A depth perception and visual comfort guided computational model for stereoscopic 3D visual saliency |
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| Affiliation: | 1. Faculty of Information Science and Engineering, Ningbo University, Ningbo 315211, China;2. Department of Information and Electronic Engineering, Zhejiang Gongshang University, 310018 Hangzhou, China;1. COSIM Lab., SUP?COM, Carthage Univ., Cité Technologique des Communications, Tunisia;2. Institut Galilée, L2TI, Université Paris 13, Sorbonne Paris Cité, France;1. Department of ECE, University of Thessaly, Volos 38221, Greece;2. High Performance Network Lab, Chinese Academy of Sciences, Beijing 100190, China;3. Department of Electrical Engineering, Yale University, New Haven, 06511 CT, USA;4. University of Nebraska-Lincoln, Omaha 68046, USA;1. Fujian Key Laboratory of Sensing and Computing for Smart City, School of Information Science and Technology, Xiamen University, Xiamen 361005, China;2. Cognitive Science Department, Xiamen University, Xiamen 361005, China;3. Computer Science Department, Xiamen University, Xiamen 361005, China;1. ASELSAN Inc., Turkey;2. Department of Electrical and Electronics Engineering, Middle East Technical University, Turkey |
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| Abstract: | With the emerging development of three-dimensional (3D) related technologies, 3D visual saliency modeling is becoming particularly important and challenging. This paper presents a new depth perception and visual comfort guided saliency computational model for stereoscopic 3D images. The prominent advantage of the proposed model is that we incorporate the influence of depth perception and visual comfort on 3D visual saliency computation. The proposed saliency model is composed of three components: 2D image saliency, depth saliency and visual comfort based saliency. In the model, color saliency, texture saliency and spatial compactness are computed respectively and fused to derive 2D image saliency. Global disparity contrast is considered to compute depth saliency. Particularly, we train a visual comfort prediction function to distinguish stereoscopic image pair as high comfortable stereo viewing (HCSV) or low comfortable stereo viewing (LCSV), and devise different computational rules to generate a visual comfort based saliency map. The final 3D saliency map is obtained by using a linear combination and enhanced by a “saliency-center bias” model. Experimental results show that the proposed 3D saliency model outperforms the state-of-the-art models on predicting human eye fixations and visual comfort assessment. |
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| Keywords: | 3D visual saliency Depth saliency Visual comfort based saliency Visual comfort assessment |
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