pp. 54-63.
Elsevier DOI
1701
Stereoscopic quality assessment
BibRef
Ma, J.[Jian],
An, P.[Ping],
Shen, L.Q.[Li-Quan],
Li, K.[Kai],
Joint binocular energy-contrast perception for quality assessment of
stereoscopic images,
SP:IC(65), 2018, pp. 33-45.
Elsevier DOI
1805
Binocular visual system, Stereoscopic image quality,
Full reference, CSF, Binocular energy-contrast perception
BibRef
Geng, X.Q.[Xian-Qiu],
Shen, L.Q.[Li-Quan],
An, P.[Ping],
Liu, Z.,
Using independent component analysis and binocular combination for
stereoscopic image quality assessment,
VCIP16(1-4)
IEEE DOI
1701
Feature extraction
BibRef
Chen, F.[Fen],
Jiao, R.Z.[Ren-Zhi],
Peng, Z.J.[Zong-Ju],
Jiang, G.Y.[Gang-Yi],
Yu, M.[Mei],
Virtual view quality assessment based on shift compensation and
visual masking effect,
JVCIR(43), No. 1, 2017, pp. 41-49.
Elsevier DOI
1702
Depth image based rendering
BibRef
Ko, H.[Hyunsuk],
Song, R.[Rui],
Kuo, C.C.J.[C.C. Jay],
A ParaBoost stereoscopic image quality assessment (PBSIQA) system,
JVCIR(45), No. 1, 2017, pp. 156-169.
Elsevier DOI
1704
Stereoscopic images
BibRef
Hachicha, W.,
Kaaniche, M.,
Beghdadi, A.,
Cheikh, F.A.,
No-reference stereo image quality assessment based on joint wavelet
decomposition and statistical models,
SP:IC(54), No. 1, 2017, pp. 107-117.
Elsevier DOI
1704
Stereo image
BibRef
Delis, S.,
Mademlis, I.,
Nikolaidis, N.,
Pitas, I.,
Automatic Detection of 3D Quality Defects in Stereoscopic Videos
Using Binocular Disparity,
CirSysVideo(27), No. 5, May 2017, pp. 977-991.
IEEE DOI
1705
Estimation, Measurement, Quality assessment,
Stereo image processing, Videos,
Visualization, 3D quality, binocular disparity, stereoscopic video,
visual, discomfort
BibRef
Jiang, G.Y.[Gang-Yi],
Xu, H.Y.[Hai-Yong],
Yu, M.[Mei],
Luo, T.[Ting],
Zhang, Y.[Yun],
Stereoscopic image quality assessment by learning non-negative matrix
factorization-based color visual characteristics and considering
binocular interactions,
JVCIR(46), No. 1, 2017, pp. 269-279.
Elsevier DOI
1706
Stereoscopic image quality assessment
BibRef
Zhou, W.[Wujie],
Yu, L.[Lu],
Zhou, Y.[Yang],
Qiu, W.W.[Wei-Wei],
Wu, M.W.[Ming-Wei],
Luo, T.[Ting],
Blind quality estimator for 3D images based on binocular combination
and extreme learning machine,
PR(71), No. 1, 2017, pp. 207-217.
Elsevier DOI
1707
3D, image, quality, assessment
BibRef
Kawabata, N.[Norifumi],
Miyao, M.[Masaru],
Multi-View 3D CG Image Quality Assessment for Contrast Enhancement
Based on S-CIELAB Color Space,
IEICE(E100-D), No. 7, July 2017, pp. 1448-1462.
WWW Link.
1708
BibRef
Oh, H.,
Ahn, S.,
Kim, J.,
Lee, S.,
Blind Deep S3D Image Quality Evaluation via Local to Global Feature
Aggregation,
IP(26), No. 10, October 2017, pp. 4923-4936.
IEEE DOI
1708
Feature extraction, Image quality, Machine learning, Measurement,
Visualization, Stereoscopic 3D, convolutional neural network,
deep learning, local feature aggregation, no-reference, image,
quality, assessment
BibRef
Ahn, S.,
Lee, S.,
Deep Blind Video Quality Assessment Based on Temporal Human
Perception,
ICIP18(619-623)
IEEE DOI
1809
Feature extraction, Quality assessment, Video recording, Databases,
Streaming media, Nonlinear distortion, Video quality assessment,
temporal pooling
BibRef
Kim, J.,
Lee, S.,
Deep Learning of Human Visual Sensitivity in Image Quality Assessment
Framework,
CVPR17(1969-1977)
IEEE DOI
1711
Computational modeling, Convolution, Databases, Distortion,
Image quality, Sensitivity, Visualization
BibRef
Karimi, M.,
Nejati, M.,
Soroushmehr, S.M.R.,
Samavi, S.,
Karimi, N.,
Najarian, K.,
Blind Stereo Quality Assessment Based on Learned Features From
Binocular Combined Images,
MultMed(19), No. 11, November 2017, pp. 2475-2489.
IEEE DOI
1710
Dictionaries, Estimation, Feature extraction, Phase distortion,
binocular combination, no-reference (NR) image quality assessment (IQA),
sparse representation, stereo image quality assessment (SIQA),
unsupervised, feature, learning
BibRef
Liu, Z.G.[Zhi-Guo],
Yang, C.[Chifu],
Rho, S.[Seungmin],
Liu, S.H.[Shao-Hui],
Jiang, F.[Feng],
Structured entropy of primitive:
Big Data-Based Stereoscopic Image Quality Assessment,
IET-IPR(11), No. 10, October 2017, pp. 854-860.
DOI Link
1710
BibRef
Chen, Z.,
Zhou, W.,
Li, W.,
Blind Stereoscopic Video Quality Assessment:
From Depth Perception to Overall Experience,
IP(27), No. 2, February 2018, pp. 721-734.
IEEE DOI
1712
Image quality, Measurement, Quality assessment,
Stereo image processing,
natural scene statistic
BibRef
Zhao, H.T.[Hai-Tao],
Zhang, B.[Bing],
Shang, J.L.[Jia-Li],
Liu, J.[Jiangui],
Li, D.[Dong],
Chen, Y.Y.[Yan-Yan],
Zuo, Z.L.[Zheng-Li],
Chen, Z.C.[Zheng-Chao],
Aerial photography flight quality assessment with GPS/INS and DEM
data,
PandRS(135), No. Supplement C, 2018, pp. 60-73.
