{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,30]],"date-time":"2026-01-30T01:21:04Z","timestamp":1769736064676,"version":"3.49.0"},"reference-count":59,"publisher":"MDPI AG","issue":"2","license":[{"start":{"date-parts":[[2022,1,10]],"date-time":"2022-01-10T00:00:00Z","timestamp":1641772800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100001809","name":"the National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["61471150"],"award-info":[{"award-number":["61471150"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>The human visual system (HVS), affected by viewing distance when perceiving the stereo image information, is of great significance to study of stereoscopic image quality assessment. Many methods of stereoscopic image quality assessment do not have comprehensive consideration for human visual perception characteristics. In accordance with this, we propose a Rich Structural Index (RSI) for Stereoscopic Image objective Quality Assessment (SIQA) method based on multi-scale perception characteristics. To begin with, we put the stereo pair into the image pyramid based on Contrast Sensitivity Function (CSF) to obtain sensitive images of different resolution. Then, we obtain local Luminance and Structural Index (LSI) in a locally adaptive manner on gradient maps which consider the luminance masking and contrast masking. At the same time we use Singular Value Decomposition (SVD) to obtain the Sharpness and Intrinsic Structural Index (SISI) to effectively capture the changes introduced in the image (due to distortion). Meanwhile, considering the disparity edge structures, we use gradient cross-mapping algorithm to obtain Depth Texture Structural Index (DTSI). After that, we apply the standard deviation method for the above results to obtain contrast index of reference and distortion components. Finally, for the loss caused by the randomness of the parameters, we use Support Vector Machine Regression based on Genetic Algorithm (GA-SVR) training to obtain the final quality score. We conducted a comprehensive evaluation with state-of-the-art methods on four open databases. The experimental results show that the proposed method has stable performance and strong competitive advantage.<\/jats:p>","DOI":"10.3390\/s22020499","type":"journal-article","created":{"date-parts":[[2022,1,10]],"date-time":"2022-01-10T22:03:13Z","timestamp":1641852193000},"page":"499","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":6,"title":["Rich Structural Index for Stereoscopic Image Quality Assessment"],"prefix":"10.3390","volume":"22","author":[{"given":"Hua","family":"Zhang","sequence":"first","affiliation":[{"name":"School of Computer Science and Technology, Hangzhou Dianzi University, Hangzhou 310018, China"},{"name":"Key Laboratory of Network Multimedia Technology of Zhejiang Province, Zhejiang University, Hangzhou 310018, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Xinwen","family":"Hu","sequence":"additional","affiliation":[{"name":"School of Computer Science and Technology, Hangzhou Dianzi University, Hangzhou 310018, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Ruoyun","family":"Gou","sequence":"additional","affiliation":[{"name":"School of Computer Science and Technology, Hangzhou Dianzi University, Hangzhou 310018, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Lingjun","family":"Zhang","sequence":"additional","affiliation":[{"name":"School of Computer Science and Technology, Hangzhou Dianzi University, Hangzhou 310018, China"},{"name":"Key Laboratory of Brain Machine Collaborative Intelligence of Zhejiang Province, Hangzhou Dianzi University, Hangzhou 310018, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Bolun","family":"Zheng","sequence":"additional","affiliation":[{"name":"School of Computer Science and Technology, Hangzhou Dianzi University, Hangzhou 310018, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Zhuonan","family":"Shen","sequence":"additional","affiliation":[{"name":"School of Computer Science and Technology, Hangzhou Dianzi University, Hangzhou 310018, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2022,1,10]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"459","DOI":"10.1007\/s10043-015-0087-4","article-title":"New stereo shooting evaluation metric based on stereoscopic distortion and subjective perception","volume":"3","author":"Yang","year":"2015","journal-title":"Opt. Rev."