Online Video Super-Resolution with Convolutional Kernel Bypass Graft
Jun Xiao, Xinyang Jiang, Ningxin Zheng, Huan Yang, Yifan Yang, Yuqing, Yang, Dongsheng Li, Kin-Man Lam

TL;DR
This paper introduces a low-latency, real-time video super-resolution method called CKBG that uses kernel knowledge transfer to enhance a lightweight model, achieving high frame rates suitable for online applications.
Contribution
The paper proposes a novel kernel knowledge transfer method, CKBG, for real-time video super-resolution that combines low latency, low complexity, and competitive performance.
Findings
Processes up to 110 FPS in online video sequences.
Uses a lightweight network without future frame inputs.
Achieves competitive super-resolution quality.
Abstract
Deep learning-based models have achieved remarkable performance in video super-resolution (VSR) in recent years, but most of these models are less applicable to online video applications. These methods solely consider the distortion quality and ignore crucial requirements for online applications, e.g., low latency and low model complexity. In this paper, we focus on online video transmission, in which VSR algorithms are required to generate high-resolution video sequences frame by frame in real time. To address such challenges, we propose an extremely low-latency VSR algorithm based on a novel kernel knowledge transfer method, named convolutional kernel bypass graft (CKBG). First, we design a lightweight network structure that does not require future frames as inputs and saves extra time costs for caching these frames. Then, our proposed CKBG method enhances this lightweight base model…
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Taxonomy
TopicsAdvanced Image Processing Techniques · Advanced Vision and Imaging · Image Processing Techniques and Applications
MethodsBalanced Selection
