Full-Reference Quality Assessment of Stereoscopic Images by Learning Binocular Receptive Field Properties
Abstract
Quality assessment of 3D images encounters more challenges than its 2D counterparts. Directly applying 2D image quality metrics is not the solution. In this paper, we propose a new full-reference quality assessment for stereoscopic images by learning binocular receptive field properties to be more in line with human visual perception. To be more specific, in the training phase, we learn a multiscale dictionary from the training database, so that the latent structure of images can be represented as a set of basis vectors. In the quality estimation phase, we compute sparse feature similarity index based on the estimated sparse coefficient vectors by considering their phase difference and amplitude difference, and compute global luminance similarity index by considering luminance changes. The final quality score is obtained by incorporating binocular combination based on sparse energy and sparse complexity. Experimental results on five public 3D image quality assessment databases demonstrate that in comparison with the most related existing methods, the devised algorithm achieves high consistency with subjective assessment.
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
Shao et al. proposed a new full-reference quality assessment model for stereoscopic images where binocular receptive field properties are learned and aligned with human visual perception.
“A new full-reference quality assessment model for stereoscopic images is proposed by Shao et al. [339], where binocular receptive field properties are learned and aligned with human visual perception.”
From QoE Management of Multimedia Streaming Services in Future Networks: A Tutorial and Survey (2020) · cites this paper as [339]
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Cites
Image quality assessment: from error visibility to structural similarity
Wang, Z., Bovik, A. C., Sheikh, H. R., & Simoncelli, E. P. (2004) · IEEE Transactions on Image Processing · doi:10.1109/tip.2003.819861 · Cited by 57,892 · see citing works
$rm K$-SVD: An Algorithm for Designing Overcomplete Dictionaries for Sparse Representation
Aharon, M., Elad, M., & Bruckstein, A. M. (2006) · IEEE Transactions on Signal Processing · doi:10.1109/tsp.2006.881199 · Cited by 9,464 · see citing works
Multiscale structural similarity for image quality assessment
Wang, Z., Simoncelli, E. P., & Bovik, A. C. (2004) · The Thrity-Seventh Asilomar Conference on Signals, Systems & Computers, 2003 · doi:10.1109/acssc.2003.1292216 · Cited by 6,253 · see citing works
No-Reference Image Quality Assessment in the Spatial Domain
Mittal, A., Moorthy, A. K., & Bovik, A. C. (2012) · IEEE Transactions on Image Processing · doi:10.1109/tip.2012.2214050 · Cited by 6,064 · see citing works
A universal image quality index
Wang, Z., & Bovik, A. C. (2002) · IEEE Signal Processing Letters · doi:10.1109/97.995823 · Cited by 5,757 · see citing works
Emergence of simple-cell receptive field properties by learning a sparse code for natural images
Olshausen, B. A., & Field, D. J. (1996) · Nature · doi:10.1038/381607a0 · Cited by 5,694 · see citing works
Image information and visual quality
Sheikh, H. R., & Bovik, A. C. (2006) · IEEE Transactions on Image Processing · doi:10.1109/tip.2005.859378 · Cited by 4,104 · see citing works
Blind Image Quality Assessment: From Natural Scene Statistics to Perceptual Quality
Moorthy, A. K., & Bovik, A. C. (2011) · IEEE Transactions on Image Processing · doi:10.1109/tip.2011.2147325 · Cited by 1,763 · see citing works
Blind Image Quality Assessment: A Natural Scene Statistics Approach in the DCT Domain
Saad, M. A., Bovik, A. C., & Charrier, C. (2012) · IEEE Transactions on Image Processing · doi:10.1109/tip.2012.2191563 · Cited by 1,750 · see citing works
Jones, J. P., & Palmer, L. A. (1987) · Journal of Neurophysiology · doi:10.1152/jn.1987.58.6.1233 · Cited by 1,658 · see citing works
Computing visual correspondence with occlusions using graph cuts
Kolmogorov, V., & Ramin Zabih, A. (2002) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2001.937668 · Cited by 1,128 · see citing works
Foundations of Cyclopean Perception
Wilson, H. R., & Julesz, B. (1972) · Chicago Review · doi:10.2307/25294744 · Cited by 976 · see citing works
Perceptual visual quality metrics: A survey
Lin, W., & Kuo, C. ‐. J. (2011) · Journal of Visual Communication and Image Representation · doi:10.1016/j.jvcir.2011.01.005 · Cited by 923 · see citing works
Self-citationFull-reference quality assessment of stereopairs accounting for rivalry
Chen, M.-J., Su, C.-C., Kwon, D.-K., Cormack, L. K., & Bovik, A. C. (2013) · Signal Processing Image Communication · doi:10.1016/j.image.2013.05.006 · Cited by 383 · see citing works
Quality Assessment of Stereoscopic Images
Benoît, A., Callet, P. L., Campisi, P., & Cousseau, R. (2008) · EURASIP Journal on Image and Video Processing · doi:10.1155/2008/659024 · Cited by 376 · see citing works
Neural encoding of binocular disparity: Energy models, position shifts and phase shifts
Fleet, D. J., Wagner, H., & Heeger, D. J. (1996) · Vision Research · doi:10.1016/0042-6989(95)00313-4 · Cited by 343 · see citing works
A gain-control theory of binocular combination