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"18","DOI":"10.1016\/j.ins.2016.02.043","article-title":"Orientation selectivity based visual pattern for reduced-reference image quality assessment","volume":"251","author":"Wu","year":"2016","journal-title":"Inf. Sci."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"1289","DOI":"10.1109\/TIT.2006.871582","article-title":"Compressed sensing","volume":"4","author":"Donoho","year":"2006","journal-title":"IEEE Trans. Inf. Theory"},{"key":"ref_4","first-page":"10","article-title":"Most apparent distortion: Full-reference image quality assessment and the role of strategy","volume":"1","author":"Larson","year":"2010","journal-title":"J. Electron. Imaging"},{"key":"ref_5","unstructured":"Wang, Z., Simoncelli, E., and Bovik, A. (2003, January 9\u201312). Multiscale structural similarity for image quality assessment. Proceedings of the Thrity-Seventh Asilomar Conference on Signals, Systems & Computers, Pacific Grove, CA, USA."},{"key":"ref_6","first-page":"21","article-title":"Image Quality Assessment Based on Gradient Similarity","volume":"4","author":"Liu","year":"2012","journal-title":"IEEE Trans. Image Process."},{"key":"ref_7","first-page":"19","article-title":"No-Reference and Robust Image Sharpness Evaluation Based on Multiscale Spatial and Spectral Features","volume":"5","author":"Li","year":"2017","journal-title":"IEEE Trans. Multimed."},{"key":"ref_8","unstructured":"Yang, J., Hou, C., Zhou, Y., Zhang, Z., and Guo, J. (2009, January 4\u20136). Objective Quality Assessment Method Of Stereo Images. Proceedings of the 2009 3DTV Conference: The True Vision\u2014Capture, Transmission and Display of 3D Video, Potsdam, Germany."},{"key":"ref_9","first-page":"22","article-title":"Perceptual Full-Reference Quality Assessment of Stereoscopic Images by Considering Binocular Visual Characteristics","volume":"5","author":"Shao","year":"2013","journal-title":"IEEE Trans. Image Process."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"5197","DOI":"10.1016\/j.eswa.2010.10.041","article-title":"Feature selection and parameter optimization for support vector machines: A new approach based on genetic algorithm with feature chromosomes","volume":"38","author":"Zhao","year":"2011","journal-title":"Expert Syst. Appl. Int. J."},{"key":"ref_11","doi-asserted-by":"crossref","unstructured":"Maalouf, A., and Larabi, M. (2011, January 22\u201327). Cyclop: A stereo color image quality assessment metric. Proceedings of the IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), Prague, Czech Republic.","DOI":"10.1109\/ICASSP.2011.5946615"},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"1143","DOI":"10.1016\/j.image.2013.05.006","article-title":"Full-reference quality assessment of stereopairs accounting for rivalry","volume":"9","author":"Chen","year":"2013","journal-title":"Signal Process. Image Commun."},{"key":"ref_13","doi-asserted-by":"crossref","unstructured":"Fezza, S., and Larabi, M. (2014, January 7\u201310). Stereoscopic 3d image quality assessment based on cyclopean view and depth map. Proceedings of the IEEE Fourth International Conference on Consumer Electronics\u2014Berlin (ICCE-Berlin), Berlin, Germany.","DOI":"10.1109\/ICCE-Berlin.2014.7034289"},{"key":"ref_14","first-page":"89","article-title":"Quality index for stereoscopic images by jointly evaluating cyclopean amplitude and cyclopean phase","volume":"1","author":"Lin","year":"2016","journal-title":"IEEE J. Sel. Top. Signal Process."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"251","DOI":"10.1016\/j.ins.2016.09.004","article-title":"Quality assessment metric of stereo images considering cyclopean integration and visual saliency","volume":"373","author":"Yang","year":"2016","journal-title":"Inf. Sci."