超快神经视频压缩
Ultra-Fast Neural Video Compression
提出基于块（chunk）的编码框架，将多帧编码为单一紧凑潜变量并并行解码，配合跨帧交互模块与帧特定解码器，可显著提升视频压缩的吞吐量并建模长期时序相关性；其精简熵编码机制将比特流交互合并为单步，降低解码开销，对视频压缩与高效视频处理有直接参考价值。
原文依据 Instead of processing frames sequentially, our approach encodes a chunk of multiple frames into a single compact latent representation and decodes them simultaneously.
摘要译文
由模型翻译 · 术语统一 · 请对照原文核对关键结论尽管神经视频编解码器（NVC）已展现出优越的压缩率，但其高昂的计算复杂度仍是实际部署的关键障碍。本文提出一种基于块的编码框架，旨在显著改善率-失真-复杂度权衡。我们的方法不是顺序处理帧，而是将多帧组成的块编码为单一紧凑潜变量表示，并同时解码这些帧。这得益于用于联合时空建模的跨帧交互模块以及用于并行重建的帧特定解码器。这一范式不仅大幅提升了编码吞吐量，还促进了对长期时序相关性的更有效建模。为进一步提升速度，我们提出一种精简的熵编码机制，将比特流交互整合为单一步骤，从而大幅降低解码开销。基于这些创新，我们提出DCVC-UF（超快），一种新的NVC，在性能上树立了新的SOTA。实验表明，DCVC-UF能够实现超快的编码和解码速度，显著优于此前领先的编解码器。DCVC-UF是NVC演进历程中的一个显著里程碑。代码见https://github.com/microsoft/DCVC。
英文原文摘要 1308 字符
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.