{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,16]],"date-time":"2026-06-16T14:39:36Z","timestamp":1781620776824,"version":"3.54.5"},"reference-count":65,"publisher":"MDPI AG","issue":"10","license":[{"start":{"date-parts":[[2023,5,22]],"date-time":"2023-05-22T00:00:00Z","timestamp":1684713600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Stabilization Support Plan for Shenzhen Higher Education Institutions","award":["20200812165210001"],"award-info":[{"award-number":["20200812165210001"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Blind image quality assessment (BIQA) aims to evaluate image quality in a way that closely matches human perception. To achieve this goal, the strengths of deep learning and the characteristics of the human visual system (HVS) can be combined. In this paper, inspired by the ventral pathway and the dorsal pathway of the HVS, a dual-pathway convolutional neural network is proposed for BIQA tasks. The proposed method consists of two pathways: the \u201cwhat\u201d pathway, which mimics the ventral pathway of the HVS to extract the content features of distorted images, and the \u201cwhere\u201d pathway, which mimics the dorsal pathway of the HVS to extract the global shape features of distorted images. Then, the features from the two pathways are fused and mapped to an image quality score. Additionally, gradient images weighted by contrast sensitivity are used as the input to the \u201cwhere\u201d pathway, allowing it to extract global shape features that are more sensitive to human perception. Moreover, a dual-pathway multi-scale feature fusion module is designed to fuse the multi-scale features of the two pathways, enabling the model to capture both global features and local details, thus improving the overall performance of the model. Experiments conducted on six databases show that the proposed method achieves state-of-the-art performance.<\/jats:p>","DOI":"10.3390\/s23104974","type":"journal-article","created":{"date-parts":[[2023,5,23]],"date-time":"2023-05-23T02:02:27Z","timestamp":1684807347000},"page":"4974","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":6,"title":["Using HVS Dual-Pathway and Contrast Sensitivity to Blindly Assess Image Quality"],"prefix":"10.3390","volume":"23","author":[{"given":"Fan","family":"Chen","sequence":"first","affiliation":[{"name":"Department of Artificial Intelligence, Shenzhen University, Shenzhen 518060, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2246-7552","authenticated-orcid":false,"given":"Hong","family":"Fu","sequence":"additional","affiliation":[{"name":"Department of Mathematics and Information Technology, The Education University of Hong Kong, Hong Kong, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5852-0813","authenticated-orcid":false,"given":"Hengyong","family":"Yu","sequence":"additional","affiliation":[{"name":"Department of Electrical and Computer Engineering, University of Massachusetts Lowell, Lowell, MA 01854, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5905-3989","authenticated-orcid":false,"given":"Ying","family":"Chu","sequence":"additional","affiliation":[{"name":"Department of Artificial Intelligence, Shenzhen University, Shenzhen 518060, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2023,5,22]]},"reference":[{"key":"ref_1","first-page":"27","article-title":"Display device-adapted video quality-of-experience assessment","volume":"9394","author":"Rehman","year":"2015","journal-title":"Hum. 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