Ding, J., & Sperling, G. (2006) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.0509629103 · Cited by 318 · see citing works
Subjective evaluation of stereoscopic image quality
Moorthy, A. K., Su, C.-C., Mittal, A., & Bovik, A. C. (2012) · Signal Processing Image Communication · doi:10.1016/j.image.2012.08.004 · Cited by 304 · see citing works
Stereoscopic 3D-TV: Visual Comfort
Tam, W. J., Speranza, F., Yano, S., Shimono, K., & Ono, H. (2011) · IEEE Transactions on Broadcasting · doi:10.1109/tbc.2011.2125070 · Cited by 292 · see citing works
Binocular contrast vision at and above threshold
Meese, T. S., Georgeson, M. A., & Baker, D. H. (2006) · Journal of Vision · doi:10.1167/6.11.7 · Cited by 272 · see citing works
No-Reference Quality Assessment of Natural Stereopairs
Chen, M.-J., Cormack, L. K., & Bovik, A. C. (2013) · IEEE Transactions on Image Processing · doi:10.1109/tip.2013.2267393 · Cited by 268 · see citing works
Blake, R., & Wilson, H. R. (2010) · Vision Research · doi:10.1016/j.visres.2010.10.009 · Cited by 263 · see citing works
Hyvärinen, A., & Hoyer, P. O. (2001) · Vision Research · doi:10.1016/s0042-6989(01)00114-6 · Cited by 260 · see citing works
Perceptual Quality Metric With Internal Generative Mechanism
Wu, J., Lin, W., Shi, G., & Liu, A. (2012) · IEEE Transactions on Image Processing · doi:10.1109/tip.2012.2214048 · Cited by 260 · see citing works
Self-citationTowards a New Quality Metric for 3-D Synthesized View Assessment
Bosc, E., Pépion, R., Callet, P. L., Köppel, M., Ndjiki-Nya, P., Pressigout, M., & Morin, L. (2011) · IEEE Journal of Selected Topics in Signal Processing · doi:10.1109/jstsp.2011.2166245 · Cited by 221 · see citing works
Quality Assessment of Stereoscopic 3D Image Compression by Binocular Integration Behaviors
Lin, Y., & Wu, J. (2014) · IEEE Transactions on Image Processing · doi:10.1109/tip.2014.2302686 · Cited by 200 · see citing works
Shao, F., Lin, W., Gu, S., Jiang, G., & Srikanthan, T. (2013) · IEEE Transactions on Image Processing · doi:10.1109/tip.2013.2240003 · Cited by 191 · see citing works
Self-citationA Convolutional Neural Network Approach for Objective Video Quality Assessment
Callet, P. L., Viard-Gaudin, C., & Dominique Barba, B. (2006) · IEEE Transactions on Neural Networks · doi:10.1109/tnn.2006.879766 · Cited by 188 · see citing works
Quality analysis for 3D video using 2D video quality models
Yasakethu, S. L. P., Hewage, C. T. E. R., Fernando, W. A. C., & KONDOZ, A. M. (2008) · IEEE Transactions on Consumer Electronics · doi:10.1109/tce.2008.4711260 · Cited by 183 · see citing works
Stereoscopic image quality metrics and compression
Gorley, P. W., & Holliman, N. S. (2008) · Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE · doi:10.1117/12.763530 · Cited by 159 · see citing works
Sparse Feature Fidelity for Perceptual Image Quality Assessment
Chang, H.-W., Yang, H., Gan, Y., & Wang, M. (2013) · IEEE Transactions on Image Processing · doi:10.1109/tip.2013.2266579 · Cited by 157 · see citing works
Quality Evaluation of Color Plus Depth Map-Based Stereoscopic Video
Hewage, C. T. E. R., Worrall, S., Dogan, S., Villette, S., & KONDOZ, A. M. (2009) · IEEE Journal of Selected Topics in Signal Processing · doi:10.1109/jstsp.2009.2014805 · Cited by 153 · see citing works
Objective Image Quality Assessment Based on Support Vector Regression
Narwaria, M., & Lin, W. (2010) · IEEE Transactions on Neural Networks · doi:10.1109/tnn.2010.2040192 · Cited by 147 · see citing works
Self-citationA perceptual metric for stereoscopic image quality assessment based on the binocular energy
Bensalma, R., & Larabi, M. (2012) · Multidimensional Systems and Signal Processing · doi:10.1007/s11045-012-0178-3 · Cited by 141 · see citing works
Gao, X., Gao, F., Tao, D., & Li, X. (2013) · IEEE Transactions on Neural Networks and Learning Systems · doi:10.1109/tnnls.2013.2271356 · Cited by 137 · see citing works
No-Reference Quality Assessment for Stereoscopic Images Based on Binocular Quality Perception
Ryu, S., & Sohn, K. (2013) · IEEE Transactions on Circuits and Systems for Video Technology · doi:10.1109/tcsvt.2013.2279971 · Cited by 112 · see citing works
Mechanisms of stereoscopic vision: the disparity energy model
Ohzawa, I. (1998) · Current Opinion in Neurobiology · doi:10.1016/s0959-4388(98)80039-1 · Cited by 105 · see citing works
Lutz Goldmann, A., Francesca, D. S., & Ebrahimi, T. (2010) · Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE · doi:10.1117/12.839438 · Cited by 103 · see citing works
Binocular Just-Noticeable-Difference Model for Stereoscopic Images
Zhao, Y., Chen, Z., Zhu, C., Tan, Y., & Yu, L. (2010) · IEEE Signal Processing Letters · doi:10.1109/lsp.2010.2090041 · Cited by 101 · see citing works
CYCLOP: A stereo color image quality assessment metric
Maalouf, A., & Larabi, M. (2011) · IEEE International Conference on Acoustics Speech and Signal Processing · doi:10.1109/icassp.2011.5946615 · Cited by 97 · see citing works
Towards compound stereo-video quality metric: a specific encoder-based framework
Boev, A., Gotchev, A. P., Egiazarian, K., Aksay, A., & Akar, G. B. (2006) · IEEE Southwest Symposium on Image Analysis and Interpretation (SSIAI) · doi:10.1109/ssiai.2006.1633754 · Cited by 88 · see citing works
Foundations of Binocular Vision: A Clinical Perspective.
