PT Unknown
AU Yifan Wang
   Luka Murn
   Luis Herranz
   Fei Yang
   Marta Mrak
   Wei Zhang
   Shuai Wan
   Marc Gorriz Blanch
TI Efficient Super-Resolution for Compression Of Gaming Videos
BT IEEE International Conference on Acoustics, Speech and Signal Processing
PY 2023
DI 10.1109/ICASSP49357.2023.10096031
AB Due to the increasing demand for game-streaming services, efficient compression of computer-generated video is more critical than ever, especially when the available bandwidth is low. This paper proposes a super-resolution framework that improves the coding efficiency of computer-generated gaming videos at low bitrates. Most state-of-the-art super-resolution networks generalize over a variety of RGB inputs and use a unified network architecture for frames of different levels of degradation, leading to high complexity and redundancy. Since games usually consist of a limited number of fixed scenarios, we specialize one model for each scenario and assign appropriate network capacities for different QPs to perform super-resolution under the guidance of reconstructed high-quality luma components. Experimental results show that our framework achieves a superior quality-complexity trade-off compared to the ESRnet baseline, saving at most 93.59% parameters while maintaining comparable performance. The compression efficiency compared to HEVC is also improved by more than 17% BD-rate gain.
ER