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arXiv · 2412.00446

Hybrid Local-Global Context Learning for Neural Video Compression

Yongqi Zhai·Jiayu Yang·Wei Jiang·Chunhui Yang·Luyang Tang·Ronggang Wang
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Abstract

In neural video codecs, current state-of-the-art methods typically adopt multi-scale motion compensation to handle diverse motions. These methods estimate and compress either optical flow or deformable offsets to reduce inter-frame redundancy. However, flow-based methods often suffer from inaccurate motion estimation in complicated scenes. Deformable convolution-based methods are more robust but have a higher bit cost for motion coding. In this paper, we propose a hybrid context generation module, which combines the advantages of the above methods in an optimal way and achieves accurate compensation at a low bit cost. Specifically, considering the characteristics of features at different scales, we adopt flow-guided deformable compensation at largest-scale to produce accurate alignment in detailed regions. For smaller-scale features, we perform flow-based warping to save the bit cost for motion coding. Furthermore, we design a local-global context enhancement module to fully explore the local-global information of previous reconstructed signals. Experimental results demonstrate that our proposed Hybrid Local-Global Context learning (HLGC) method can significantly enhance the state-of-the-art methods on standard test datasets.

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Yongqi Zhai, Jiayu Yang, Wei Jiang, Chunhui Yang, Luyang Tang, Ronggang Wang. 2024-11-30. Hybrid Local-Global Context Learning for Neural Video Compression. https://arxiv.org/abs/2412.00446

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