Kent, D. M. (2001) · Optometry and Vision Science · doi:10.1097/00006324-200104000-00004 · Cited by 88 · see citing works
Neural dynamics of binocular brightness perception
Grossberg, S., & Kelly, F. (1999) · Vision Research · doi:10.1016/s0042-6989(99)00095-4 · Cited by 84 · see citing works
Silva, D., Arachchi, H. K., Ekmekcioǧlu, E., & KONDOZ, A. M. (2013) · IEEE Transactions on Image Processing · doi:10.1109/tip.2013.2268422 · Cited by 80 · see citing works
Evaluating Depth Perception of 3D Stereoscopic Videos
Lebreton, P., Raake, A., Barkowsky, M., & Callet, P. L. (2012) · IEEE Journal of Selected Topics in Signal Processing · doi:10.1109/jstsp.2012.2213236 · Cited by 73 · see citing works
3D-DCT based perceptual quality assessment of stereo video
Jin, L., Boev, A., Gotchev, A. P., & Egiazarian, K. (2011) · Proceedings - International Conference on Image Processing · doi:10.1109/icip.2011.6116175 · Cited by 72 · see citing works
Stereoscopic image quality prediction
Sazzad, Z. M. P., Yamanaka, S., Kawayokeita, Y., & Horita, Y. (2009) · International Conference on Quality of Multimedia Experience (QoMEX) · doi:10.1109/qomex.2009.5246956 · Cited by 69 · see citing works
3D Perception Based Quality Pooling: Stereopsis, Binocular Rivalry, and Binocular Suppression
Lee, K., & Lee, S. (2015) · IEEE Journal of Selected Topics in Signal Processing · doi:10.1109/jstsp.2015.2393296 · Cited by 63 · see citing works
Binocular Receptive Field Models, Disparity Tuning, and Characteristic Disparity
Zhu, Y.-D., & Ning Qian, A. (1996) · Neural Computation · doi:10.1162/neco.1996.8.8.1611 · Cited by 62 · see citing works
Considering binocular spatial sensitivity in stereoscopic image quality assessment
Wang, X., Kwong, S., Zhang, Y., & Zhang, Y. (2011) · Visual Communications and Image Processing · doi:10.1109/vcip.2011.6116015 · Cited by 57 · see citing works
Foundations of Sensation and Perception
Mather, G. (2022) · Psychology Press eBooks · doi:10.4324/9781003335481 · Cited by 54 · see citing works
Subjective quality analyses of stereoscopic images in 3DTV system
Zhou, J., Jiang, G., Mao, X., Yu, M., Shao, F., Peng, Z., & Zhang, Y. (2011) · Visual Communications and Image Processing · doi:10.1109/vcip.2011.6115913 · Cited by 53 · see citing works
Self-citationBinocular energy responses to natural images
Hibbard, P. B. (2008) · Vision Research · doi:10.1016/j.visres.2008.03.013 · Cited by 49 · see citing works
Nonlinearities in the binocular combination of luminance and contrast
Baker, D. H., Wallis, S. A., Georgeson, M. A., & Meese, T. S. (2012) · Vision Research · doi:10.1016/j.visres.2012.01.008 · Cited by 49 · see citing works
Effect of Binocular Cortical Misalignment on Ocular Dominance and Orientation Selectivity
Shouval, H. Z., Intrator, N., Law, C. C., & Cooper, L. N. (1996) · Neural Computation · doi:10.1162/neco.1996.8.5.1021 · Cited by 36 · see citing works
Cyclopean geometry of binocular vision
Hansard, M. E., & Horaud, R. (2008) · Journal of the Optical Society of America A · doi:10.1364/josaa.25.002357 · Cited by 33 · see citing works
Sparse representation-based image quality assessment
Guha, T., Nezhadarya, E., & Ward, R. K. (2014) · Signal Processing Image Communication · doi:10.1016/j.image.2014.09.010 · Cited by 30 · see citing works
The changing disparity energy model
Peng, Q., & Shi, B. E. (2009) · Vision Research · doi:10.1016/j.visres.2009.11.012 · Cited by 25 · see citing works
Stidwill, D., & Fletcher, R. H. (2010) · Wiley eBooks · doi:10.1002/9781118788684 · Cited by 22 · see citing works
An efficient human visual system based quality metric for 3D video
Banitalebi-Dehkordi, A., Pourazad, M. T., & Nasiopoulos, P. (2015) · Multimedia Tools and Applications · doi:10.1007/s11042-015-2466-z · Cited by 21 · see citing works
Jin, L., Boev, A., Egiazarian, K., & Gotchev, A. P. (2014) · EURASIP Journal on Image and Video Processing · doi:10.1186/1687-5281-2014-6 · Cited by 20 · see citing works
Exploring V1 by modeling the perceptual quality of images
Zhang, F., Jiang, W., Autrusseau, F., & Lin, W. (2014) · Journal of Vision · doi:10.1167/14.1.26 · Cited by 19 · see citing works
Self-citationAnaglyph image generation by matching color appearance attributes
Li, S., Ma, L., & Ngan, K. N. (2013) · Signal Processing Image Communication · doi:10.1016/j.image.2013.03.004 · Cited by 18 · see citing works
Stereo Image Quality Assessment Using Visual Attention and Distortion Predictors
Hwang, J. (2011) · KSII Transactions on Internet and Information Systems · doi:10.3837/tiis.2011.09.007 · Cited by 16 · see citing works
MIQM: A Multicamera Image Quality Measure
Solh, M., & AlRegib, G. (2012) · IEEE Transactions on Image Processing · doi:10.1109/tip.2012.2200490 · Cited by 13 · see citing works
