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Title: No-reference stereoscopic image-quality metric accounting for left and right similarity map and spatial structure degradation
Authors: Wang, Huanling
Yang, Jiachen
Lu, Wen
Li, Baihua
Tian, Kun
Meng, Qinggang
Issue Date: 2016
Publisher: © 2016 Optical Society of America. One print or electronic copy may be made for personal use only. Systematic reproduction and distribution, duplication of any material in this paper for a fee or for commercial purposes, or modifications of the content of this paper are prohibited.
Citation: WANG, H. ...et al., 2016. No-reference stereoscopic image-quality metric accounting for left and right similarity map and spatial structure degradation. Optics Letters, 41(24), pp. 5640-5643.
Abstract: Blind quality assessment of 3D images is used to confront more real challenges than 2D images. In this Letter, we develop a no-reference stereoscopic image quality assessment (SIQA) model based on the proposed left and right (LR)-similarity map and structural degradation. In the proposed method, local binary pattern features are extracted from the cyclopean image that are effective for describing the distortion of 3D images. More importantly, we first propose the LR-similarity map that can indicate the stereopair quality and demonstrate that the use of LR-similarity information results in a consistent improvement in the performance. The massive experimental results on the LIVE 3D and IRCCyN IQA databases demonstrate that the designed model is strongly correlated to subjective quality evaluations and competitive to the state-of-the-art SIQA algorithms.
Description: This paper was accepted for publication in the journal Optics Letters and the definitive published version is available at http://dx.doi.org/10.1364/OL.41.005640
Sponsor: This research was funded by National Natural Science Foundation of China (NSFC) (61471260, 61271324); Natural Science Foundation of Tianjin (16JCYBJC16000)
Version: Accepted for publication
DOI: 10.1364/OL.41.005640
URI: https://dspace.lboro.ac.uk/2134/23955
Publisher Link: http://dx.doi.org/10.1364/OL.41.005640
ISSN: 0146-9592
Appears in Collections:Published Articles (Computer Science)

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