论文标题
多功能视频编码和超分辨率，可有效地交付8K视频，并具有4K向后兼容
Versatile video coding and super-resolution for efficient delivery of 8K video with 4K backward-compatibility
论文作者
论文摘要
在本文中，我们通过一项客观研究提出，以比较和评估不同编码方法的性能，从而允许在广播网络上传递具有4K向后兼容的8K视频信号。提出的方法包括使用高效视频编码（HEVC）和多功能视频编码（VVC）标准编码的8K和4K单层信号的模拟广播，使用具有4K基础层（BL）和8K增强层（EL）的SHVC的空间可伸缩性，以及在4K VVC上应用4K VVC Signble Signal Desconduction descondodeds decdoddoddododdesdododdododdesdoddododdododedodeds desdododeDodeDecoddodeDecondossigalsial shvc的空间可伸缩性。为了提高量表，我们选择了基于深度学习的超分辨率方法，称为“超级分辨率”，带有反馈网络（SRFBN）和兰斯佐斯插值过滤器。我们表明，基于深度学习的方法比模拟广播可实现视觉质量的增长，尤其是在低于30Mb/s的比特率上，PSNR，SSIM和VMAF的平均增益分别为0.77dB，0.015和7.97，并且表现出的表现能够平均BD速率节省29％的BD量。
In this paper, we propose, through an objective study, to compare and evaluate the performance of different coding approaches allowing the delivery of an 8K video signal with 4K backward-compatibility on broadcast networks. Presented approaches include simulcast of 8K and 4K single-layer signals encoded using High-Efficiency Video Coding (HEVC) and Versatile Video Coding (VVC) standards, spatial scalability using SHVC with 4K base layer (BL) and 8K enhancement-layer (EL), and super-resolution applied on 4K VVC signal after decoding to reach 8K resolution. For up-scaling, we selected the deep-learning-based super-resolution method called Super-Resolution with Feedback Network (SRFBN) and the Lanczos interpolation filter. We show that the deep-learning-based approach achieves visual quality gain over simulcast, especially on bit-rates lower than 30Mb/s with average gain of 0.77dB, 0.015, and 7.97 for PSNR, SSIM, and VMAF, respectively and out-performs the Lanczos filter in average by 29% of BD-rate savings.