Coria, L. E., Xu, D., & Nasiopoulos, P. (2011) · International Conference on Consumer Electronics · doi:10.1109/icce.2011.5722846 · Cited by 13 · see citing works
3D image quality index using SDP-based binocular perception model
Ko, H., Kim, C., Choi, S. Y., & Kuo, C. ‐. J. (2013) · Multidimensional Signal Processing Workshop · doi:10.1109/ivmspw.2013.6611936 · Cited by 10 · see citing works
Stereoscopic quality datasets under various test conditions
Xing, L., You, J., Ebrahimi, T., & Perkis, A. (2013) · International Conference on Quality of Multimedia Experience (QoMEX) · doi:10.1109/qomex.2013.6603226 · Cited by 8 · see citing works
Binocular Energy Estimation Based on Properties of the Human Visual System
Bensalma, R., & Larabi, M. (2012) · Cognitive Computation · doi:10.1007/s12559-012-9187-7 · Cited by 7 · see citing works
Flagged first, then by citations
Cited by
Sorted by citations
Perceptual image quality assessment: a survey
Zhai, G., & Min, X. (2020) · Science China Information Sciences · doi:10.1007/s11432-019-2757-1 · Cited by 662 · see citing works
QoE Management of Multimedia Streaming Services in Future Networks: A Tutorial and Survey
Barakabitze, A. A., Barman, N., Ahmad, A., Zadtootaghaj, S., Sun, L., Martini, M. G., & Atzori, L. (2020) · IEEE Communications Surveys & Tutorials · doi:10.1109/comst.2019.2958784 · Cited by 239 · see citing works
Optimizing Multistage Discriminative Dictionaries for Blind Image Quality Assessment
Jiang, Q., Shao, F., Lin, W., Gu, K., Jiang, G., & Sun, H. (2017) · IEEE Transactions on Multimedia · doi:10.1109/tmm.2017.2763321 · Cited by 204 · see citing works
Self-citationUnified No-Reference Quality Assessment of Singly and Multiply Distorted Stereoscopic Images
Jiang, Q., Shao, F., Gao, W., Chen, Z., Jiang, G., & Ho, Y. (2018) · IEEE Transactions on Image Processing · doi:10.1109/tip.2018.2881828 · Cited by 148 · see citing works
Self-citationDual-Stream Interactive Networks for No-Reference Stereoscopic Image Quality Assessment
Zhou, W., Chen, Z., & Li, W. (2019) · IEEE Transactions on Image Processing · doi:10.1109/tip.2019.2902831 · Cited by 115 · see citing works
Machine learning based video coding optimizations: A survey
Zhang, Y., Kwong, S., & Wang, S. (2019) · Information Sciences · doi:10.1016/j.ins.2019.07.096 · Cited by 80 · see citing works
Shao, F., Tian, W., Lin, W., Jiang, G., & Dai, Q. (2016) · IEEE Transactions on Image Processing · doi:10.1109/tip.2016.2538462 · Cited by 76 · see citing works
Self-citationJiang, Q., Shao, F., Lin, W., & Jiang, G. (2017) · IEEE Transactions on Circuits and Systems for Video Technology · doi:10.1109/tcsvt.2017.2783938 · Cited by 73 · see citing works
Self-citation2D and 3D Image Quality Assessment: A Survey of Metrics and Challenges
Niu, Y., Zhong, Y., Guo, W., Shi, Y., & Chen, P. (2018) · IEEE Access · doi:10.1109/access.2018.2885818 · Cited by 56 · see citing works
Visual Attention Modeling for Stereoscopic Video: A Benchmark and Computational Model
Fang, Y., Zhang, C., Li, J., Lei, J., Silva, M. P. D., & Callet, P. L. (2017) · IEEE Transactions on Image Processing · doi:10.1109/tip.2017.2721112 · Cited by 56 · see citing works
Light Field Image Quality Assessment via the Light Field Coherence
Tian, Y., Zeng, H., Hou, J., Chen, J., & Ma, K. (2020) · IEEE Transactions on Image Processing · doi:10.1109/tip.2020.3008856 · Cited by 52 · see citing works
Wan, Z., Gu, K., & Zhao, D. (2019) · IEEE Transactions on Multimedia · doi:10.1109/tmm.2019.2950533 · Cited by 48 · see citing works
Learning Blind Quality Evaluator for Stereoscopic Images Using Joint Sparse Representation
Shao, F., Li, K., Lin, W., Jiang, G., & Dai, Q. (2016) · IEEE Transactions on Multimedia · doi:10.1109/tmm.2016.2594142 · Cited by 44 · see citing works
Self-citationLv, Y., Yu, M., Jiang, G., Shao, F., Peng, Z., & Chen, F. (2016) · Signal Processing Image Communication · doi:10.1016/j.image.2016.07.003 · Cited by 43 · see citing works
Self-citationQuality assessment metric of stereo images considering cyclopean integration and visual saliency
Yang, J., Wang, Y., Li, B., Lu, W. B., Meng, Q., Lv, Z., Zhao, D., & Gao, Z. (2016) · Information Sciences · doi:10.1016/j.ins.2016.09.004 · Cited by 41 · see citing works
Quaternion representation based visual saliency for stereoscopic image quality assessment
Wang, X., Ma, L., Kwong, S., & Zhou, Y. (2017) · Signal Processing · doi:10.1016/j.sigpro.2017.12.002 · Cited by 41 · see citing works
Estimating Depth-Salient Edges and Its Application to Stereoscopic Image Quality Assessment
Khan, S., & Channappayya, S. S. (2018) · IEEE Transactions on Image Processing · doi:10.1109/tip.2018.2860279 · Cited by 41 · see citing works
Quality Index for Stereoscopic Images by Jointly Evaluating Cyclopean Amplitude and Cyclopean Phase