Elsevier DOI
1712
Aerial photography, Flight quality, Accuracy assessment,
GPS/INS, DEM, Overlap, Crab angle, Tilt angle
BibRef
Jiang, Q.P.[Qiu-Ping],
Shao, F.[Feng],
Lin, W.S.[Wei-Si],
Jiang, G.Y.[Gang-Yi],
Learning a referenceless stereopair quality engine with deep
nonnegativity constrained sparse autoencoder,
PR(76), No. 1, 2018, pp. 242-255.
Elsevier DOI
1801
Image quality assessment
BibRef
Jiang, G.Y.[Gang-Yi],
Liu, S.S.[Shan-Shan],
Yu, M.[Mei],
Shao, F.[Feng],
Peng, Z.J.[Zong-Ju],
Chen, F.[Fen],
No reference stereo video quality assessment based on motion feature
in tensor decomposition domain,
JVCIR(50), 2018, pp. 247-262.
Elsevier DOI
1802
No reference stereo video quality assessment,
Tensor decomposition, Motion feature, Entropy, Random forest
BibRef
Shen, J.B.[Jian-Bing],
Zhang, Y.[Yan],
Liang, Z.Y.[Zhi-Yuan],
Liu, C.[Chang],
Sun, H.Q.[Han-Qiu],
Hao, X.P.[Xiao-Peng],
Liu, J.H.[Jian-Hong],
Yang, J.[Jian],
Shao, L.[Ling],
Robust Stereoscopic Crosstalk Prediction,
CirSysVideo(28), No. 5, May 2018, pp. 1158-1168.
IEEE DOI
1805
Brightness, Crosstalk, Image color analysis, Measurement,
Stereo image processing, objective metric
BibRef
Yao, Y.[Yang],
Shen, L.Q.[Li-Quan],
An, P.[Ping],
Bivariate analysis of 3D structure for stereoscopic image quality
assessment,
SP:IC(65), 2018, pp. 128-140.
Elsevier DOI
1805
No reference, Stereoscopic image quality assessment,
Bivariate analysis, Natural scene statistics, Machine learning
BibRef
Shao, F.,
Gao, Y.,
Jiang, Q.,
Jiang, G.,
Ho, Y.,
Multistage Pooling for Blind Quality Prediction of Asymmetric
Multiply-Distorted Stereoscopic Images,
MultMed(20), No. 10, October 2018, pp. 2605-2619.
IEEE DOI
1810
distortion, image representation, stereo image processing,
asymmetric distortions, multiple distortions,
multi-stage pooling
BibRef
Yang, J.,
Sim, K.,
Gao, X.,
Lu, W.,
Meng, Q.,
Li, B.,
A Blind Stereoscopic Image Quality Evaluator With Segmented Stacked
Autoencoders Considering the Whole Visual Perception Route,
IP(28), No. 3, March 2019, pp. 1314-1328.
IEEE DOI
1812
Image color analysis, Measurement, Visualization,
Image edge detection, Retina, Ganglia, Feature extraction,
color quality
BibRef
Yang, J.,
Sim, K.,
Lu, W.,
Jiang, B.,
Predicting Stereoscopic Image Quality via Stacked Auto-Encoders Based
on Stereopsis Formation,
MultMed(21), No. 7, July 2019, pp. 1750-1761.
IEEE DOI
1906
Feature extraction, Measurement,
Image quality, Distortion, Stereo image processing,
cyclopean channel
BibRef
Fang, Y.M.[Yu-Ming],
Yan, J.B.[Jie-Bin],
Liu, X.L.[Xue-Lin],
Wang, J.H.[Ji-Heng],
Stereoscopic Image Quality Assessment by Deep Convolutional Neural
Network,
JVCIR(58), 2019, pp. 400-406.
Elsevier DOI
1901
Image quality assessment, Stereoscopic images, No reference,
Convolutional neural network
See also Stereoscopic Image Retargeting Based on Deep Convolutional Neural Network.
BibRef
Shao, X.[Xiao],
Liu, M.Q.[Meng-Qing],
Li, Z.[Zihan],
Zhang, P.Y.[Pei-Yun],
CPDINet: Blind image quality assessment via a content perception and
distortion inference network,
IET-IPR(16), No. 7, 2022, pp. 1973-1987.
DOI Link
2205
BibRef
Pan, Z.Q.[Zhao-Qing],
Zhang, H.[Hao],
Lei, J.J.[Jian-Jun],
Fang, Y.M.[Yu-Ming],
Shao, X.[Xiao],
Ling, N.[Nam],
Kwong, S.[Sam],
DACNN: Blind Image Quality Assessment via a Distortion-Aware
Convolutional Neural Network,
CirSysVideo(32), No. 11, November 2022, pp. 7518-7531.
IEEE DOI
2211
Distortion, Feature extraction, Image quality, Databases, Fuses,
Semantics, Knowledge engineering, Blind image quality assessment,
fusion network
BibRef
Chen, L.[Lei],
Zhao, J.[Jiying],
No-reference perceptual quality assessment of stereoscopic images
based on binocular visual characteristics,
SP:IC(76), 2019, pp. 1-10.
Elsevier DOI
1906
3D image quality assessment, No-reference,
Local amplitude and phase, Visual saliency, Support vector regression
BibRef
Zhou, W.,
Chen, Z.,
Li, W.,
Dual-Stream Interactive Networks for No-Reference Stereoscopic Image
Quality Assessment,
IP(28), No. 8, August 2019, pp. 3946-3958.
IEEE DOI
1907
stereo image processing, visual perception,
dual-stream interactive networks,
end-to-end prediction
BibRef
Li, S.[Sumei],
Han, X.[Xu],
Chang, Y.L.[Yong-Li],
Adaptive Cyclopean Image-Based Stereoscopic Image-Quality Assessment
Using Ensemble Learning,
MultMed(21), No. 10, October 2019, pp. 2616-2624.
IEEE DOI
1910
feature extraction, learning (artificial intelligence),
stereo image processing, visual databases,
stereoscopic image quality assessment
BibRef
Li, S.[Sumei],
Ding, Y.X.[Yi-Xiu],
Chang, Y.L.[Yong-Li],
No-reference stereoscopic image quality assessment based on cyclopean
image and enhanced image,
SIViP(14), No. 3, April 2020, pp. 565-573.
WWW Link.
2004
BibRef
Li, S.[Sumei],
Zhao, P.[Ping],
Chang, Y.L.[Yong-Li],
No-Reference Stereoscopic Image Quality Assessment Based On Visual
Attention Mechanism,
VCIP20(326-329)
IEEE DOI
2102
Visualization, Dams, Stereo image processing, Feature extraction,
Databases, Data models, Image quality, stereoscopic image,
data selection
BibRef
Li, S.[Sumei],
Li, Y.Y.[Yue-Yang],
Han, Y.T.[Yong-Tian],
Stereoscopic image quality assessment considering visual mechanism
and multi-loss constraints,
JVCIR(79), 2021, pp. 103255.
