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Stereoscopic Image Retargeting Based on Deep Convolutional Neural Network

文献类型：期刊论文

作者Fan, Xiaoting6; Lei, Jianjun6; Liang, Jie5; Fang, Yuming4; Ling, Nam3; Huang, Qingming1,2
刊名IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS FOR VIDEO TECHNOLOGY
出版日期2021-12-01
卷号31期号:12页码:4759-4770
关键词Stereo image processing Three-dimensional displays Two dimensional displays Feature extraction Distortion Visualization Shape Stereoscopic image image retargeting cross-attention disparity consistency
ISSN号1051-8215
DOI10.1109/TCSVT.2021.3054062
英文摘要Stereoscopic image retargeting aims at converting stereoscopic images to the target resolution adaptively. Different from 2D image retargeting, stereoscopic image retargeting needs to preserve both the shape structure of salient objects and depth consistency of 3D scenes. In this paper, we present a stereoscopic image retargeting method based on deep convolutional neural network to obtain high-quality retargeted images with both object shape preservation and scene depth preservation. First, a cross-attention extraction mechanism is constructed to generate attention map, which contains the valuable attention features of the left and right images and the common attention features between them. Second, since the disparity map can provide accurate depth information of objects in 3D scenes, a disparity-assisted 3D significance map generation module is utilized to further preserve the valuable depth information of stereoscopic images. Finally, in order to predict the retargeted stereoscopic images accurately, an image consistency loss is developed to preserve the geometric structure of salient objects, and a disparity consistency loss is introduced to eliminate depth distortions. Experimental results demonstrate that the proposed deep convolutional neural network can provide favorable stereoscopic image retargeting results.
资助项目National Natural Science Foundation of China[61931014] ; National Natural Science Foundation of China[61620106009] ; National Natural Science Foundation of China[U1636214] ; National Key Research and Development Program of China[2018YFE0203900] ; Natural Science Foundation of Tianjin[18ZXZNGX00110] ; Natural Science Foundation of Tianjin[18JCJQJC45800] ; China Scholarship Council[201906250188]
WOS研究方向Engineering
语种英语
WOS记录号WOS:000725812500020
出版者IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
源URL[http://119.78.100.204/handle/2XEOYT63/18146]  
专题中国科学院计算技术研究所期刊论文_英文
通讯作者Lei, Jianjun
作者单位1.Chinese Acad Sci, Inst Comp Technol, Key Lab Intelligent Informat Proc, Beijing 100190, Peoples R China
2.Univ Chinese Acad Sci, Sch Comp Sci & Technol, Beijing 101408, Peoples R China
3.Santa Clara Univ, Dept Comp Sci & Engn, Santa Clara, CA 95053 USA
4.Jiangxi Univ Finance & Econ, Sch Informat Technol, Nanchang 330032, Jiangxi, Peoples R China
5.Simon Fraser Univ, Sch Engn Sci, Burnaby, BC V5A 1S6, Canada
6.Tianjin Univ, Sch Elect & Informat Engn, Tianjin 300072, Peoples R China
推荐引用方式
GB/T 7714		 Fan, Xiaoting,Lei, Jianjun,Liang, Jie,et al. Stereoscopic Image Retargeting Based on Deep Convolutional Neural Network[J]. IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS FOR VIDEO TECHNOLOGY,2021,31(12):4759-4770.
APA Fan, Xiaoting,Lei, Jianjun,Liang, Jie,Fang, Yuming,Ling, Nam,&Huang, Qingming.(2021).Stereoscopic Image Retargeting Based on Deep Convolutional Neural Network.IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS FOR VIDEO TECHNOLOGY,31(12),4759-4770.
MLA Fan, Xiaoting,et al."Stereoscopic Image Retargeting Based on Deep Convolutional Neural Network".IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS FOR VIDEO TECHNOLOGY 31.12(2021):4759-4770.

入库方式： OAI收割

来源：计算技术研究所

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