@InProceedings{YifanWang2023,
author="Yifan Wang
and Luka Murn
and Luis Herranz
and Fei Yang
and Marta Mrak
and Wei Zhang
and Shuai Wan
and Marc Gorriz Blanch",
title="Efficient Super-Resolution for Compression Of Gaming Videos",
booktitle="IEEE International Conference on Acoustics, Speech and Signal Processing",
year="2023",
abstract="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.",
optnote="LAMP; MACO",
optnote="exported from refbase (http://158.109.8.37/show.php?record=3911), last updated on Fri, 19 Jan 2024 13:01:15 +0100",
doi="10.1109/ICASSP49357.2023.10096031",
opturl="https://ieeexplore.ieee.org/document/10096031"
}