Elsevier DOI
2109
Image quality assessment, Binocular information, Multi-loss, Proxy label
BibRef
Han, Y.T.[Yong-Tian],
Li, S.[Sumei],
Yue, G.H.[Guang-Hui],
Chang, Y.L.[Yong-Li],
No-Reference Stereoscopic Image Quality Assessment Considering
Multi-loss Constraints,
VCIP20(334-337)
IEEE DOI
2102
Feature extraction, Stereo image processing, Training, Databases,
Task analysis, Adaptive systems, Quality assessment,
convolutional neural network
BibRef
Li, S.[Sumei],
Wang, M.Y.[Ming-Yi],
No-Reference Stereoscopic Image Quality Assessment Based on
Convolutional Neural Network with A Long-Term Feature Fusion,
VCIP20(318-321)
IEEE DOI
2102
Feature extraction, Stereo image processing, Solid modeling,
Fuses, Transform coding, Image quality,
binocular fusion
BibRef
Yang, J.C.[Jia-Chen],
Hou, C.P.[Chun-Ping],
Xu, R.[Ran],
Lei, J.J.[Jian-Jun],
New metric for stereo image quality assessment based on HVS,
IJIST(20), No. 4, December 2010, pp. 301-307.
DOI Link
1011
BibRef
Chang, Y.L.[Yong-Li],
Li, S.[Sumei],
Han, X.[Xu],
Hou, C.P.[Chun-Ping],
Cyclopean Image Based Stereoscopic Image Quality Assessment by Using
Sparse Representation,
ICIP18(2825-2829)
IEEE DOI
1809
Image color analysis, Stereo image processing,
Image reconstruction, Feature extraction, Entropy, Dictionaries, entropy
BibRef
Messai, O.[Oussama],
Hachouf, F.[Fella],
Seghir, Z.A.[Zianou Ahmed],
AdaBoost neural network and cyclopean view for no-reference
stereoscopic image quality assessment,
SP:IC(82), 2020, pp. 115772.
Elsevier DOI
2001
Stereoscopic quality assessment, No-reference,
Binocular rivalry, Cyclopean view, Neural network, AdaBoost
BibRef
Shi, Y.Q.[Yi-Qing],
Guo, W.Z.[Wen-Zhong],
Niu, Y.Z.[Yu-Zhen],
Zhan, J.M.[Jia-Mei],
No-reference stereoscopic image quality assessment using a multi-task
CNN and registered distortion representation,
PR(100), 2020, pp. 107168.
Elsevier DOI
2005
No-reference stereoscopic image quality assessment,
Multi-task learning, Convolutional neural network, Image registration
BibRef
Liu, L.X.[Li-Xiong],
Zhang, J.F.[Jiu-Fa],
Saad, M.A.[Michele A.],
Huang, H.[Hua],
Bovik, A.C.[Alan Conrad],
Blind S3D image quality prediction using classical and non-classical
receptive field models,
SP:IC(87), 2020, pp. 115915.
Elsevier DOI
2007
Stereoscopic quality assessment, No-reference,
Visual perception, Receptive field
BibRef
Zheng, Z.[Zhi],
Liu, Y.[Yun],
Liu, Y.[Yun],
Huang, B.Q.[Bao-Qing],
Yu, H.W.[Hong-Wei],
No-reference stereoscopic images quality assessment method based on
monocular superpixel visual features and binocular visual features?,
JVCIR(71), 2020, pp. 102848.
Elsevier DOI
2009
Image quality evaluation, Superpixel visual patches,
Human visual system, Support vector regression
BibRef
Yang, J.,
Zhao, Y.,
Jiang, B.,
Lu, W.,
Gao, X.,
No-Reference Quality Evaluation of Stereoscopic Video Based on
Spatio-Temporal Texture,
MultMed(22), No. 10, October 2020, pp. 2635-2644.
IEEE DOI
2009
Stereo image processing, Feature extraction, Distortion,
Visualization, Quality assessment, Video recording, Data mining,
local binary patterns from three orthogonal planes (LBP-TOP)
BibRef
Yang, J.,
Zhao, Y.,
Jiang, B.,
Meng, Q.,
Lu, W.,
Gao, X.,
No-Reference Quality Assessment of Stereoscopic Videos With
Inter-Frame Cross on a Content-Rich Database,
CirSysVideo(30), No. 10, October 2020, pp. 3608-3623.
IEEE DOI
2010
Videos, Databases, Quality assessment,
Stereo image processing, Distortion,
TJU-SVQA database
BibRef
Viola, I.,
Cesar, P.,
A Reduced Reference Metric for Visual Quality Evaluation of Point
Cloud Contents,
SPLetters(27), 2020, pp. 1660-1664.
IEEE DOI
1806
Distortion, Visualization,
Distortion measurement, Feature extraction, Geometry, Compression,
reduced reference metric
BibRef
Sun, G.,
Shi, B.,
Chen, X.,
Krylov, A.S.,
Ding, Y.,
Learning Local Quality-Aware Structures of Salient Regions for
Stereoscopic Images via Deep Neural Networks,
MultMed(22), No. 11, November 2020, pp. 2938-2949.
IEEE DOI
2010
Convolutional neural network,
stereoscopic image quality assessment, three-column deep model, visual saliency
BibRef
Zhou, W.J.[Wu-Jie],
Lin, X.Y.[Xin-Yang],
Zhou, X.[Xi],
Lei, J.S.[Jing-Sheng],
Yu, L.[Lu],
Luo, T.[Ting],
Multi-layer fusion network for blind stereoscopic 3D visual quality
prediction,
SP:IC(91), 2021, pp. 116095.
Elsevier DOI
2012
Stereoscopic 3D image, Visual quality prediction, Dual-stream,
Fusion network, Binocular vision
BibRef
Galkandage, C.,
Calic, J.,
Dogan, S.,
Guillemaut, J.Y.,
Full-Reference Stereoscopic Video Quality Assessment Using a Motion
Sensitive HVS Model,
CirSysVideo(31), No. 2, February 2021, pp. 452-466.
IEEE DOI
2102
Stereo image processing, Quality assessment, Video recording,
Sensitivity, Physiology, Brain modeling,
quality of experience
BibRef
Zhang, Y.[Yi],
Chandler, D.M.[Damon M.],
Mou, X.[Xuanqin],
Quality assessment of multiply and singly distorted stereoscopic
images via adaptive construction of cyclopean views,
SP:IC(94), 2021, pp. 116175.