Lin, Y., Yang, J., Lu, W. B., Meng, Q., Lv, Z., & Song, H. H. (2016) · IEEE Journal of Selected Topics in Signal Processing · doi:10.1109/jstsp.2016.2632422 · Cited by 40 · see citing works
Yue, G., Hou, C., Jiang, Q., & Yang, Y. (2018) · Signal Processing · doi:10.1016/j.sigpro.2018.04.019 · Cited by 39 · see citing works
Shi, Y., Guo, W.-Z., Niu, Y., & Zhan, J. (2019) · Pattern Recognition · doi:10.1016/j.patcog.2019.107168 · Cited by 37 · see citing works
Perceptual Depth Quality in Distorted Stereoscopic Images
Wang, J., Wang, S., Ma, K., & Wang, Z. (2016) · IEEE Transactions on Image Processing · doi:10.1109/tip.2016.2642791 · Cited by 35 · see citing works
Jiang, Q., Shao, F., Lin, W., & Jiang, G. (2017) · Pattern Recognition · doi:10.1016/j.patcog.2017.11.001 · Cited by 32 · see citing works
Self-citationLearning based no-reference metric for assessing quality of experience of stereoscopic images
Liu, T.-J., Liu, K.-H., & Shen, K.-H. (2019) · Journal of Visual Communication and Image Representation · doi:10.1016/j.jvcir.2019.04.004 · Cited by 32 · see citing works
Wang, X., Jiang, Q., Shao, F., Gu, K., Zhai, G., & Yang, X. (2020) · IEEE Transactions on Multimedia · doi:10.1109/tmm.2020.2986583 · Cited by 32 · see citing works
Self-citationGeng, X., Shen, L., Li, K., & An, P. (2016) · Signal Processing Image Communication · doi:10.1016/j.image.2016.12.004 · Cited by 32 · see citing works
Self-citationQuality Evaluation of Arbitrary Style Transfer: Subjective Study and Objective Metric
Chen, H., Shao, F., Chai, X., Gu, Y., Jiang, Q., Meng, X., & Ho, Y. (2022) · IEEE Transactions on Circuits and Systems for Video Technology · doi:10.1109/tcsvt.2022.3231041 · Cited by 32 · see citing works
Self-citationYang, J., Zhao, Y. S., Zhu, Y., Xu, H., Lu, W. B., & Meng, Q. (2018) · Information Sciences · doi:10.1016/j.ins.2018.08.066 · Cited by 30 · see citing works
Stereoscopic Visual Attention Guided Seam Carving for Stereoscopic Image Retargeting
Shao, F., Lin, W., Lin, W., Jiang, G., Yu, M., & Fu, R. (2015) · Journal of Display Technology · doi:10.1109/jdt.2015.2446973 · Cited by 29 · see citing works
Self-citationSparse Representation-Based Video Quality Assessment for Synthesized 3D Videos
Zhang, Y., Zhang, H., Yu, M., Kwong, S., & Ho, Y. (2019) · IEEE Transactions on Image Processing · doi:10.1109/tip.2019.2929433 · Cited by 29 · see citing works
Self-citationShao, F., Tian, W., Lin, W., Jiang, G., & Dai, Q. (2017) · IEEE Transactions on Multimedia · doi:10.1109/tmm.2017.2685240 · Cited by 27 · see citing works
Self-citationHigh-Efficiency 3D Depth Coding Based on Perceptual Quality of Synthesized Video
Zhang, Y., Yang, X., Liu, X., Zhang, Y., Jiang, G., & Kwong, S. (2016) · IEEE Transactions on Image Processing · doi:10.1109/tip.2016.2615290 · Cited by 26 · see citing works
Self-citationMa, J., An, P., Shen, L., & Li, K. (2017) · IEEE Access · doi:10.1109/access.2017.2785282 · Cited by 25 · see citing works
Self-citationNo-Reference View Synthesis Quality Prediction for 3-D Videos Based on Color–Depth Interactions
Shao, F., Yuan, Q., Lin, W., & Jiang, G. (2017) · IEEE Transactions on Multimedia · doi:10.1109/tmm.2017.2748460 · Cited by 25 · see citing works
Self-citationWang, H., Li, C., Guan, T., & Zhao, S. (2021) · Displays · doi:10.1016/j.displa.2021.102058 · Cited by 24 · see citing works
Jiang, G., Xu, H., Yu, M., Luo, T., & Zhang, Y. (2017) · Journal of Visual Communication and Image Representation · doi:10.1016/j.jvcir.2017.04.010 · Cited by 22 · see citing works
Self-citationSun, G., Shi, B., Chen, X., Krylov, A. S., & Ding, Y. S. (2020) · IEEE Transactions on Multimedia · doi:10.1109/tmm.2020.2965461 · Cited by 22 · see citing works
Blind Stereo Quality Assessment Based on Learned Features From Binocular Combined Images
Karimi, M., Nejati, M., Soroushmehr, S. M. R., Samavi, S., Karimi, N., & Najarian, K. (2017) · IEEE Transactions on Multimedia · doi:10.1109/tmm.2017.2699082 · Cited by 20 · see citing works
Modeling the Perceptual Quality of Stereoscopic Images in the Primary Visual Cortex
Shao, F., Chen, W., Jiang, G., & Ho, Y. (2017) · IEEE Access · doi:10.1109/access.2017.2733161 · Cited by 20 · see citing works
Self-citationLiu, Y., Yan, W., Zheng, Z., Huang, B., & Yu, H. (2020) · IEEE Access · doi:10.1109/access.2020.2974006 · Cited by 19 · see citing works
Local and global sparse representation for no-reference quality assessment of stereoscopic images
Li, F., Shao, F., Jiang, Q., Fu, R., Jiang, G., & Yu, M. (2017) · Information Sciences · doi:10.1016/j.ins.2017.09.011 · Cited by 18 · see citing works
Self-citationFull-reference quality assessment of stereoscopic images by learning binocular visual properties
