低延迟配置下传统与学习型视频编解码器的基准测试
Benchmarking Conventional and Learned Video Codecs with a Low-Delay Configuration
AI总结：
本文在低延迟配置下系统比较 H.266/VVC VTM、JVET ECM、AV1 libaom、AVM 及 DCVC-DC/DCVC-FM，发现 JVET ECM 整体性能最优，并揭示学习型编解码器在大背景运动内容上的不稳定性。
摘要：
随着新标准和基于学习的视频编解码器的发展，视频压缩的最新进展带来了显著的编码性能提升。然而，这些工作大多聚焦于允许一定系统延迟的应用场景（例如 MPEG 编解码器中的 Random Access 模式），而这对于实时传输并不总是可接受。本文基于低延迟配置，对最先进的传统视频编码方法和学习型视频编码方法进行了比较研究。具体而言，本研究包括两款 MPEG 标准编解码器（H.266/VVC VTM 和 JVET ECM）、两款 AOM 编解码器（AV1 libaom 和 AVM），以及两个近期的神经视频编码模型（DCVC-DC 和 DCVC-FM）。为实现公平且有意义的比较，评估在 AOM 和 MPEG 通用测试条件所定义的测试序列上、于 YCbCr 4:2:0 色彩空间中进行。评估结果表明，JVET ECM 编解码器在所有受测编解码器中提供最佳的整体编码性能：相较于 AOM AVM，其平均 BD-rate 节省为 16.1%（基于 PSNR）；相较于 DCVC-FM，则为 11.0%。我们还观察到，对于具有大幅背景运动的测试内容，学习型视频编解码器 DCVC-DC 和 DCVC-FM 的性能并不稳定。
英文摘要：
Recent advances in video compression have seen significant coding performance improvements with the development of new standards and learning-based video codecs. However, most of these works focus on application scenarios that allow a certain amount of system delay (e.g., Random Access mode in MPEG codecs), which is not always acceptable for live delivery. This paper conducts a comparative study of state-of-the-art conventional and learned video coding methods based on a low delay configuration. Specifically, this study includes two MPEG standard codecs (H.266/VVC VTM and JVET ECM), two AOM codecs (AV1 libaom and AVM), and two recent neural video coding models (DCVC-DC and DCVC-FM). To allow a fair and meaningful comparison, the evaluation was performed on test sequences defined in the AOM and MPEG common test conditions in the YCbCr 4:2:0 color space. The evaluation results show that the JVET ECM codecs offer the best overall coding performance among all codecs tested, with a 16.1% (based on PSNR) average BD-rate saving over AOM AVM, and 11.0% over DCVC-FM. We also observed inconsistent performance with the learned video codecs, DCVC-DC and DCVC-FM, for test content with large background motions.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
