No-reference stereoscopic image quality assessment based on binocular collaboration.

Autor: Wang H; Fujian Provincial Key Laboratory of Networking Computing and Intelligent Information Processing, College of Computer and Data Science, Fuzhou University, Fuzhou, 350116, Fujian, China; Engineering Research Center of Big Data Intelligence, Ministry of Education, Fuzhou University, Fuzhou, 350116, China., Ke X; Fujian Provincial Key Laboratory of Networking Computing and Intelligent Information Processing, College of Computer and Data Science, Fuzhou University, Fuzhou, 350116, Fujian, China; Engineering Research Center of Big Data Intelligence, Ministry of Education, Fuzhou University, Fuzhou, 350116, China. Electronic address: kex@fzu.edu.cn., Guo W; Fujian Provincial Key Laboratory of Networking Computing and Intelligent Information Processing, College of Computer and Data Science, Fuzhou University, Fuzhou, 350116, Fujian, China; Engineering Research Center of Big Data Intelligence, Ministry of Education, Fuzhou University, Fuzhou, 350116, China., Zheng W; Fujian Provincial Key Laboratory of Networking Computing and Intelligent Information Processing, College of Computer and Data Science, Fuzhou University, Fuzhou, 350116, Fujian, China; Engineering Research Center of Big Data Intelligence, Ministry of Education, Fuzhou University, Fuzhou, 350116, China.
Jazyk: angličtina
Zdroj: Neural networks : the official journal of the International Neural Network Society [Neural Netw] 2024 Dec; Vol. 180, pp. 106752. Date of Electronic Publication: 2024 Sep 24.
DOI: 10.1016/j.neunet.2024.106752
Abstrakt: Stereoscopic images typically consist of left and right views along with depth information. Assessing the quality of stereoscopic/3D images (SIQA) is often more complex than that of 2D images due to scene disparities between the left and right views and the intricate process of fusion in binocular vision. To address the problem of quality prediction bias of multi-distortion images, we investigated the visual physiology and the processing of visual information by the primary visual cortex of the human brain and proposed a no-reference stereoscopic image quality evaluation method. The method mainly includes an innovative end-to-end NR-SIQA neural network with a picture patch generation algorithm. The algorithm generates a saliency map by fusing the left and right views and then guides the image cropping in the database based on the saliency map. The proposed models are validated and compared based on publicly available databases. The results show that the model and algorithm together outperform the state-of-the-art NR-SIQA metric in the LIVE 3D database and the WIVC 3D database, and have excellent results in the specific noise metric. The model generalization experiments demonstrate a certain degree of generality of our proposed model.
Competing Interests: Declaration of competing interest The authors declare the following financial interests/personal relationships which may be considered as potential competing interests: Wenzhong Guo reports financial support was provided by National Natural Science Foundation of China. Wenzhong Guo reports financial support was provided by National Key Research and Development Plan of China. Wenzhong Guo reports was provided by Natural Science Foundation of Fujian Province. Wenzhong Guo reports was provided by Major Science and Technology Project of Fujian Province. Wenzhong Guo reports financial support was provided by Industry-Academy Cooperation Project of Fujian Province. If there are other authors, they declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.
(Copyright © 2024 Elsevier Ltd. All rights reserved.)
Databáze: MEDLINE
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