Institutional Repository of Chinese Acad Sci, Inst Automat, Natl Lab Pattern Recognit, Beijing 100190, Peoples R China
| Neural texture transfer assisted video coding with adaptive up-sampling | |
Yu, Li1,2; Chang, Wenshuai1,2; Quan, Weize3,4 ; Xiao, Jimin5; Yan, Dong-Ming3,4; Gabbouj, Moncef1,6
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| Source Publication | SIGNAL PROCESSING-IMAGE COMMUNICATION
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| ISSN | 0923-5965 |
| 2022-09-01 | |
| Volume | 107Pages:10 |
| Corresponding Author | Gabbouj, Moncef(moncef.gabbouj@tuni.fi) |
| Abstract | 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. |
| Keyword | High-efficiency video coding (HEVC) Reference-based super-resolution Low bitrate Video compression Deep learning Machine learning |
| DOI | 10.1016/j.image.2022.116754 |
| WOS Keyword | LEARNING-BASED SUPERRESOLUTION |
| Indexed By | SCI |
| Language | 英语 |
| Funding Project | 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 |
| Funding Organization | 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 |
| WOS Research Area | Engineering |
| WOS Subject | Engineering, Electrical & Electronic |
| WOS ID | WOS:000812902400002 |
| Publisher | ELSEVIER |
| Citation statistics | |
| Document Type | 期刊论文 |
| Identifier | http://ir.ia.ac.cn/handle/173211/49192 |
| Collection | 模式识别国家重点实验室_三维可视计算 |
| Corresponding Author | Gabbouj, Moncef |
| Affiliation | 1.Nanjing Univ Informat Sci & Technol, Sch Comp & Software, Nanjing 210044, Peoples R China 2.Nanjing Univ Informat Sci & Technol, Engn Res Ctr Digital Forens, Minist Educ, Nanjing 210044, Peoples R China 3.Chinese Acad Sci, Inst Automat, Natl Lab Pattern Recognit NLPR, Beijing 100049, Peoples R China 4.Univ Chinese Acad Sci, Sch Artificial Intelligence, Beijing 100049, Peoples R China 5.Xian Jiaotong Liverpool Univ, Sch Adv Technol, Suzhou 215028, Peoples R China 6.Tampere Univ, Dept Comp Sci, Tampere, Finland |
| Recommended Citation 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. |
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