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。

长视频序列的分层 B 帧视频编码

Hierarchical B-frame Video Coding for Long Group of Pictures

We consider a gossip network, consisting of $n$ nodes, which tracks the
information at a source. The source updates its information with a Poisson
arrival process and also sends updates to the nodes in the network. The nodes
themselves can exchange information among themselves to become as timely as
possible. However, the network structure is sparse and irregular, i.e., not
every node is connected to every other node in the network, rather, the order
of connectivity is low, and varies across different nodes. This asymmetry of
the network implies that the nodes in the network do not perform equally in
terms of timelines. Due to the gossiping nature of the network, some nodes are
able to track the source very timely, whereas, some nodes fall behind versions
quite often. In this work, we investigate how the rate-constrained source
should distribute its update rate across the network to maintain fairness
regarding timeliness, i.e., the overall worst case performance of the network
can be minimized. Due to the continuous search space for optimum rate
allocation, we formulate this problem as a continuum-armed bandit problem and
employ Gaussian process based Bayesian optimization to meet a trade-off between
exploration and exploitation sequentially.

研究内容为在一个稀疏且不规则的交际网络中，调查了信息源如何分配更新速率以实现公平性，并采用高斯过程基于贝叶斯优化方法来解决这个问题。