Elsevier DOI
2104
No reference quality assessment, Stereoscopic image,
Multiple distortions, Distortion parameter estimation
BibRef
Li, L.[Ling],
Chen, C.[Chunyi],
Hu, X.J.[Xiao-Juan],
Liu, Y.B.[Yun-Biao],
Liang, W.D.[Wei-Dong],
Visual perception of computer-generated stereoscopic pictures:
Toward the impact of image resolution,
SP:IC(96), 2021, pp. 116301.
Elsevier DOI
2106
Human perception, Image resolution,
Stereoscopic image quality assessment (SIQA), Perceptual difference
BibRef
Lim, P.C.[Pyung-Chae],
Rhee, S.[Sooahm],
Seo, J.[Junghoon],
Kim, J.I.[Jae-In],
Chi, J.[Junhwa],
Lee, S.B.[Suk-Bae],
Kim, T.[Taejung],
An Optimal Image-Selection Algorithm for Large-Scale Stereoscopic
Mapping of UAV Images,
RS(13), No. 11, 2021, pp. xx-yy.
DOI Link
2106
BibRef
Si, J.W.[Jian-Wei],
Yang, H.[Huan],
Huang, B.X.[Bao-Xiang],
Pan, Z.K.[Zhen-Kuan],
Su, H.L.[Hong-Lei],
A full-reference stereoscopic image quality assessment index based on
stable aggregation of monocular and binocular visual features,
IET-IPR(15), No. 8, 2021, pp. 1629-1643.
DOI Link
2106
BibRef
Fang, Y.M.[Yu-Ming],
Sui, X.J.[Xiang-Jie],
Wang, J.H.[Ji-Heng],
Yan, J.B.[Jie-Bin],
Lei, J.J.[Jian-Jun],
Le Callet, P.[Patrick],
Perceptual Quality Assessment for Asymmetrically Distorted
Stereoscopic Video by Temporal Binocular Rivalry,
CirSysVideo(31), No. 8, August 2021, pp. 3010-3024.
IEEE DOI
2108
Visualization, Quality assessment, Stereo image processing,
Distortion, Image quality, Video sequences, Distortion measurement,
motion energy
BibRef
Yang, Q.[Qi],
Chen, H.[Hao],
Ma, Z.[Zhan],
Xu, Y.L.[Yi-Ling],
Tang, R.J.[Rong-Jun],
Sun, J.[Jun],
Predicting the Perceptual Quality of Point Cloud:
A 3D-to-2D Projection-Based Exploration,
MultMed(23), 2021, pp. 3877-3891.
IEEE DOI
2110
Distortion, Image color analysis,
Indexes, Distortion measurement, Image edge detection,
quality assessment
BibRef
Liu, Q.[Qi],
Yuan, H.[Hui],
Su, H.L.[Hong-Lei],
Liu, H.[Hao],
Wang, Y.[Yu],
Yang, H.[Huan],
Hou, J.H.[Jun-Hui],
PQA-Net: Deep No Reference Point Cloud Quality Assessment via
Multi-View Projection,
CirSysVideo(31), No. 12, December 2021, pp. 4645-4660.
IEEE DOI
2112
Measurement, Distortion,
Quality assessment, Feature extraction, Geometry, multi-view
BibRef
Wu, X.J.[Xin-Ju],
Zhang, Y.[Yun],
Fan, C.L.[Chun-Ling],
Hou, J.H.[Jun-Hui],
Kwong, S.[Sam],
Subjective Quality Database and Objective Study of Compressed Point
Clouds With 6DoF Head-Mounted Display,
CirSysVideo(31), No. 12, December 2021, pp. 4630-4644.
IEEE DOI
2112
Geometry, Databases, Distortion,
Monitoring, Degradation, Videos, Point clouds,
six degrees of freedom (6DoF)
BibRef
Liu, Y.[Yun],
Huang, B.[Baoqing],
Yu, H.W.[Hong-Wei],
Zheng, Z.[Zhi],
No-reference stereoscopic image quality evaluator based on human
visual characteristics and relative gradient orientation,
JVCIR(81), 2021, pp. 103354.
Elsevier DOI
2112
Stereoscopic image quality, Binocularity, Monocular feature,
Binocular feature, Features extraction and regression
BibRef
Guan, T.[Tuxin],
Li, C.F.[Chao-Feng],
Zheng, Y.H.[Yu-Hui],
Zhao, S.[Shenghu],
Wu, X.J.[Xiao-Jun],
No-reference stereoscopic image quality assessment on both complex
contourlet and spatial domain via Kernel ELM,
SP:IC(101), 2022, pp. 116547.
Elsevier DOI
2201
No reference stereoscopic image quality assessment,
Complex contourlet transform, Visual discomfort,
Kernel extreme learning machine
BibRef
Hu, J.B.[Jin-Bin],
Wang, X.J.[Xue-Jin],
Chai, X.L.[Xiong-Li],
Shao, F.[Feng],
Jiang, Q.P.[Qiu-Ping],
Deep network based stereoscopic image quality assessment via
binocular summing and differencing,
JVCIR(82), 2022, pp. 103420.
Elsevier DOI
2201
Stereoscopic image quality assessment,
Deep regression network, Binocular summing, Binocular differencing
BibRef
He, Z.Y.[Zhou-Yan],
Jiang, G.Y.[Gang-Yi],
Yu, M.[Mei],
Jiang, Z.[Zhidi],
Peng, Z.[Zongju],
Chen, F.[Fen],
TGP-PCQA: Texture and geometry projection based quality assessment
for colored point clouds,
JVCIR(83), 2022, pp. 103449.
Elsevier DOI
2202
BibRef
Earlier: A1, A2, A4, A3, Only:
Towards A Colored Point Cloud Quality Assessment Method Using Colored
Texture and Curvature Projection,
ICIP21(1444-1448)
IEEE DOI
2201
Colored point cloud, Visual quality assessment,
Texture and geometry projection, Objective quality assessment.
Visualization, Image coding, Image color analysis, Databases,
Feature extraction, Distortion, Visual quality assessment,
curvature projection
BibRef
Hua, L.[Lei],
Yu, M.[Mei],
He, Z.[Zhouyan],
Tu, R.[Renwei],
Jiang, G.Y.[Gang-Yi],
CPC-GSCT: Visual quality assessment for coloured point cloud based on
geometric segmentation and colour transformation,
IET-IPR(16), No. 4, 2022, pp. 1083-1095.
DOI Link
2203
BibRef
Sim, K.[Kyohoon],
Yang, J.C.[Jia-Chen],
Lu, W.[Wen],
Gao, X.B.[Xin-Bo],
Blind Stereoscopic Image Quality Evaluator Based on Binocular
Semantic and Quality Channels,
MultMed(24), 2022, pp. 1389-1398.
