sparse representation.pdf (3.42 MB)
Sparse representation based stereoscopic image quality assessment accounting for perceptual cognitive process
journal contribution
posted on 2018-09-10, 09:15 authored by Jiachen Yang, Bin Jiang, Yafang Wang, Wen Lu, Qinggang MengQinggang MengIn 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.
Funding
This work was supported in part by National Natural Science Foundation of China (No.61471260), and Natural Science Foundation of Tianjin: 16JCYBJC16000.
History
School
- Science
Department
- Computer Science
Published in
Information SciencesVolume
430-431Pages
1 - 16Citation
YANG, J. ... et al, 2018. Sparse representation based stereoscopic image quality assessment accounting for perceptual cognitive process. Information Sciences, 430-431, pp.1-16.Publisher
© ElsevierVersion
- AM (Accepted Manuscript)
Publisher statement
This work is made available according to the conditions of the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International (CC BY-NC-ND 4.0) licence. Full details of this licence are available at: https://creativecommons.org/licenses/by-nc-nd/4.0/Acceptance date
2017-10-30Publication date
2017Notes
This paper was published in the journal Information Sciences and the definitive published version is available at https://doi.org/10.1016/j.ins.2017.10.053.ISSN
0020-0255Publisher version
Language
- en