},{"key":"ref_16","doi-asserted-by":"crossref","unstructured":"Khan, M., and Channappayya, S. (2016, January 6\u20139). Sparsity based stereoscopic image quality assessment. Proceedings of the 50th Asilomar Conference on Signals, Systems and Computers, Pacific Grove, CA, USA.","DOI":"10.1109\/ACSSC.2016.7869706"},{"key":"ref_17","first-page":"1841","article-title":"3d visual attention for stereoscopic image quality assessment","volume":"7","author":"Jiang","year":"2014","journal-title":"JSW"},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"237","DOI":"10.1016\/j.sigpro.2016.01.019","article-title":"Stereoscopic image quality assessment method based on binocular combination saliency model","volume":"125","author":"Liu","year":"2016","journal-title":"Signal Process."},{"key":"ref_19","doi-asserted-by":"crossref","unstructured":"Yao, Y., Shen, X., Geng, X., and An, P. (2016, January 9\u201310). Combining visual saliency and binocular energy for stereoscopic image quality assessment. Proceedings of the International Forum of Digital TV and Wireless Multimedia Communication, Shanghai, China.","DOI":"10.1007\/978-981-10-4211-9_11"},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"5892","DOI":"10.1109\/TIP.2018.2860279","article-title":"Estimating Depth-Salient Edges and Its Application to Stereoscopic Image Quality Assessment","volume":"12","author":"Khan","year":"2018","journal-title":"IEEE Trans. Image Process."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"178","DOI":"10.1016\/j.patcog.2016.01.034","article-title":"Learning structure of stereoscopic image for no-reference quality assessment with convolutional neural network","volume":"59","author":"Zhang","year":"2016","journal-title":"Pattern Recognit."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"1077","DOI":"10.1109\/TMM.2016.2542580","article-title":"Binocular responses for no-reference 3d image quality assessment","volume":"6","author":"Zhou","year":"2016","journal-title":"IEEE Trans. Multimed."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"520","DOI":"10.1109\/TBC.2015.2459851","article-title":"Quality assessment considering viewing distance and image resolution","volume":"3","author":"Gu","year":"2015","journal-title":"IEEE Trans. Broadcast."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/j.image.2016.04.005","article-title":"Multiscale contrast similarity deviation: An effective and efficient index for perceptual image quality assessment","volume":"45","author":"Wang","year":"2016","journal-title":"Signal Process. Image Commun."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"12","DOI":"10.1364\/OE.6.000012","article-title":"Visual detection of spatial contrast patterns: Evaluation of five simple models","volume":"6","author":"Watson","year":"2000","journal-title":"Opt. Express"},{"key":"ref_26","unstructured":"Agaian, S. (1999, January 23\u201329). Visual morphology. Proceedings of the IS & T\/SPIE\u2019s Symposium on Electronic Imaging Science & Technology, San Jose, CA, USA."},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"460","DOI":"10.1109\/TSMCB.2010.2058847","article-title":"Parameterized logarithmic framework for image enhancement systems","volume":"41","author":"Panetta","year":"2011","journal-title":"IEEE Trans. Syst. Man, Cybern. Part B (Cybern.)"},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"2392","DOI":"10.1109\/TIP.2016.2545863","article-title":"A Novel Image Quality Assessment with Globally and Locally Consilient Visual Quality Perception","volume":"25","author":"Bae","year":"2011","journal-title":"IEEE Trans. Image Process."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"893","DOI":"10.1109\/LSP.2013.2272193","article-title":"A novel DCT-based JND model for luminance adaptation effect in DCT frequency","volume":"20","author":"Bae","year":"2013","journal-title":"IEEE Signal Process. Lett."},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"525","DOI":"10.1109\/TIT.1974.1055250","article-title":"The effects of a visual delity criterion of the encoding of images","volume":"4","author":"Mannos","year":"1974","journal-title":"IEEE Trans. Inf. Theory"},{"key":"ref_31","unstructured":"Daly, S. (1987). Subroutine for the Generation of a Two Dimensional Human Visual Contrast Sensitivity Function, Eastman Kodak. Technical Report 233203Y."