通过动态滤波与重建增强，显著降低神经视频编码的码率。
Neural Video Compression with In-Loop Contextual Filtering and Out-of-Loop Reconstruction Enhancement
- 分环内上下文滤波与环外重建增强，实现分层质量优化。
- 相比顶尖神经视频编码器，码率降低7.71%。
- 适合追求高效视频压缩的工业级应用与研究者。
本文探索了增强滤波技术在神经视频压缩中的应用。根据增强表示是否影响后续编码环，将技术分为环内上下文滤波和环外重建增强。环内滤波通过减轻逐帧编码中的误差传播，优化时间上下文，但其对当前及后续帧的影响带来自适应滤波应用的挑战。为此，提出一种动态编码决策策略，以自适应决定滤波应用时机。环外重建增强则用于提升重建帧质量，简单而有效提升编码效率。据我们所知，这是首个针对基于条件的神经视频压缩中增强滤波的系统性研究。大量实验表明，相比当前最优神经视频编码器，本方法可实现7.71%的码率降低，验证了所提方法的有效性。
原文摘要 · Abstract (English)
This paper explores the application of enhancement filtering techniques in neural video compression. Specifically, we categorize these techniques into in-loop contextual filtering and out-of-loop reconstruction enhancement based on whether the enhanced representation affects the subsequent coding loop. In-loop contextual filtering refines the temporal context by mitigating error propagation during frame-by-frame encoding. However, its influence on both the current and subsequent frames poses challenges in adaptively applying filtering throughout the sequence. To address this, we introduce an adaptive coding decision strategy that dynamically determines filtering application during encoding. Additionally, out-of-loop reconstruction enhancement is employed to refine the quality of reconstructed frames, providing a simple yet effective improvement in coding efficiency. To the best of our knowledge, this work presents the first systematic study of enhancement filtering in the context of conditional-based neural video compression. Extensive experiments demonstrate a 7.71% reduction in bit rate compared to state-of-the-art neural video codecs, validating the effectiveness of the proposed approach.
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