Hybrid Local-Global Context Learning for Neural Video Compression
This work addresses video compression efficiency for applications like streaming and storage, but it is incremental as it builds on existing methods by optimizing their combination.
The paper tackled the problem of inaccurate motion estimation and high bit costs in neural video compression by proposing a hybrid context generation module that combines flow-guided deformable compensation at large scales with flow-based warping at smaller scales, achieving significant enhancement over state-of-the-art methods on standard test datasets.
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.