},{"key":"ref_32","first-page":"636","article-title":"Image quality assessment based on a degradation model","volume":"4","author":"Kite","year":"2000","journal-title":"IEEE Trans. Image Process."},{"key":"ref_33","doi-asserted-by":"crossref","unstructured":"Daly, S. (1992, January 9\u201314). Visible differences predictor: An algorithm for the assessment of image fidelity. Proceedings of the 1992 Symposium on Electronic Imaging: Science and Technology, San Jose, CA, USA.","DOI":"10.1117\/12.135952"},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"3327","DOI":"10.1016\/S0042-6989(97)00121-1","article-title":"The \u2018independent components\u2019 of natural scenes are edge filters","volume":"23","author":"Bell","year":"1997","journal-title":"Vis. Res."},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"684","DOI":"10.1109\/TIP.2013.2293423","article-title":"Gradient magnitude similarity deviation: A highly efficient perceptual image quality index","volume":"2","author":"Xue","year":"2014","journal-title":"IEEE Trans. Image Process."},{"key":"ref_36","first-page":"3775","article-title":"Sparse representation-based image quality index with adaptive sub-dictionaries","volume":"8","author":"Li","year":"2016","journal-title":"IEEE Trans. Image Process."},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"2378","DOI":"10.1109\/TIP.2011.2109730","article-title":"Fsim: A feature similarity index for image quality assessment","volume":"8","author":"Zhang","year":"2011","journal-title":"IEEE Trans. Image Process."},{"key":"ref_38","first-page":"347","article-title":"SVD-based quality metric for image and video using machine learning","volume":"2","author":"Narwaria","year":"2011","journal-title":"IEEE Trans. Syst. Man Cybern. Part B (Cybern.)"},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"3116","DOI":"10.1109\/TIP.2010.2052820","article-title":"Automatic Parameter Selection for Denoising Algorithms Using a No-Reference Measure of Image Content","volume":"19","author":"Narwaria","year":"2010","journal-title":"IEEE Trans. Image Process."},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"754","DOI":"10.1016\/j.visres.2010.10.009","article-title":"Binocular vision","volume":"7","author":"Blake","year":"2011","journal-title":"Vis. Res."},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"23710","DOI":"10.1364\/OE.23.023710","article-title":"Simulating binocular vision for no-reference 3D visual quality measurement","volume":"18","author":"Zhou","year":"2015","journal-title":"Opt. Express"},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"3379","DOI":"10.1109\/TIP.2013.2267393","article-title":"No-Reference quality assessment of natural stereopairs","volume":"9","author":"Chen","year":"2013","journal-title":"IEEE Trans. Image Process."},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"10","DOI":"10.1016\/j.visres.2013.04.015","article-title":"Measuring contrast sensitivity","volume":"90","author":"Pelli","year":"2013","journal-title":"Vis. Res."},{"key":"ref_44","doi-asserted-by":"crossref","first-page":"2032","DOI":"10.1364\/JOSAA.7.002032","article-title":"Contrast in complex images","volume":"10","author":"Peli","year":"1990","journal-title":"JOSA A"},{"key":"ref_45","doi-asserted-by":"crossref","first-page":"204","DOI":"10.1016\/j.sigpro.2018.04.019","article-title":"Blind stereoscopic 3D image quality assessment via analysis of naturalness, structure, and binocular asymmetry","volume":"150","author":"Yue","year":"2018","journal-title":"Signal Process."},{"key":"ref_46","doi-asserted-by":"crossref","first-page":"1468","DOI":"10.1049\/el.2017.2625","article-title":"No-reference stereoscopic image quality assessment based on saliency-guided binocular feature consolidation","volume":"22","author":"Xu","year":"2017","journal-title":"Electron. Lett."