Paper Info
Title
Perceived quality measurement of stereoscopic 3D images based on sparse representation and binocular combination.
Abstract
Measurement of the perceived quality of stereoscopic three-dimensional (S3D) images has attracted an increasing amount of research interest in recent years. This paper proposes a S3D image quality measurement (IQM) metric based on sparse representation and binocular combination. The proposed method involves learning binocular and monocular dictionaries from a training database such that the sparse features of binocular combination can be expressed by a linear combination of a few selected basis feature vectors. Following this, scores for the similarity of these sparse features between reference and distorted S3D images are measured. Based on the observation that sparse features are invariant against weak degradations, similarity scores of the features of the gradient magnitude of binocular combination are then computed and used as a complementary feature. Finally, by using kernel-based support vector regression (SVR), these similarity scores are integrated into an overall quality value. Experimental results on three public S3D-IQM datasets show that in comparison with the relevant existing metrics, the devised metric attains significantly high consistency alignment with subjective quality assessment.
Year
DOI
Venue
2019
10.1016/j.dsp.2019.07.008
Digital Signal Processing
Keywords
Field
DocType
S3D image quality measurement,Binocular combination,Machine learning,Sparse representation
Linear combination,Feature vector,Pattern recognition,Stereoscopy,Support vector machine,Sparse approximation,Image quality,Invariant (mathematics),Artificial intelligence,Monocular,Mathematics
Journal
Volume
ISSN
Citations 
93
1051-2004
0
PageRank 
References 
Authors
0.34
0
5
Name
Order
Citations
PageRank
Wujie Zhou100.34
yang zhou27130.85
Weiwei Qiu3553.90
Ting Luo4339.06
Zhinian Zhai500.34