A learning-based visual saliency prediction model for stereoscopic 3D video (LBVS-3D)-Reference-Cited by-同舟云学术

A learning-based visual saliency prediction model for stereoscopic 3D video (LBVS-3D)

Published:2016-11-23 Issue:22 Volume:76 Page:23859-23890
ISSN:1380-7501
Container-title:Multimedia Tools and Applications
language:en
Short-container-title:Multimed Tools Appl

Author:

Banitalebi-Dehkordi Amin,Pourazad Mahsa T.,Nasiopoulos Panos

Funder

Canadian Network for Research and Innovation in Machining Technology, Natural Sciences and Engineering Research Council of Canada

Institute for Computing Information and Cognitive Systems (ICICS) at UBC

Publisher

Springer Science and Business Media LLC

Subject

Computer Networks and Communications,Hardware and Architecture,Media Technology,Software

Link

http://link.springer.com/article/10.1007/s11042-016-4155-y/fulltext.html

Reference86 articles.

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2. Azimi M, Banitalebi-Dehkordi A, Dong Y, Pourazad MT, Nasiopoulos P (2014) Evaluating the performance of existing full-reference quality metrics on High Dynamic Range (HDR) Video content,” ICMSP 2014: XII International Conference on Multimedia Signal Processing, Nov. 2014, Venice

3. Baik M et al. (2013) Investigation of eye-catching colors using eye tracking. Proc. of SPIE-IS&T Electronic Imaging, SPIE vol. 8651

4. Banitalebi-Dehkordi A, Pourazad MT, Nasiopoulos P (2012) A human visual system based 3D video quality metric. 2nd International Conference on 3D Imaging, IC3D, Dec. 2012, Belgium

5. Banitalebi-Dehkordi A, Pourazad MT, Nasiopoulos P (2013a) 3D video quality metric for mobile applications. 38th International Conference on Acoustic, Speech, and Signal Processing, ICASSP, May 2013, Vancouver

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