Ma, J., An, P., Shen, L., & Li, K. (2017) · Applied Optics · doi:10.1364/ao.56.008291 · Cited by 18 · see citing works
Self-citationYang, J., Jiang, B., Wang, Y., Lu, W. B., & Meng, Q. (2017) · Information Sciences · doi:10.1016/j.ins.2017.10.053 · Cited by 18 · see citing works
Leveraging visual attention and neural activity for stereoscopic 3D visual comfort assessment
Jiang, Q., Shao, F., Jiang, G., Yu, M., & Peng, Z. (2016) · Multimedia Tools and Applications · doi:10.1007/s11042-016-3548-2 · Cited by 17 · see citing works
Self-citationQuality of experience measurement for light field 3D displays on multilayer LCDs
Wang, S., Ong, K. S., Surman, P., Yuan, J., Zheng, Y., & Sun, X. W. (2016) · Journal of the Society for Information Display · doi:10.1002/jsid.514 · Cited by 17 · see citing works
No-reference screen content video quality assessment
Li, T., Min, X., Zhu, W., Xu, Y., & Zhang, W. (2021) · Displays · doi:10.1016/j.displa.2021.102030 · Cited by 16 · see citing works
Jiang, Q., Peng, Z., Shao, F., Gu, K., Zhang, Y., Zhang, W., & Lin, W. (2021) · IEEE Transactions on Broadcasting · doi:10.1109/tbc.2021.3113280 · Cited by 16 · see citing works
Self-citationSiamese-Network-Based Learning to Rank for No-Reference 2D and 3D Image Quality Assessment
Niu, Y., Huang, D., Shi, Y., & Ke, X. (2019) · IEEE Access · doi:10.1109/access.2019.2930707 · Cited by 16 · see citing works
3D saliency guided deep quality predictor for no-reference stereoscopic images
Messai, O., Chetouani, A., Hachouf, F., & Seghir, Z. A. (2022) · Neurocomputing · doi:10.1016/j.neucom.2022.01.002 · Cited by 15 · see citing works
Jiang, G., He, M., Yu, M., Shao, F., & Peng, Z. (2018) · IET Image Processing · doi:10.1049/iet-ipr.2017.0650 · Cited by 15 · see citing works
Self-citationNo-Reference Stereoimage Quality Assessment for Multimedia Analysis Towards Internet-of-Things
Yang, J., Jiang, B., Song, H. H., Yang, X., Lu, W. B., & Liu, H. (2018) · IEEE Access · doi:10.1109/access.2018.2791560 · Cited by 15 · see citing works
Bivariate analysis of 3D structure for stereoscopic image quality assessment
Yao, Y., Shen, L., & An, P. (2018) · Signal Processing Image Communication · doi:10.1016/j.image.2018.02.014 · Cited by 14 · see citing works
Xu, X., Shi, B., Gu, Z., Deng, R., Chen, X., Krylov, A. S., & Ding, Y. S. (2019) · IEEE Access · doi:10.1109/access.2019.2925084 · Cited by 14 · see citing works
Jiang, Q., Shao, F., Jiang, G., Yu, M., & Peng, Z. (2017) · Neurocomputing · doi:10.1016/j.neucom.2016.02.089 · Cited by 13 · see citing works
Self-citationBlind stereo image quality assessment inspired by brain sensory-motor fusion
Karimi, M., Soltanian, N., Samavi, S., Najarian, K., Karimi, N., & Soroushmehr, S. M. R. (2019) · Digital Signal Processing · doi:10.1016/j.dsp.2019.03.004 · Cited by 13 · see citing works
Multistage Pooling for Blind Quality Prediction of Asymmetric Multiply-Distorted Stereoscopic Images
Shao, F., Gao, Y., Jiang, Q., Jiang, G., & Ho, Y. (2018) · IEEE Transactions on Multimedia · doi:10.1109/tmm.2018.2817072 · Cited by 12 · see citing works
Self-citationDing, Y. S., Zhao, Y., Chen, X., Zhu, X., & Andrey, K. (2019) · IET Image Processing · doi:10.1049/iet-ipr.2018.5605 · Cited by 12 · see citing works
Wang, X., Shao, F., Jiang, Q., Meng, X., & Ho, Y. (2020) · IEEE Transactions on Multimedia · doi:10.1109/tmm.2020.2993942 · Cited by 12 · see citing works
Self-citationYang, J., Sim, K., Jiang, B., & Lu, W. B. (2018) · Applied Optics · doi:10.1364/ao.57.003915 · Cited by 11 · see citing works
Stereoscopic Image Quality Assessment by Considering Binocular Visual Mechanisms
Sun, G., Ding, Y. S., Deng, R., Zhao, Y., Chen, X., & Krylov, A. S. (2018) · IEEE Access · doi:10.1109/access.2018.2869525 · Cited by 11 · see citing works
Deep network based stereoscopic image quality assessment via binocular summing and differencing
Hu, J., Wang, X., Chai, X., Shao, F., & Jiang, Q. (2021) · Journal of Visual Communication and Image Representation · doi:10.1016/j.jvcir.2021.103420 · Cited by 11 · see citing works
Self-citationOptimal Region Selection for Stereoscopic Video Subtitle Insertion
Yue, G., Hou, C., Lei, J., Fang, Y., & Lin, W. (2017) · IEEE Transactions on Circuits and Systems for Video Technology · doi:10.1109/tcsvt.2017.2739756 · Cited by 10 · see citing works
Self-citationToward a Quality Predictor for Stereoscopic Images via Analysis of Human Binocular Visual Perception
Liu, Y., Kong, F., & Zhen, Z. (2019) · IEEE Access · doi:10.1109/access.2019.2919155 · Cited by 10 · see citing works
Multi-layer and Multi-scale feature aggregation for DIBR-Synthesized image quality assessment
Zhang, K., Wang, X., Chai, X., & Shao, F. (2023) · Journal of Visual Communication and Image Representation · doi:10.1016/j.jvcir.2023.103851 · Cited by 9 · see citing works
