Title
On Versatile Video Coding at UHD with Machine-Learning-Based Super-Resolution
Abstract
Coding 4K data has become of vital interest in recent years, since the amount of 4K data is significantly increasing. We propose a coding chain with spatial down- and upscaling that combines the next-generation VVC codec with machine learning based single image super-resolution algorithms for 4K. The investigated coding chain, which spatially downscales the 4K data before coding, shows superior quality than the conventional VVC reference software for low bitrate scenarios. Throughout several tests, we find that up to 12 % and 18% Bj⊘ntegaard delta rate gains can be achieved on average when coding 4K sequences with VVC and QP values above 34 and 42, respectively. Additionally, the investigated scenario with up- and downscaling helps to reduce the loss of details and compression artifacts, as it is shown in a visual example.
Year
DOI
Venue
2020
10.1109/QoMEX48832.2020.9123140
2020 Twelfth International Conference on Quality of Multimedia Experience (QoMEX)
Keywords
DocType
ISSN
VVC,video compression,CNN super-resolution,4K/UHD video,spatial-resolution scaling,VDSR,RDN
Conference
2372-7179
ISBN
Citations 
PageRank 
978-1-7281-5966-9
1
0.39
References 
Authors
0
3
Name
Order
Citations
PageRank
Kristian Fischer124.33
Christian Herglotz2209.45
André Kaup3861127.24