  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.
