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Neural texture transfer assisted video coding with adaptive up-sampling

文献类型：期刊论文

作者Yu, Li5,6; Chang, Wenshuai5,6; Quan, Weize3,4; Xiao, Jimin2; Yan, Dong-Ming3,4; Gabbouj, Moncef1,6
刊名SIGNAL PROCESSING-IMAGE COMMUNICATION
出版日期2022-09-01
卷号107页码:10
关键词High-efficiency video coding (HEVC) Reference-based super-resolution Low bitrate Video compression Deep learning Machine learning
ISSN号0923-5965
DOI10.1016/j.image.2022.116754
通讯作者Gabbouj, Moncef(moncef.gabbouj@tuni.fi)
英文摘要Deep learning techniques have been extensively investigated for the purpose of further increasing the efficiency of traditional video compression. Some deep learning techniques for down/up-sampling-based video coding were found to be especially effective when the bandwidth or storage is limited. Existing works mainly differ in the super-resolution models used. Some works simply use a single image super-resolution model, ignoring the rich information in the correlation between video frames, while others explore the correlation between frames by simply concatenating the features across adjacent frames. This, however, may fail when the textures are not well aligned. In this paper, we propose to utilize neural texture transfer which exploits the semantic correlation between frames and is able to explore the correlated information even when the textures are not aligned. Meanwhile, an adaptive group of pictures (GOP) method is proposed to automatically decide whether a frame should be down-sampled or not. Experimental results show that the proposed method outperforms the standard HEVC and state-of-the-art methods under different compression configurations. When compared to standard HEVC, the BD-rate (PSNR) and BD-rate (SSIM) of the proposed method are up to-19.1% and-26.5%, respectively.
WOS关键词LEARNING-BASED SUPERRESOLUTION
资助项目National Natural Science Foundation of China[62002172] ; National Natural Science Foundation of China[61972323] ; Natural Science Foundation of the Jiangsu Higher Education Institutions of China[19KJB510040] ; Nanjing Scientific Innovation Foundation for the Returned Overseas Chinese Scholars[R2019LZ04] ; Jiangsu Provincial Double-Innovation Doctor Program[202100002] ; Open Project Program of the National Laboratory of Pattern Recognition (NLPR) , China[2018r080] ; Startup Foundation for Introducing Talent of NUIST, China ; High Performane Computing Center of Nanjing University of Information Science Technology
WOS研究方向Engineering
语种英语
WOS记录号WOS:000812902400002
出版者ELSEVIER
资助机构National Natural Science Foundation of China ; Natural Science Foundation of the Jiangsu Higher Education Institutions of China ; Nanjing Scientific Innovation Foundation for the Returned Overseas Chinese Scholars ; Jiangsu Provincial Double-Innovation Doctor Program ; Open Project Program of the National Laboratory of Pattern Recognition (NLPR) , China ; Startup Foundation for Introducing Talent of NUIST, China ; High Performane Computing Center of Nanjing University of Information Science Technology
源URL[http://ir.ia.ac.cn/handle/173211/49191]  
专题模式识别国家重点实验室_三维可视计算
通讯作者Gabbouj, Moncef
作者单位1.Tampere Univ, Dept Comp Sci, Tampere, Finland
2.Xian Jiaotong Liverpool Univ, Sch Adv Technol, Suzhou 215028, Peoples R China
3.Univ Chinese Acad Sci, Sch Artificial Intelligence, Beijing 100049, Peoples R China
4.Chinese Acad Sci, Inst Automat, Natl Lab Pattern Recognit NLPR, Beijing 100049, Peoples R China
5.Nanjing Univ Informat Sci & Technol, Engn Res Ctr Digital Forens, Minist Educ, Nanjing 210044, Peoples R China
6.Nanjing Univ Informat Sci & Technol, Sch Comp & Software, Nanjing 210044, Peoples R China
推荐引用方式
GB/T 7714		 Yu, Li,Chang, Wenshuai,Quan, Weize,et al. Neural texture transfer assisted video coding with adaptive up-sampling[J]. SIGNAL PROCESSING-IMAGE COMMUNICATION,2022,107:10.
APA Yu, Li,Chang, Wenshuai,Quan, Weize,Xiao, Jimin,Yan, Dong-Ming,&Gabbouj, Moncef.(2022).Neural texture transfer assisted video coding with adaptive up-sampling.SIGNAL PROCESSING-IMAGE COMMUNICATION,107,10.
MLA Yu, Li,et al."Neural texture transfer assisted video coding with adaptive up-sampling".SIGNAL PROCESSING-IMAGE COMMUNICATION 107(2022):10.

入库方式： OAI收割

来源：自动化研究所

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