Super Resolution-Based Video Coding via Lightweight Implicit Neural Modeling
Xianlu Bian, Wenyu Wang, Dandan Ding, Urvang B. Joshi, Debargha Mukherjee · 2025
The super-resolution (SR)-based coding tool is widely employed in modern video coding standards. By encoding video frames at a reduced resolution and then restoring them to their original resolution during the in-loop filtering stage, this tool helps to further reduce the bitrate and improve the coding performance. Current video coding standards typically devise rule-based SR methods in their codecs, compromising the coding efficiency to maintain low computational complexity. As deep neural network (DNN)-based SR methods are proving more effective than rule-based approaches, this paper proposes integrating the neural SR into video codecs to enhance coding performance while minimizing the computational cost. To this end, we propose a Lightweight Implicit Neural Model (LIM). Specifically, our LIM, consisting of Lightweight Feature Aggregation Network (LFANet) and Coordinate Upsampling Network Based on B-spline Representation (CURNet), is developed to support SR-based coding at an arbitrary scale. We exemplify the proposed method on the ongoing AVM reference software and conduct extensive experiments to demonstrate its effectiveness. Compared with anchored AVM, our method improves the BD-Rate by 5.52%, which significantly outperforms state-of-the-art works. Meanwhile, its computational complexity is much lower than others, having only 22.5k parameters and 18.7k FLOPs/pixel complexity, which is attractive to real-world applications.