Self-citationZhang, Y., Liu, X., Liu, H., & Fan, C. (2019) · Signal Processing Image Communication · doi:10.1016/j.image.2019.07.012 · Cited by 9 · see citing works
Blind S3D image quality prediction using classical and non-classical receptive field models
Liu, L., Zhang, J., Saad, M. A., Huang, H., & Bovik, A. C. (2020) · Signal Processing Image Communication · doi:10.1016/j.image.2020.115915 · Cited by 9 · see citing works
Wang, X., Shao, F., Jiang, Q., Fu, Z., Meng, X., Gu, K., & Ho, Y. (2021) · IEEE Transactions on Multimedia · doi:10.1109/tmm.2021.3081259 · Cited by 8 · see citing works
Self-citationMultiple just-noticeable-difference-based no-reference stereoscopic image quality assessment
Gu, Z., Ding, Y. S., Deng, R., Chen, X., & Krylov, A. S. (2019) · Applied Optics · doi:10.1364/ao.58.000340 · Cited by 6 · see citing works
A Survey on Image Quality Assessment Techniques, Challenges and Databases
Yogita, V. H., & Patil, H. Y. (2015) · Cited by 7 · see citing works
A comprehensive review of image retargeting
Fan, X., Zhang, Z., Sun, L., Xiao, B., & Durrani, T. S. (2024) · Neurocomputing · doi:10.1016/j.neucom.2024.127416 · Cited by 6 · see citing works
Opinion-unaware blind stereoscopic image quality assessment: A comprehensive study
Yan, J., Fang, Y., Liu, X., Jiang, W., & Liu, Y. (2025) · Pattern Recognition · doi:10.1016/j.patcog.2025.111749 · Cited by 6 · see citing works
Stereoscopic Image Quality Assessment Based on Cyclopean Image
Lu, K., & Zhu, W. (2016) · IEEE Intl Conf on Dependable, Autonomic and Secure Computing, Intl Conf on Pervasive Intelligence and Computing, Intl Conf on Cloud and Big Data Computing, Intl Conf on Cyber Science and Technology Congress (DASC/PiCom/CBDCom/CyberSciTech) · doi:10.1109/dasc-picom-datacom-cyberscitec.2016.169 · Cited by 6 · see citing works
Sparsity based stereoscopic image quality assessment
Khan, S., & Channappayya, S. S. (2016) · Asilomar Conference on Signals, Systems and Computers (ACSSC) · doi:10.1109/acssc.2016.7869706 · Cited by 6 · see citing works
Yang, X., Sun, Q., & Wang, T. (2018) · Applied Optics · doi:10.1364/ao.57.003268 · Cited by 6 · see citing works
Stereoscopic image quality assessment combining statistical features and binocular theory
Yang, J., Xu, H., Zhao, Y. S., Liu, H., & Lu, W. B. (2018) · Pattern Recognition Letters · doi:10.1016/j.patrec.2018.10.012 · Cited by 6 · see citing works
Chen, L., & Zhao, J. (2018) · Multimedia Tools and Applications · doi:10.1007/s11042-018-6759-x · Cited by 6 · see citing works
Visual quality prediction on distorted stereoscopic images
Lin, C.-T., Liu, T.-J., & Liu, K.-H. (2017) · Proceedings - International Conference on Image Processing · doi:10.1109/icip.2017.8296929 · Cited by 5 · see citing works
Fan, Y., Larabi, M., Cheikh, F. A., & Fernández-Maloigne, C. (2017) · Proceedings - International Conference on Image Processing · doi:10.1109/icip.2017.8296383 · Cited by 4 · see citing works
Guan, T., Li, C., & Ma, J. (2019) · International Congress on Image and Signal Processing (CISP) · doi:10.1109/cisp-bmei48845.2019.8965677 · Cited by 4 · see citing works
Zhou, W., Zhou, Y., Qiu, W., Luo, T., & Zhai, Z. (2019) · Digital Signal Processing · doi:10.1016/j.dsp.2019.07.008 · Cited by 4 · see citing works
Simulating receptive fields of human visual cortex for 3D image quality prediction
Shao, F., Chen, W., Lin, W., Jiang, Q., & Jiang, G. (2016) · Applied Optics · doi:10.1364/ao.55.005488 · Cited by 4 · see citing works
Self-citationParts-based stereoscopic image assessment by learning binocular manifold color visual properties
Xu, H., Yu, M., Luo, T., Zhang, Y., & Jiang, G. (2016) · Journal of Electronic Imaging · doi:10.1117/1.jei.25.6.061611 · Cited by 4 · see citing works
Self-citationBi-featured image quality assessment with the hierarchical image quality enhancement algorithm
Ishu, A., & Garg, N. K. (2016) · International Conference on Inventive Computation Technologies (ICICT) · doi:10.1109/inventive.2016.7824834 · Cited by 4 · see citing works
Emerging Applications and Services in Future 5G Networks
Barakabitze, A. A., & Hines, A. (2022) · Wiley eBooks · doi:10.1002/9781119800828.ch8 · Cited by 4 · see citing works
Stereoscopic Image Quality Assessment Based on Cyclopean Image
Lu, K., & Liu, X. (2016) · IEEE International Conference on Internet of Things (iThings) and IEEE Green Computing and Communications (GreenCom) and IEEE Cyber, Physical and Social Computing (CPSCom) and IEEE Smart Data (SmartData) · doi:10.1109/ithings-greencom-cpscom-smartdata.2016.158 · Cited by 3 · see citing works
Ma, J., Xu, G., & Han, X. (2021) · IEEE Access · doi:10.1109/access.2021.3129814 · Cited by 3 · see citing works
Hierarchical multi-scale stereoscopic image quality assessment based on visual mechanism
