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In this paper, we explore how to extract effective features to enhance the prediction accuracy of perceptual quality assessment. Inspired by the structure representation of the human visual system and the machine learning technique, we propose a no\u2010reference quality assessment scheme for stereoscopic images. More specifically, the statistical features of the gradient magnitude and Laplacian of Gaussian responses are extracted to form binocular quality\u2010predictive features. After feature extraction, these features of distorted stereoscopic image and its human perceptual score are used to construct a statistical regression model with the machine learning technique. Experimental results on the benchmark databases show that the proposed model generates image quality prediction well correlated with the human visual perception and delivers highly competitive performance with the typical and representative methods. The proposed scheme can be further applied to the real\u2010world applications on video broadcasting and 3D multimedia industry.<\/jats:p>","DOI":"10.1155\/2021\/8834652","type":"journal-article","created":{"date-parts":[[2021,1,29]],"date-time":"2021-01-29T19:05:10Z","timestamp":1611947110000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["No\u2010Reference Stereoscopic Image Quality Assessment Based on Binocular Statistical Features and Machine Learning"],"prefix":"10.1155","volume":"2021","author":[{"given":"Peng","family":"Xu","sequence":"first","affiliation":[]},{"given":"Man","family":"Guo","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7922-3551","authenticated-orcid":false,"given":"Lei","family":"Chen","sequence":"additional","affiliation":[]},{"given":"Weifeng","family":"Hu","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6098-2120","authenticated-orcid":false,"given":"Qingshan","family":"Chen","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4455-5991","authenticated-orcid":false,"given":"Yujun","family":"Li","sequence":"additional","affiliation":[]}],"member":"311","published-online":{"date-parts":[[2021,1,29]]},"reference":[{"doi-asserted-by":"crossref","unstructured":"ChenL.andZhaoJ. 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