},{"key":"ref_47","doi-asserted-by":"crossref","first-page":"37595","DOI":"10.1109\/ACCESS.2018.2851255","article-title":"No-reference stereoscopic image quality assessment using convolutional neural network for adaptive feature extraction","volume":"6","author":"Ding","year":"2018","journal-title":"IEEE Access"},{"key":"ref_48","doi-asserted-by":"crossref","first-page":"3350","DOI":"10.1109\/TIP.2011.2147325","article-title":"Blind image quality assessment: From natural scene statistics to perceptual quality","volume":"12","author":"Moorthy","year":"2011","journal-title":"IEEE Trans. Image Process."},{"key":"ref_49","doi-asserted-by":"crossref","first-page":"1314","DOI":"10.1109\/TIP.2018.2878283","article-title":"A blind stereoscopic image quality evaluator with segmented stacked autoencoders considering the whole visual perception route","volume":"3","author":"Yang","year":"2019","journal-title":"IEEE Trans. Image Process. Mar."},{"key":"ref_50","doi-asserted-by":"crossref","first-page":"870","DOI":"10.1016\/j.image.2012.08.004","article-title":"Subjective evaluation of stereoscopic image quality","volume":"8","author":"Moorthy","year":"2013","journal-title":"Signal Process. Image Commun."},{"key":"ref_51","doi-asserted-by":"crossref","unstructured":"Wang, J., Zeng, K., and Wang, Z. (2014, January 14\u201318). Quality prediction of asymmetrically distorted stereoscopic images from single views. Proceedings of the 2014 IEEE International Conference on Multimedia and Expo (ICME), Chengdu, China.","DOI":"10.1109\/ICME.2014.6890303"},{"key":"ref_52","unstructured":"Song, R., Ko, H., and Kuo, C. (2014). Mcl-3d: A Database for Stereoscopic Image Quality Assessment Using 2d-Image-Plus-Depth Source. arXiv."},{"key":"ref_53","doi-asserted-by":"crossref","first-page":"207","DOI":"10.1016\/j.patcog.2017.06.008","article-title":"Blind quality estimator for 3D images based on binocular combination and extreme learning machine","volume":"71","author":"Zhou","year":"2017","journal-title":"Pattern Recognit."},{"key":"ref_54","doi-asserted-by":"crossref","first-page":"8291","DOI":"10.1364\/AO.56.008291","article-title":"Full-reference quality assessment of stereoscopic images by learning binocular visual properties","volume":"29","author":"Ma","year":"2017","journal-title":"Appl. Opt."},{"key":"ref_55","doi-asserted-by":"crossref","first-page":"54","DOI":"10.1016\/j.image.2016.12.004","article-title":"A stereoscopic image quality assessment model based on independent component analysis and binocular fusion property","volume":"52","author":"Geng","year":"2017","journal-title":"Signal Process. Image Commun."},{"key":"ref_56","doi-asserted-by":"crossref","first-page":"810","DOI":"10.1049\/iet-ipr.2017.0650","article-title":"Perceptual stereoscopic image quality assessment method with tensor decomposition and manifold learning","volume":"5","author":"Jiang","year":"2018","journal-title":"IET Image Process."},{"key":"ref_57","doi-asserted-by":"crossref","first-page":"15706","DOI":"10.1109\/ACCESS.2017.2733161","article-title":"Modeling the perceptual quality of stereoscopic images in the primary visual cortex","volume":"5","author":"Shao","year":"2017","journal-title":"IEEE Access"},{"key":"ref_58","doi-asserted-by":"crossref","first-page":"2059","DOI":"10.1109\/TIP.2016.2538462","article-title":"Toward a blind deep quality evaluator for stereoscopic images based on monocular and binocular interactions","volume":"5","author":"Shao","year":"2016","journal-title":"IEEE Trans. Image Process."},{"key":"ref_59","doi-asserted-by":"crossref","first-page":"8058","DOI":"10.1109\/ACCESS.2018.2890304","article-title":"Blind Stereoscopic Image Quality Assessment Based on Hierarchical Learning","volume":"7","author":"Liu","year":"2019","journal-title":"IEEE Access"}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/22\/2\/499\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,13]],"date-time":"2025-10-13T13:27:12Z","timestamp":1760362032000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/22\/2\/499"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,1,10]]},"references-count":59,"journal-issue":{"issue":"2","published-online":{"date-parts":[[2022,1]]}},"alternative-id":["s22020499"],"URL":"https:\/\/doi.org\/10.3390\/s22020499","relation":{},"ISSN":["1424-8220"],"issn-type":[{"value":"1424-8220","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022,1,10]]}}}