5.3.10.5 Image Quality Evaluation, Stereoscopic Imagery, Stereo, Depth

Chapter Contents (Back)
Image Quality. Stereo. Some related papers here:
See also Stereoscopic Viewing, Three Dimensional Visualization, Displays.

Benoit, A.[Alexandre], Le Callet, P.[Patrick], Campisi, P.[Patrizio], Cousseau, R.[Romain],
Quality Assessment of Stereoscopic Images,
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 rivalry,
SP:IC(28), No. 9, 2013, pp. 1143-1155.
Elsevier DOI 1310
Binocular rivalry BibRef

Chen, M.J.[Ming-Jun], Cormack, L.K.[Lawrence K.], Bovik, A.C.[Alan C.],
No-Reference Quality Assessment of Natural Stereopairs,
IP(22), No. 9, 2013, pp. 3379-3391.
IEEE DOI 1308
feature extraction BibRef

Shao, F.[Feng], Li, K.[Kemeng], Lin, W.S.[Wei-Si], Jiang, G.Y.[Gang-Yi], Yu, M.[Mei],
Using Binocular Feature Combination for Blind Quality Assessment of Stereoscopic Images,
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],
A perceptual stereoscopic image quality assessment model accounting for binocular combination behavior,
JVCIR(31), No. 1, 2015, pp. 138-145.
Elsevier DOI 1508
Binocular vision BibRef

Yang, J.C.[Jia-Chen], Xu, H.F.[Hui-Fang], Zhao, Y.[Yang], Liu, H.[Hehan], Lu, W.[Wen],
Stereoscopic image quality assessment combining statistical features and binocular theory,
PRL(127), 2019, pp. 48-55.
Elsevier DOI 1911
Blind stereoscopic image quality assessment, Ocular dominance, Adding channel, Subtracting channel BibRef

Ding, Y.[Yong], Zhao, Y.[Yang], Chen, X.D.[Xiao-Dong], Zhu, X.L.[Xiao-Lei], Andrey, K.[Krylov],
Stereoscopic image quality assessment by analysing visual hierarchical structures and binocular effects,
IET-IPR(13), No. 10, 22 August 2019, pp. 1608-1615.
DOI Link 1909
BibRef

Ryu, S.C.[Seung-Chul], Sohn, K.H.[Kwang-Hoon],
No-Reference Quality Assessment for Stereoscopic Images Based on Binocular Quality Perception,
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], Sohn, K.H.[Kwang-Hoon],
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.[Yaqi], 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

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.[Yuzhen], Zhan, J.[Jiamei],
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.[Chaofeng], Zheng, Y.[Yuhui], 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.[Xinbo],
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

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

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

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

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], 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

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

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


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

Chang, Y., Li, S., Ma, L., Jin, J.,
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

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

Su, H., Duanmu, Z., Liu, W., Liu, Q., Wang, Z.,
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

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 BibRef

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, Human Visual System Based, HVS .


Last update:Aug 14, 2022 at 21:20:19