Paper Info
Title
Sparse representation based stereoscopic image quality assessment accounting for perceptual cognitive process.
Abstract
In this paper, we propose a sparse representation based Reduced-Reference Image Quality Assessment (RR-IQA) index for stereoscopic images from the following two perspectives: 1) Human visual system (HVS) always tries to infer the meaningful information and reduces uncertainty from the visual stimuli, and the entropy of primitive (EoP) can well describe this visual cognitive progress when perceiving natural images. 2) Ocular dominance (also known as binocularity) which represents the interaction between two eyes is quantified by the sparse representation coefficients. Inspired by previous research, the perception and understanding of an image is considered as an active inference process determined by the level of “surprise”, which can be described by EoP. Therefore, the primitives learnt from natural images can be utilized to evaluate the visual information by computing entropy. Meanwhile, considering the binocularity in stereo image quality assessment, a feasible way is proposed to characterize this binocular process according to the sparse representation coefficients of each view. Experimental results on LIVE 3D image databases and MCL database further demonstrate that the proposed algorithm achieves high consistency with subjective evaluation.
Year
DOI
Venue
2018
10.1016/j.ins.2017.10.053
Information Sciences
Keywords
Field
DocType
Sparse representation,Reduced reference image quality assessment,Stereoscopic images,Entropy,Binocularity,Ocular dominance
Computer vision,Binocular vision,Stereoscopy,Computer science,Human visual system model,Inference,Sparse approximation,Image quality,Artificial intelligence,Perception,Machine learning,Visual perception
Journal
Volume
ISSN
Citations 
430
0020-0255
1
PageRank 
References 
Authors
0.35
31
5
Name
Order
Citations
PageRank
Jiachen Yang136229.01
Bin Jiang28513.70
Yafang Wang313413.56
Wen Lu421235.55
Qinggang Meng5507.85