JSW 2014 Vol.9(7): 1841-1847 ISSN: 1796-217X
doi: 10.4304/jsw.9.7.1841-1847
doi: 10.4304/jsw.9.7.1841-1847
3D Visual Attention for Stereoscopic Image Quality Assessment
Qiuping Jiang, Fenfang Duan, Feng Shao
Faculty of Information Science and Engineering, Ningbo University, 315211, Ningbo, China
Abstract—Depth perception is one of the most important characteristic in three-dimensional (3D) images different from traditional two-dimensional (2D) images. Therefore, 3D visual attention will be advantageous to improve 3D visual experience and particularly depth perception. In this paper, we propose a 3D visual attention model for stereoscopic image quality assessment task. The proposed model is constructed based on 2D saliency model, center bias, depth cue (foreground cue and background). Different combination and modulation means of the 3D visual attention model for quality assessment are investigated. The experimental results show that compared with other schemes, the proposed 3D visual attention-based pooling scheme can achieve higher consistency with the subjective assessment of stereoscopic images.
Index Terms—3D visual attention, stereoscopic image quality assessment, saliency model, center bias, depth cue
Abstract—Depth perception is one of the most important characteristic in three-dimensional (3D) images different from traditional two-dimensional (2D) images. Therefore, 3D visual attention will be advantageous to improve 3D visual experience and particularly depth perception. In this paper, we propose a 3D visual attention model for stereoscopic image quality assessment task. The proposed model is constructed based on 2D saliency model, center bias, depth cue (foreground cue and background). Different combination and modulation means of the 3D visual attention model for quality assessment are investigated. The experimental results show that compared with other schemes, the proposed 3D visual attention-based pooling scheme can achieve higher consistency with the subjective assessment of stereoscopic images.
Index Terms—3D visual attention, stereoscopic image quality assessment, saliency model, center bias, depth cue
Cite: Qiuping Jiang, Fenfang Duan, Feng Shao, "3D Visual Attention for Stereoscopic Image Quality Assessment," Journal of Software vol. 9, no. 7, pp. 1841-1847, 2014.
General Information
ISSN: 1796-217X (Online)
Frequency: Quarterly
Editor-in-Chief: Prof. Antanas Verikas
Executive Editor: Ms. Yoyo Y. Zhou
Abstracting/ Indexing: DBLP, EBSCO, CNKI, Google Scholar, ProQuest, INSPEC(IET), ULRICH's Periodicals Directory, WorldCat, etc
E-mail: jsweditorialoffice@gmail.com
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