IEEE DOI
2204
Semantics, Feature extraction, Stereo image processing,
Image quality, Image recognition, Visualization, Semantic channel,
convolutional neural networks
BibRef
Wang, X.J.[Xue-Jin],
Shao, F.[Feng],
Jiang, Q.P.[Qiu-Ping],
Fu, Z.Q.[Zhen-Qi],
Meng, X.C.[Xiang-Chao],
Gu, K.[Ke],
Ho, Y.S.[Yo-Sung],
Combining Retargeting Quality and Depth Perception Measures for
Quality Evaluation of Retargeted Stereopairs,
MultMed(24), 2022, pp. 2422-2434.
IEEE DOI
2205
Distortion, Visualization, Stereo image processing,
Measurement, Distortion measurement, Depth perception
BibRef
Jiang, H.[Hao],
Jiang, G.Y.[Gang-Yi],
Yu, M.[Mei],
Luo, T.[Ting],
Xu, H.Y.[Hai-Yong],
Multi-Angle Projection Based Blind Omnidirectional Image Quality
Assessment,
CirSysVideo(32), No. 7, July 2022, pp. 4211-4223.
IEEE DOI
2207
Feature extraction, Distortion, Quality assessment, Image quality,
Image color analysis, Visualization, Resists,
tensor space
BibRef
Zhou, W.[Wujie],
Yu, L.[Lu],
Qian, Y.G.[Ya-Guan],
Qiu, W.W.[Wei-Wei],
Zhou, Y.[Yang],
Luo, T.[Ting],
Deep blind quality evaluator for multiply distorted images based on
monogenic binary coding,
JVCIR(60), 2019, pp. 305-311.
Elsevier DOI
1903
Quality assessment, Monogenic binary coding,
Local structural information, Blind prediction, Deep neural network
BibRef
Xiang, J.J.[Jian-Jun],
Jiang, G.Y.[Gang-Yi],
Yu, M.[Mei],
Jiang, Z.[Zhidi],
Ho, Y.S.[Yo-Sung],
No-Reference Light Field Image Quality Assessment Using
Four-Dimensional Sparse Transform,
MultMed(25), 2023, pp. 457-472.
IEEE DOI
2302
Feature extraction, Image coding, Frequency-domain analysis,
Tensors, Principal component analysis, Periodic structures,
spatial-angular quality
BibRef
Xiang, J.J.[Jian-Jun],
Chen, P.[Peng],
Dang, Y.J.[Yuan-Jie],
Liang, R.H.[Rong-Hua],
Jiang, G.Y.[Gang-Yi],
Pseudo Light Field Image and 4D Wavelet-Transform-Based
Reduced-Reference Light Field Image Quality Assessment,
MultMed(26), 2024, pp. 929-943.
IEEE DOI
2402
Wavelet transforms, Data mining, Feature extraction, Redundancy,
Wavelet domain, Spatial resolution, Light fields,
view synthesis
BibRef
Qi, Y.[Yubin],
Jiang, G.Y.[Gang-Yi],
Yu, M.[Mei],
Zhang, Y.[Yun],
Ho, Y.S.[Yo-Sung],
Viewport Perception Based Blind Stereoscopic Omnidirectional Image
Quality Assessment,
CirSysVideo(31), No. 10, October 2021, pp. 3926-3941.
IEEE DOI
2110
Measurement, Visualization, Stereo image processing,
Feature extraction, Image coding, viewport perception
BibRef
Lv, Y.Q.[Ya-Qi],
Jiang, G.Y.[Gang-Yi],
Yu, M.[Mei],
Xu, H.Y.[Hai-Yong],
Shao, F.[Feng],
Liu, S.S.[Shan-Shan],
Difference of Gaussian Statistical Features Based Blind Image Quality
Assessment: A Deep Learning Approach,
ICIP15(2344-2348)
IEEE DOI
1512
Blind image quality assessment
BibRef
Jiang, H.[Hao],
Jiang, G.Y.[Gang-Yi],
Yu, M.[Mei],
Zhang, Y.[Yun],
Yang, Y.[You],
Peng, Z.J.[Zong-Ju],
Chen, F.[Fen],
Zhang, Q.B.[Qing-Bo],
Cubemap-Based Perception-Driven Blind Quality Assessment for
360-degree Images,
IP(30), 2021, pp. 2364-2377.
IEEE DOI
2102
distortion, face recognition, feature extraction,
image representation, cross dataset validation, cubemap projection
BibRef
Shao, F.[Feng],
Lin, W.S.[Wei-Si],
Gu, S.B.[Shan-Bo],
Jiang, G.Y.[Gang-Yi],
Srikanthan, T.[Thambipillai],
Perceptual Full-Reference Quality Assessment of Stereoscopic Images by
Considering Binocular Visual Characteristics,
IP(22), No. 5, May 2013, pp. 1940-1953.
IEEE DOI
1303
BibRef
Shao, F.[Feng],
Li, K.M.[Ke-Meng],
Lin, W.S.[Wei-Si],
Jiang, G.Y.[Gang-Yi],
Yu, M.[Mei],
Dai, Q.H.[Qiong-Hai],
Full-Reference Quality Assessment of Stereoscopic Images by Learning
Binocular Receptive Field Properties,
IP(24), No. 10, October 2015, pp. 2971-2983.
IEEE DOI
1507
Brain modeling
BibRef
Shao, F.[Feng],
Lin, W.S.[Wei-Si],
Wang, S.S.[Shan-Shan],
Jiang, G.Y.[Gang-Yi],
Yu, M.[Mei],
Dai, Q.H.[Qiong-Hai],
Learning Receptive Fields and Quality Lookups for Blind Quality
Assessment of Stereoscopic Images,
Cyber(46), No. 3, March 2016, pp. 730-743.
IEEE DOI
1602
Encoding
See also Supervised Dictionary Learning for Blind Image Quality Assessment Using Quality-Constraint Sparse Coding.
BibRef
Chi, B.W.[Bi-Wei],
Yu, M.[Mei],
Jiang, G.Y.[Gang-Yi],
He, Z.Y.[Zhou-Yan],
Peng, Z.J.[Zong-Ju],
Chen, F.[Fen],
Blind tone mapped image quality assessment with image segmentation
and visual perception,
JVCIR(67), 2020, pp. 102752.
