Abstract
In this paper, we concentrate on the super-resolution (SR) of compressed screen content video, in an effort to address the real-world challenges by considering the underlying characteristics of screen content. Firstly, we propose a new dataset for the SR of screen content video with different distortion levels. Meanwhile, we design an efficient SR structure that could capture the characteristics of compressed screen content video and manipulate the inner-connections in consecutive compressed low-resolution frames, facilitating the high-quality recovery of the high-resolution counter-part. Moreover, we design a new loss function for network training to better remedy the compression distortion and perceptual distortion. Experimental results demonstrate the effectiveness and superiority of the proposed method.
Original language | English |
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Title of host publication | Proceedings : DCC 2021 : 2021 Data Compression Conference |
Editors | Ali BILGIN, Michael W. MARCELLIN, Joan SERRA-SAGRISTA, James A. STORER |
Publisher | IEEE |
Pages | 173-182 |
Number of pages | 10 |
ISBN (Electronic) | 9780738112275 |
DOIs | |
Publication status | Published - Mar 2021 |
Externally published | Yes |
Event | 2021 Data Compression Conference, DCC 2021 - Snowbird, United States Duration: 23 Mar 2021 → 26 Mar 2021 |
Publication series
Name | Data Compression Conference Proceedings |
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Volume | 2021-March |
ISSN (Print) | 1068-0314 |
Conference
Conference | 2021 Data Compression Conference, DCC 2021 |
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Country/Territory | United States |
City | Snowbird |
Period | 23/03/21 → 26/03/21 |
Bibliographical note
Publisher Copyright:© 2021 IEEE.
Funding
This research was supported in part by the Science, Technology, and Innovation Commission of Shenzhen Municipality under Project JCYJ20180307123934031, National Natural Science Foundation of China under 62022002, in part by the Hong Kong RGC ECS under Grant 21211018, GRF under Grant 11203220. This work was done while the author was a research intern in ByteDance Inc.