Compressed Domain Prior-Guided Video Super-Resolution for Cloud Gaming Content

TL;DR

This paper introduces a lightweight super-resolution network tailored for compressed game videos in cloud gaming, effectively reducing artifacts and preserving details by leveraging coding priors and a novel loss function.

Contribution

The paper proposes a novel Coding Prior-Guided Super-Resolution network that utilizes compressed domain features and a partitioned focal frequency loss for improved video reconstruction.

Findings

Outperforms existing SR methods on compressed game videos

Effectively reduces block artifacts and ringing effects

Preserves high-frequency details in reconstructed frames

Abstract

Cloud gaming is an advanced form of Internet service that necessitates local terminals to decode within limited resources and time latency. Super-Resolution (SR) techniques are often employed on these terminals as an efficient way to reduce the required bit-rate bandwidth for cloud gaming. However, insufficient attention has been paid to SR of compressed game video content. Most SR networks amplify block artifacts and ringing effects in decoded frames while ignoring edge details of game content, leading to unsatisfactory reconstruction results. In this paper, we propose a novel lightweight network called Coding Prior-Guided Super-Resolution (CPGSR) to address the SR challenges in compressed game video content. First, we design a Compressed Domain Guided Block (CDGB) to extract features of different depths from coding priors, which are subsequently integrated with features from the U-net backbone. Then, a series of re-parameterization blocks are utilized for reconstruction. Ultimately, inspired by the quantization in video coding, we propose a partitioned focal frequency loss to effectively guide the model's focus on preserving high-frequency information. Extensive experiments demonstrate the advancement of our approach.