Stereoscopic image quality assessment by deep convolutional neural network
| dc.contributor.author | Fang, Yuming | |
| dc.contributor.author | Yan, Jiebin | |
| dc.contributor.author | Liu, Xuelin | |
| dc.contributor.author | Wang, Jiheng | |
| dc.date.accessioned | 2020-02-05T20:41:45Z | |
| dc.date.available | 2020-02-05T20:41:45Z | |
| dc.date.issued | 2019-01 | |
| dc.description | The final publication is available at Elsevier via https://doi.org/10.1016/j.jvcir.2018.12.006. © 2018 This manuscript version is made available under the CC-BY-NC-ND 4.0 license http://creativecommons.org/licenses/by-nc-nd/4.0/ | en |
| dc.description.abstract | In this paper, we propose a no-reference (NR) quality assessment method for stereoscopic images by deep convolutional neural network (DCNN). Inspired by the internal generative mechanism (IGM) in the human brain, which shows that the brain first analyzes the perceptual information and then extract effective visual information. Meanwhile, in order to simulate the inner interaction process in the human visual system (HVS) when perceiving the visual quality of stereoscopic images, we construct a two-channel DCNN to evaluate the visual quality of stereoscopic images. First, we design a Siamese Network to extract high-level semantic features of left- and right-view images for simulating the process of information extraction in the brain. Second, to imitate the information interaction process in the HVS, we combine the high-level features of left- and right-view images by convolutional operations. Finally, the information after interactive processing is used to estimate the visual quality of stereoscopic image. Experimental results show that the proposed method can estimate the visual quality of stereoscopic images accurately, which also demonstrate the effectiveness of the proposed two-channel convolutional neural network in simulating the perception mechanism in the HVS. | en |
| dc.description.sponsorship | This work was supported in part by the Natural Science Foundation of China under Grant 61822109 and 61571212, Fok Ying Tung Education Foundation under Grant 161061 and by the Natural Science Foundation of Jiangxi under Grant 20181BBH80002. | en |
| dc.identifier.uri | https://doi.org/10.1016/j.jvcir.2018.12.006 | |
| dc.identifier.uri | http://hdl.handle.net/10012/15618 | |
| dc.language.iso | en | en |
| dc.publisher | Elsevier | en |
| dc.rights | Attribution-NonCommercial-NoDerivatives 4.0 International | * |
| dc.rights.uri | http://creativecommons.org/licenses/by-nc-nd/4.0/ | * |
| dc.subject | image quality assessment | en |
| dc.subject | stereoscopic images | en |
| dc.subject | no reference | en |
| dc.subject | convolution neural network | en |
| dc.title | Stereoscopic image quality assessment by deep convolutional neural network | en |
| dc.type | Article | en |
| dcterms.bibliographicCitation | Y. Fang, J. Yan, X. Liu, J. Wang, Stereoscopic Image Quality Assessment by Deep Convolutional Neural Network, J. Vis. Commun. Image R. (2018), doi: https://doi.org/10.1016/j.jvcir.2018.12.006 | en |
| uws.contributor.affiliation1 | Faculty of Engineering | en |
| uws.contributor.affiliation2 | Electrical and Computer Engineering | en |
| uws.peerReviewStatus | Reviewed | en |
| uws.scholarLevel | Post-Doctorate | en |
| uws.typeOfResource | Text | en |