提出新型视频压缩框架，实现超高速编码且保持高画质。
Ultra-Fast Neural Video Compression
- 将多帧打包成一个潜在表示，同步解码提升速度
- 编码解码速度远超现有模型，支持实时应用
- 适合需要低延迟视频处理的场景
尽管神经视频编解码器（NVC）已展现优异压缩率，其高昂的计算复杂度仍是实际部署的主要障碍。本文提出一种基于分块的编码框架，显著优化率-失真-复杂度权衡。不同于逐帧处理，该方法将多帧编码为单一紧凑潜在表示，并并行解码。通过跨帧交互模块实现联合时空建模，配合帧级解码器完成并行重建。此范式大幅提升编码吞吐量，更有效建模长期时序相关性。为进一步提速，提出简化熵编码机制，将比特流交互合并为单步，大幅降低解码开销。基于这些创新，我们构建了DCVC-UF（Ultra-Fast）新编解码器，在性能上达到新SOTA。实验表明，DCVC-UF可实现超高速编码与解码，显著优于此前领先模型。该工作标志着神经视频编码演进的重要里程碑。代码已开源：https://github.com/microsoft/DCVC。
原文摘要 · Abstract (English)
While neural video codecs (NVCs) have demonstrated superior compression ratio, their prohibitive computational complexity remains a critical barrier to real-world deployment. This paper introduces a chunk-based coding framework designed to significantly improve the rate-distortion-complexity trade-off. Instead of processing frames sequentially, our approach encodes a chunk of multiple frames into a single compact latent representation and decodes them simultaneously. This is enabled by cross-frame interaction modules for joint spatial-temporal modeling and frame-specific decoders for parallel reconstruction. This paradigm not only dramatically enhances coding throughput but also facilitates more effective modeling of long-term temporal correlations. To further boost speed, we propose a streamlined entropy coding mechanism that consolidates bit-stream interactions into a single step, substantially reducing decoding overhead. Building on these innovations, we present DCVC-UF (Ultra-Fast), a new NVC that sets a new SOTA in performance. Our experiments show that DCVC-UF can achieve ultra-fast encoding and decoding speeds, significantly outperforming previous leading codecs. DCVC-UF serves as a notable landmark in the journey of NVC evolution. The code is at https://github.com/microsoft/DCVC.
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