Open AccessNo‐reference stereoscopic image quality assessment based on saliency‐guided binocular feature consolidation
Author(s) -
Xu Xiaogang,
Zhao Yang,
Ding Yong
Publication year - 2017
Publication title -
electronics letters
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.375
H-Index - 146
eISSN - 1350-911X
pISSN - 0013-5194
DOI - 10.1049/el.2017.2625
Subject(s) - artificial intelligence , stereoscopy , skew , computer science , kurtosis , pattern recognition (psychology) , computer vision , image quality , entropy (arrow of time) , feature (linguistics) , image (mathematics) , mathematics , statistics , telecommunications , linguistics , physics , philosophy , quantum mechanics
Different from traditional methods depending on the procedure of intermediate ‘cyclopean’ view construction, a novel framework based on saliency‐guided multi‐scale feature consolidation for stereoscopic image quality assessment is proposed. For quality representation, the underlying features are extracted from three aspects: (i) global natural statistics features, (ii) local spatial and spectral entropy features and (iii) the kurtosis and skew of disparity distribution. Then the binocular features are consolidated by a saliency‐guided weighted process. Finally, a machine learning technique of support vector regression is used for objective quality mapping. Experimental results demonstrate the promising performance of the proposed method.