Elsevier DOI
2004
High dynamic range image, Tone mapped image,
Image quality assessment, Image segmentation, Feature clustering
BibRef
Xiang, J.J.[Jian-Jun],
Yu, M.[Mei],
Jiang, G.Y.[Gang-Yi],
Xu, H.Y.[Hai-Yong],
Song, Y.[Yang],
Ho, Y.S.[Yo-Sung],
Pseudo Video and Refocused Images-Based Blind Light Field Image
Quality Assessment,
CirSysVideo(31), No. 7, July 2021, pp. 2575-2590.
IEEE DOI
2107
Visualization, Light fields, Feature extraction,
Quality assessment, Image quality, Shearlet transform
BibRef
Chai, X.L.[Xiong-Li],
Shao, F.[Feng],
Jiang, Q.P.[Qiu-Ping],
Wang, X.J.[Xue-Jin],
Xu, L.[Long],
Ho, Y.S.[Yo-Sung],
Blind Quality Evaluator of Light Field Images by Group-Based
Representations and Multiple Plane-Oriented Perceptual
Characteristics,
MultMed(26), 2024, pp. 607-622.
IEEE DOI
2402
Feature extraction, Visualization, Sensor arrays, Light fields,
Cameras, Electronic mail, Degradation, Light field images,
3D Log-Gabor filters
BibRef
Wang, X.J.[Xue-Jin],
Shao, F.[Feng],
Jiang, Q.P.[Qiu-Ping],
Chai, X.L.[Xiong-Li],
Meng, X.C.[Xiang-Chao],
Ho, Y.S.[Yo-Sung],
List-Wise Rank Learning for Stereoscopic Image Retargeting Quality
Assessment,
MultMed(24), 2022, pp. 1595-1608.
IEEE DOI
2204
Stereo image processing, Measurement, Visualization, Distortion,
Shape, Quality assessment, stereoscopic image retargeting, list-wise ranking
BibRef
Chai, X.L.[Xiong-Li],
Shao, F.[Feng],
Jiang, Q.P.[Qiu-Ping],
Meng, X.C.[Xiang-Chao],
Ho, Y.S.[Yo-Sung],
Monocular and Binocular Interactions Oriented Deformable
Convolutional Networks for Blind Quality Assessment of Stereoscopic
Omnidirectional Images,
CirSysVideo(32), No. 6, June 2022, pp. 3407-3421.
IEEE DOI
2206
Feature extraction, Visualization, Stereo image processing,
Distortion, Quality assessment, Image coding, Virtual reality,
deformable convolutional networks
See also Using Binocular Feature Combination for Blind Quality Assessment of Stereoscopic Images.
BibRef
Wang, X.J.[Xue-Jin],
Qi, M.L.[Mei-Ling],
Shao, F.[Feng],
Jiang, Q.P.[Qiu-Ping],
Meng, X.C.[Xiang-Chao],
Blind quality assessment for multiply distorted stereoscopic images
towards IoT-based 3D capture systems,
JVCIR(71), 2020, pp. 102868.
Elsevier DOI
2009
Internet of things, Image quality assessment,
Multiply-distorted stereoscopic images, High order statistics
BibRef
Amirpour, H.[Hadi],
Pinheiro, A.M.G.[Antonio M. G.],
Fonseca, E.[Elsa],
Ghanbari, M.[Mohammad],
Pereira, M.[Manuela],
Quality Evaluation Of Digital Holographic Data Encoded On The Object
Plane Using State Of The Art Codecs,
Quality Evaluation of Holographic Images Coded With Standard Codecs,
MultMed(24), 2022, pp. 3256-3264.
IEEE DOI
2207
BibRef
Earlier:
ICIP20(3453-3457)
IEEE DOI
2011
Holography, Quality assessment, Codecs, Transform coding,
Optical distortion, Image reconstruction, Digital holography,
codecs.
Databases, Bit rate, Image coding, digital holography, perceived quality, MOS
BibRef
Perry, S.,
Cong, H.P.,
da Silva Cruz, L.A.,
Prazeres, J.,
Pereira, M.[Manuela],
Pinheiro, A.M.G.[Antonio M. G.],
Dumic, E.,
Alexiou, E.,
Ebrahimi, T.,
Quality Evaluation Of Static Point Clouds Encoded Using MPEG Codecs,
ICIP20(3428-3432)
IEEE DOI
2011
Encoding, Transform coding,
Laboratories, Codecs, Measurement, Color, Point Cloud, Coding
BibRef
Jiang, Q.P.[Qiu-Ping],
Shao, F.[Feng],
Lin, W.S.[Wei-Si],
Jiang, G.Y.[Gang-Yi],
Learning Sparse Representation for Objective Image Retargeting
Quality Assessment,
Cyber(48), No. 4, April 2018, pp. 1276-1289.
IEEE DOI
1804
BibRef
Earlier: A1, A2, A4, Only:
MSFE: Blind Image Quality Assessment Based on Multi-Stage Feature
Encoding,
ICIP17(3160-3164)
IEEE DOI
1803
Dictionaries, Distortion, Distortion measurement,
Feature extraction, Quality assessment, Visualization,
sparse representation.
Databases, Distortion, Encoding, Feature extraction, Image coding,
Training, Blind image quality assessment (BIQA),
support vector regression (SVR)
See also Binocular Perception Based Reduced-Reference Stereo Video Quality Assessment Method.
BibRef
Liu, Y.[Yun],
Huang, B.Q.[Bao-Qing],
Yue, G.H.[Guang-Hui],
Wu, J.K.[Jing-Kai],
Wang, X.X.[Xiao-Xu],
Zheng, Z.[Zhi],
Two-stream interactive network based on local and global information
for No-Reference Stereoscopic Image Quality Assessment,
JVCIR(87), 2022, pp. 103586.
Elsevier DOI
2208
Stereoscopic image quality evaluation, Binocular fusion,
Asymmetric convolution kernel, CNN, Summation and difference channels
BibRef
Biswas, S.[Sria],
Appina, B.[Balasubramanyam],
Kara, P.A.[Peter A.],
Simon, A.[Aniko],
Jomodevi: A joint motion and depth visibility prediction algorithm
for perceived stereoscopic 3D quality,
SP:IC(108), 2022, pp. 116820.
Elsevier DOI
2209
Unsupervised objective metric, Motion-based metric,
Depth-based metric, Correlation map, Natural scene statistics,
Stereoscopic video
BibRef
Chang, Y.L.[Yong-Li],
Li, S.[Sumei],
Jin, J.[Jie],
Liu, A.[Anqi],
Xiang, W.[Wei],
Stereo image quality assessment considering the difference of
statistical feature in early visual pathway,
JVCIR(89), 2022, pp. 103643.
