Learned video compression methods already outperform VVC in the low-delay
(LD) case, but the random-access (RA) scenario remains challenging. Most works
on learned RA video compression either use HEVC as an anchor or compare it to
VVC in specific test conditions, using RGB-PSNR metric instead of Y-PSNR and
avoiding comprehensive evaluation. Here, we present an end-to-end learned video
codec for random access that combines training on long sequences of frames,
rate allocation designed for hierarchical coding and content adaptation on
inference. We show that under common test conditions (JVET-CTC), it achieves
results comparable to VTM (VVC reference software) in terms of YUV-PSNR BD-Rate
on some classes of videos, and outperforms it on almost all test sets in terms
of VMAF BD-Rate. On average it surpasses open LD and RA end-to-end solutions in
terms of VMAF and YUV BD-Rates.

通过训练长序列帧、分层编码的速率分配和内容适应推理，在常见测试条件下，我们提出了一种用于随机存取的端到端学习视频编解码器，其在某些视频类别上取得了与 VVC 相当的 YUV-PSNR BD-Rate 结果，并在几乎所有测试集上优于 VVC，在 VMAF BD-Rate 方面超过了开源的低延迟和随机访问端到端解决方案的平均水平。