Chang, Y., Li, S., & Zhao, P. (2022) · Signal Image and Video Processing · doi:10.1007/s11760-021-02068-0 · Cited by 3 · see citing works
Visual attention modeling for stereoscopic video
Fang, Y., Zhang, C., Li, J., Silva, M. P. D., & Callet, P. L. (2016) · IEEE International Conference on Multimedia & Expo Workshops (ICMEW) · doi:10.1109/icmew.2016.7574768 · Cited by 3 · see citing works
Just-noticeable difference binary pattern for reduced reference image quality assessment
Miao, X., & Lee, D.-J. (2019) · Optical Engineering · doi:10.1117/1.oe.58.9.093105 · Cited by 3 · see citing works
Quality Assessment of 3D Synthesized Images Based on Textural and Structural Distortion Estimation
Alvi, H. M. U. H., Farid, M. S., Khan, M. H., & Grzegorzek, M. (2021) · Applied Sciences · doi:10.3390/app11062666 · Cited by 3 · see citing works
Methods for Image Quality Assessment
Fang, Y., & Lin, W. (2015) · Wiley Encyclopedia of Electrical and Electronics Engineering · doi:10.1002/047134608x.w8282 · Cited by 3 · see citing works
Self-citationMa, J., An, P., Shen, L., & Li, K. (2018) · Journal of Electronic Imaging · doi:10.1117/1.jei.27.4.043016 · Cited by 2 · see citing works
Self-citationGeng, X., Shen, L., An, P., & Liu, Z. (2016) · Visual Communications and Image Processing · doi:10.1109/vcip.2016.7805534 · Cited by 2 · see citing works
Stereoscopic Image Quality Assessment Based on Binocular Adding and Subtracting
Yang, J., Jiang, B., Ji, C., Zhu, Y., & Lu, W. B. (2017) · Communications in computer and information science · doi:10.1007/978-981-10-7302-1_20 · Cited by 2 · see citing works
Weighted ensemble learning prediction for blind symmetrically distorted stereoscopic images
Pan, D., Shi, P. A., Zhong, D., Hou, M., Ying, Z., & Fu, S. (2017) · International Conference on Systems and Informatics (ICSAI) · doi:10.1109/icsai.2017.8248486 · Cited by 2 · see citing works
Stereoscopic Image Quality Assessment
Ding, Y. S., & Sun, G. (2020) · Advanced topics in science and technology in China · doi:10.1007/978-981-15-7764-2 · Cited by 2 · see citing works
Cyclopean Image Based Stereoscopic Image Quality Assessment by Using Sparse Representation
Chang, Y., Li, S., Han, X., & Hou, C. (2018) · Proceedings - International Conference on Image Processing · doi:10.1109/icip.2018.8451047 · Cited by 2 · see citing works
Reduced-Reference Image Quality Assessment Based on Improved Local Binary Pattern
Miao, X., Lee, D.-J., Cheng, X., & Yang, X. (2018) · Lecture notes in computer science · doi:10.1007/978-3-030-03801-4_34 · Cited by 2 · see citing works
Zheng, Z., Liu, Y., Liu, Y., Huang, B., & Yu, H. (2020) · Journal of Visual Communication and Image Representation · doi:10.1016/j.jvcir.2020.102848 · Cited by 2 · see citing works
Estimating Depth-Salient Edges And its Application To Stereoscopic Image Quality AssessmentPreprint
Khan, S., & Channappayya, S. S. (2018) · arXiv (Cornell University) · doi:10.48550/arxiv.1802.03883 · Cited by 2 · see citing works
New Reduced-Reference Stereo Image Quality Assessment Model for 3D Visual Communication
Wang, Y., Zheng, K., Yu, M., Du, B., & Jiang, G. (2017) · Communications in computer and information science · doi:10.1007/978-981-10-3966-9_41 · Cited by 1 · see citing works
Self-citation
- Authors
- Shao, F., Li, K., Lin, W., Jiang, G., Yu, M., & Dai, Q.
- Affiliations
- Ningbo University (CN)
- Nanyang Technological University (SG)
- Tsinghua University (CN)
- Venue
- IEEE Transactions on Image Processing
- Published
- 2015-10
- Volume
- 24
- Issue
- 10
- Pages
- 2971-2983
- ISSN
- 1057-7149 (ISSN-L)
- 1941-0042
- OpenAlex
- W2293889938
- Cited by
- 116
- References
- 69
- Citations by year
- 2025: 3 · 2024: 3 · 2023: 1 · 2022: 4 · 2021: 9 · 2020: 13 · 2019: 19 · 2018: 19 · 2017: 22 · 2016: 18 · 2015: 5
- FWCI
- 18.51
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- IEEE Transactions on Image Processing (publishedVersion)
- PubMed (publishedVersion)
- Funders
- National Natural Science Foundation of China (61271021, 61271270, U1301257)
- K. C. Wong Magna Fund in Ningbo University
- Topics
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Image and Video Quality Assessment
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Image Retrieval and Classification Techniques
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Digital Media Forensic Detection
- Keywords
- stereoscopic image quality assessment, full-reference quality metrics, binocular combination, visual perception, 3D image quality
- MeSH terms
- Computer Simulation, Depth Perception, Fixation, Ocular, Humans, Models, Biological, Pattern Recognition, Automated, Photogrammetry, Sensitivity and Specificity, Reproducibility of Results, Imaging, Three-Dimensional, Biomimetics
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