Elsevier DOI
2212
Stereo image quality assessment, Retinal ganglion cells,
ON and OFF receptive fields, Monocular and binocular features
BibRef
Chang, Y.L.[Yong-Li],
Li, S.[Sumei],
Ma, L.,
Jin, J.[Jie],
Stereo Image Quality Assessment Considering the Asymmetry of
Statistical Information in Early Visual Pathway,
VCIP20(342-345)
IEEE DOI
2102
Visualization, Feature extraction, Distortion, Information filters,
Image quality, Retina, Brain modeling, ON and OFF receptive fields
BibRef
Su, H.L.[Hong-Lei],
Liu, Q.[Qi],
Liu, Y.X.[Yu-Xin],
Yuan, H.[Hui],
Yang, H.[Huan],
Pan, Z.K.[Zhen-Kuan],
Wang, Z.[Zhou],
Bitstream-Based Perceptual Quality Assessment of Compressed 3D Point
Clouds,
IP(32), 2023, pp. 1815-1828.
IEEE DOI
2303
Point cloud compression, Measurement, Distortion,
Feature extraction, Image color analysis, Image coding,
G-PCC
BibRef
Su, H.L.[Hong-Lei],
Duanmu, Z.,
Liu, W.,
Liu, Q.[Qi],
Wang, Z.[Zhou],
Perceptual Quality Assessment of 3d Point Clouds,
ICIP19(3182-3186)
IEEE DOI
1910
point cloud, image quality assessment, subjective quality,
point cloud compression, downsampling
BibRef
Chang, Y.L.[Yong-Li],
Li, S.[Sumei],
Liu, A.[Anqi],
Jin, J.[Jie],
Xiang, W.[Wei],
Coarse-to-Fine Feedback Guidance Based Stereo Image Quality
Assessment Considering Dominant Eye Fusion,
MultMed(25), 2023, pp. 8855-8867.
IEEE DOI
2312
BibRef
Li, B.H.[Bing-Heng],
Huo, F.[Fushuo],
REQA: Coarse-to-fine assessment of image quality to alleviate the
range effect,
JVCIR(98), 2024, pp. 104043.
Elsevier DOI Code:
WWW Link.
2402
Blind image quality assessment, Range effect,
Coarse-to-fine assessment, Feedback hierarchy
BibRef
Zhang, Y.[Yi],
Chandler, D.M.[Damon M.],
Mou, X.[Xuanqin],
Deep neural network based distortion parameter estimation for blind
quality measurement of stereoscopic images,
SP:IC(126), 2024, pp. 117138.
Elsevier DOI
2406
Quality measurement, Stereoscopic image,
Distortion parameter estimation, Deep neural network
BibRef
Lorenz, T.[Tobias],
Ruoss, A.[Anian],
Balunovic, M.[Mislav],
Singh, G.[Gagandeep],
Vechev, M.[Martin],
Robustness Certification for Point Cloud Models,
ICCV21(7588-7598)
IEEE DOI
2203
Point cloud compression, Solid modeling, Computational modeling,
Semantics, Robustness, Adversarial learning,
BibRef
Fan, Y.,
Larabi, M.,
Cheikh, F.A.[Faouzi Alaya],
Blind Stereopair Quality Assessment Using Statistics of Monocular and
Binocular Image Structures,
ICIP19(430-434)
IEEE DOI
1910
No-reference, stereoscopic/3D images, local contrast,
Laplacian of Gaussian, local entropy, binocular rivalry
BibRef
Hachicha, W.[Walid],
Beghdadi, A.[Azeddine],
Cheikh, F.A.[Faouzi Alaya],
Stereo image quality assessment using a binocular just noticeable
difference model,
ICIP13(113-117)
IEEE DOI
1402
Computational modeling
BibRef
Yilmaz, G.N.,
Battisti, F.,
Depth Perception Prediction of 3D Video for Ensuring Advanced
Multimedia Services,
3DTV-CON18(1-3)
IEEE DOI
1812
multimedia communication, multimedia computing, television,
video signal processing, visual perception,
quality metric
BibRef
Croci, S.,
Knorr, S.,
Smolic, A.,
Sharpness Mismatch Detection in Stereoscopic Content with 360-Degree
Capability,
ICIP18(1423-1427)
IEEE DOI
1809
Visualization,
Stereo image processing, Image edge detection, Histograms,
virtual reality
BibRef
Fan, Y.,
Larabi, M.,
Cheikh, F.A.,
Fernandez-Maloigne, C.,
No-Reference Quality Assessment of Stereoscopic Images Based on
Binocular Combination of Local Features Statistics,
ICIP18(3538-3542)
IEEE DOI
1809
Databases,
Stereo image processing, Image quality, Feature extraction,
support vector regression
BibRef
Maiwald, F.,
Vietze, T.,
Schneider, D.,
Henze, F.,
Münster, S.,
Niebling, F.,
Photogrammetric Analysis of Historical Image Repositories for Virtual
Reconstruction in the Field of Digital Humanities,
3DARCH17(447-452).
DOI Link
1805
BibRef
Yao, Y.,
Shen, L.,
An, P.,
Bivariate statistics and binocular energy induced stereo-pair quality
evaluator,
VCIP17(1-4)
IEEE DOI
1804
feature extraction, image representation, natural scenes,
statistical analysis, stereo image processing, Gabor responses,
stereoscopic image quality assessment
BibRef
Ma, J.[Jian],
An, P.[Ping],
Shen, L.Q.[Li-Quan],
Li, K.[Kai],
Yang, J.[Jialu],
SSIM-based binocular perceptual model for quality assessment of
stereoscopic images,
VCIP17(1-4)
IEEE DOI
1804
filtering theory, stereo image processing, visual perception,
SSIM-based binocular perceptual model, binocular rivalry,
full reference
BibRef
Kara, P.A.,
Cserkaszky, A.,
Barsi, A.,
Papp, T.,
Martini, M.G.,
Bokor, L.,
The interdependence of spatial and angular resolution in the quality
of experience of light field visualization,
IC3D17(1-8)
IEEE DOI
1804
data visualisation, image motion analysis, image resolution,
stereo image processing, visual perception, 3D visual experience,
spatial resolution
BibRef
Malekmohamadi, H.,
Automatic subjective quality estimation of 3D stereoscopic videos:
NR-RR approach,
3DTV-CON17(1-4)
IEEE DOI
1804
decision trees, feature extraction, image restoration,
stereo image processing, video signal processing,
Subjective quality
BibRef
Lin, C.T.,
Liu, T.J.,
Liu, K.H.,
Visual quality prediction on distorted stereoscopic images,
ICIP17(3480-3484)
IEEE DOI
1803
Databases, Feature extraction, Image quality, Predictive models,
Solid modeling, Stereo image processing,
stereoscopic images
BibRef
Fan, Y.,
Larabi, M.C.,
Cheikh, F.A.,
Fernandez-Maloigne, C.,
Full-reference stereoscopic image quality assessment accounting for
binocular combination and disparity information,
ICIP17(760-764)
IEEE DOI
1803
Distortion, Entropy, Image quality, Measurement,
visual saliency
BibRef
Chen, Y.,
Zhai, G.,
Zhou, J.,
Wan, Z.,
Tang, L.,
Global quality of assessment and optimization for the
backward-compatible stereoscopic display system,
ICIP17(191-195)
IEEE DOI
1803
Brightness, Glass, Prototypes, Quality assessment,
Stereo image processing,
quality assessment criteria
BibRef
Zhan, J.,
Niu, Y.,
Huang, Y.,
Learning from multi metrics for stereoscopic 3D image quality
assessment,
IC3D16(1-8)
IEEE DOI
1703
learning (artificial intelligence)
BibRef
Wang, K.,
Zhou, J.,
Liu, N.,
Gu, X.,
Stereoscopic images quality assessment based on deep learning,
VCIP16(1-4)
IEEE DOI
1701
Data preprocessing
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Fan, Y.[Yu],
Larabi, M.C.[Mohamed-Chaker],
Cheikh, F.A.[Faouzi Alaya],
Fernandez-Maloigne, C.[Christine],
On the performance of 3D just noticeable difference models,
ICIP16(1017-1021)
IEEE DOI
1610
Maximum tolerable distortion.
