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However, compared with 2D image quality assessment, it is much more difficult to assess the quality of stereoscopic images due to the lack of understanding of 3D visual perception. This paper proposes a novel no-reference quality assessment metric for stereoscopic images using natural scene statistics with consideration of both the quality of the cyclopean image and 3D visual perceptual information (binocular fusion and binocular rivalry). In the proposed method, not only is the quality of the cyclopean image considered, but binocular rivalry and other 3D visual intrinsic properties are also exploited. Specifically, in order to improve the objective quality of the cyclopean image, features of the cyclopean images in both the spatial domain and transformed domain are extracted based on the natural scene statistics (NSS) model. Furthermore, to better comprehend intrinsic properties of the stereoscopic image, in our method, the binocular rivalry effect and other 3D visual properties are also considered in the process of feature extraction. Following adaptive feature pruning using principle component analysis, improved metric accuracy can be found in our proposed method. The experimental results show that the proposed metric can achieve a good and consistent alignment with subjective assessment of stereoscopic images in comparison with existing methods, with the highest SROCC (0.952) and PLCC (0.962) scores being acquired on the LIVE 3D database Phase I.<\/jats:p>","DOI":"10.3390\/s23136230","type":"journal-article","created":{"date-parts":[[2023,7,10]],"date-time":"2023-07-10T01:02:50Z","timestamp":1688950970000},"page":"6230","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":6,"title":["A Novel No-Reference Quality Assessment Metric for Stereoscopic Images with Consideration of Comprehensive 3D Quality Information"],"prefix":"10.3390","volume":"23","author":[{"given":"Liquan","family":"Shen","sequence":"first","affiliation":[{"name":"Shanghai Institute for Advanced Communication and Data Science, Shanghai University, Shanghai 200444, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yang","family":"Yao","sequence":"additional","affiliation":[{"name":"Shanghai Institute for Advanced Communication and Data Science, Shanghai University, Shanghai 200444, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Xianqiu","family":"Geng","sequence":"additional","affiliation":[{"name":"Shanghai Institute for Advanced Communication and Data Science, Shanghai University, Shanghai 200444, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Ruigang","family":"Fang","sequence":"additional","affiliation":[{"name":"Department of Electrical and Computer Engineering, University of Florida, Gainesville, FL 32603, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Dapeng","family":"Wu","sequence":"additional","affiliation":[{"name":"Department of Electrical and Computer Engineering, University of Florida, Gainesville, FL 32603, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2023,7,7]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"102025","DOI":"10.1016\/j.displa.2021.102025","article-title":"Large-scale elemental image array generation in integral imaging based on scale invariant feature transform and discrete viewpoint acquisition","volume":"69","author":"Li","year":"2021","journal-title":"Displays"},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"102044","DOI":"10.1016\/j.displa.2021.102044","article-title":"Analysis of college martial arts teaching posture based on 3D image reconstruction and wavelet transform","volume":"69","author":"Deng","year":"2021","journal-title":"Displays"},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"102053","DOI":"10.1016\/j.displa.2021.102053","article-title":"Review of multi-view 3D object recognition methods based on deep learning","volume":"69","author":"Qi","year":"2021","journal-title":"Displays"},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"102102","DOI":"10.1016\/j.displa.2021.102102","article-title":"Multi-view stereo in the Deep Learning Era: A comprehensive review","volume":"70","author":"Wang","year":"2021","journal-title":"Displays"},{"key":"ref_5","doi-asserted-by":"crossref","unstructured":"Winkler, S., and Min, D. 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