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J. Patt. Recogn. Artif. Intell."],"published-print":{"date-parts":[[2024,1]]},"abstract":"<jats:p> With the widespread adoption of virtual reality and 360-degree video, there is a pressing need for objective metrics to assess quality in this immersive panoramic format reliably. However, existing image quality assessment models developed for traditional fixed-viewpoint content do not fully consider the specific perceptual issues involved in 360-degree viewing. This paper proposes a 360-degree image full-reference quality assessment (FR-IQA) methodology based on a multi-channel architecture. The proposed 360-degree FR-IQA method further optimizes and identifies the distorted image quality using two easily obtained useful saliency and depth-aware image features. The convolutional neural network (CNN) is designed for training. Furthermore, the proposed method accounts for predicting user viewing behaviors within 360-degree images, which will further benefit the multi-channel CNN architecture and enable the weighted average pooling of the predicted FR-IQA scores. The performance is evaluated on publicly available databases to demonstrate the advantages brought by the proposed multi-channel model in performance evaluation and cross-database evaluation experiments, where it outperforms other state-of-the-art ones. Moreover, an ablation study exhibits good generalization ability and robustness. <\/jats:p>","DOI":"10.1142\/s0218001423510229","type":"journal-article","created":{"date-parts":[[2023,12,12]],"date-time":"2023-12-12T07:49:55Z","timestamp":1702367395000},"source":"Crossref","is-referenced-by-count":2,"title":["Saliency and Depth-Aware Full Reference 360-Degree Image Quality Assessment"],"prefix":"10.1142","volume":"38","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-3761-1759","authenticated-orcid":false,"given":"Xuekai","family":"Wei","sequence":"first","affiliation":[{"name":"College of Computer Science, Chongqing University, 174 Shazheng Street, Chongqing 400044, P. R. China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0009-0003-7090-7193","authenticated-orcid":false,"given":"Qunyue","family":"Huang","sequence":"additional","affiliation":[{"name":"College of Computer Science, Chongqing University, 174 Shazheng Street, Chongqing 400044, P. 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