Adaptation models
BibRef
Hong, W.,
Yu, L.,
Subjective assessment methodology for super multiview content,
3DTV-CON16(1-4)
IEEE DOI
1610
stereo image processing
BibRef
Bourbia, S.[Salima],
Karine, A.[Ayoub],
Chetouani, A.[Aladine],
El Hassoun, M.[Mohammed],
A Multi-Task Convolutional Neural Network for Blind Stereoscopic
Image Quality Assessment Using Naturalness Analysis,
ICIP21(1434-1438)
IEEE DOI
2201
Image quality, Deep learning, Wavelet domain, Databases,
Stereo image processing, Image processing,
Binocular features
BibRef
Fezza, S.A.,
Chetouani, A.,
Larabi, M.C.,
Universal blind image quality assessment for stereoscopic images,
3DTV-CON16(1-4)
IEEE DOI
1610
image resolution
BibRef
Ma, J.,
An, P.,
Method to quality assessment of stereo images,
VCIP16(1-4)
IEEE DOI
1701
Databases
BibRef
Ma, J.,
An, P.,
You, Z.,
Shen, L.,
A novel image quality index for stereo image,
3DTV-CON16(1-4)
IEEE DOI
1610
Gabor filters
BibRef
Deng, X.D.[Xiang-Dong],
Zheng, G.W.[Guan-Wen],
Jia, T.[Tao],
Cao, X.,
Limits of brightness and color distortions based on subjective
evaluation of stereoscopic images,
IC3D15(1-6)
IEEE DOI
1603
distortion
BibRef
Authors not listed on paper.
A full-reference stereoscopic image quality metric based on binocular
energy and regression analysis,
3DTV-CON15(1-5)
IEEE DOI
1508
Analytical models
BibRef
Farid, M.S.[Muhammad Shahid],
Lucenteforte, M.[Maurizio],
Grangetto, M.[Marco],
Blind depth quality assessment using histogram shape analysis,
3DTV-CON15(1-5)
IEEE DOI
1508
Histograms
BibRef
Boehs, G.[Gustavo],
Vieira, M.L.H.[Milton L.H.],
Stereoscopic image quality in virtual environments,
IC3D14(1-8)
IEEE DOI
1503
DH-HEMTs
BibRef
Fezza, S.A.[Sid Ahmed],
Larabi, M.C.[Mohamed-Chaker],
Faraoun, K.M.[Kamel Mohamed],
Stereoscopic image quality metric based on local entropy and
binocular just noticeable difference,
ICIP14(2002-2006)
IEEE DOI
1502
Entropy
BibRef
Chetouani, A.[Aladine],
An Image Quality Metric with Reference for Multiply Distorted Image,
ACIVS16(477-485).
Springer DOI
1611
BibRef
Chetouani, A.[Aladine],
Full Reference Image Quality Assessment: Limitation,
ICPR14(833-837)
IEEE DOI
1412
BibRef
And:
Full reference image quality metric for stereo images based on
Cyclopean image computation and neural fusion,
VCIP14(109-112)
IEEE DOI
1504
BibRef
And:
Neural learning-based image quality metric without reference,
IPTA14(1-6)
IEEE DOI
1503
feature extraction
Degradation.
feature extraction
BibRef
Wang, X.[Xu],
Cao, L.,
Ma, L.,
Zhou, Y.,
Kwong, S.[Sam],
Complex singular value decomposition based stereoscopic image quality
assessment,
VCIP16(1-4)
IEEE DOI
1701
Databases
BibRef
Wang, X.[Xu],
Kwong, S.[Sam],
Zhang, Y.[Yun],
Zhang, Y.[Yun],
Considering binocular spatial sensitivity in stereoscopic image quality
assessment,
VCIP11(1-4).
IEEE DOI
1201
BibRef
Chetouani, A.[Aladine],
Beghdadi, A.[Azeddine],
Deriche, M.[Mohamed],
A universal Full Reference image Quality Metric based on a neural
fusion approach,
ICIP10(2517-2520).
IEEE DOI
1009
BibRef
And:
Statistical Modeling of Image Degradation Based on Quality Metrics,
ICPR10(714-717).
IEEE DOI
1008
BibRef
Yang, J.C.[Jia-Chen],
Hou, C.P.[Chun-Ping],
Zhou, Y.[Yuan],
Zhang, Z.Y.[Zhuo-Yun],
Guo, J.C.[Ji-Chang],
Objective quality assessment method of stereo images,
3DTV09(1-4).
IEEE DOI
0905
BibRef
Shen, L.[Lili],
Yang, J.C.[Jia-Chen],
Zhang, Z.Y.[Zhuo-Yun],
Quality Assessment of Stereo Images with Stereo Vision,
CISP09(1-4).
IEEE DOI
0910
BibRef
And:
Stereo Picture Quality Estimation Based on a Multiple Channel HVS Model,
CISP09(1-4).
IEEE DOI
0910
BibRef
Chapter on Image Processing, Restoration, Enhancement, Filters, Image and Video Coding continues in
Image Quality Evaluation, Perceptual Quality, Subjective Quality .