论文标题
B-canf：自适应B框架编码有条件增强归一化流量
B-CANF: Adaptive B-frame Coding with Conditional Augmented Normalizing Flows
论文作者
论文摘要
在过去的几年中，基于学习的视频压缩已成为一个活跃的研究领域。但是，大多数工作都专注于P框架编码。学到的B框架编码的探索不足，更具挑战性。这项工作引入了一个新型的B帧编码框架，称为B-Canf，该框架利用有条件的增强归一化流量来进行B框架编码。 B-Canf还具有两个新的元素：帧型自适应编码和B*-Frames。我们的帧类型自适应编码通过根据B帧类型动态调整特征分布来学习层次B框架编码的更好的位分配。我们的b*-frames通过将B框架编解码器重用以模仿P框架编码，而无需其他单独的P框架编解码器，从而可以更大的灵活性来指定图组（GOP）结构。在常用数据集上，B-CANF与其他学到的B框架编解码器相比，实现了最新的压缩性能，并以PSNR的方式显示了随机访问配置下的BD率结果与HM-16.23。当对不同的共和党结构进行评估时，我们的B*-Frames具有与单独使用P-Frame编解码器的附加使用相似的性能。
Over the past few years, learning-based video compression has become an active research area. However, most works focus on P-frame coding. Learned B-frame coding is under-explored and more challenging. This work introduces a novel B-frame coding framework, termed B-CANF, that exploits conditional augmented normalizing flows for B-frame coding. B-CANF additionally features two novel elements: frame-type adaptive coding and B*-frames. Our frame-type adaptive coding learns better bit allocation for hierarchical B-frame coding by dynamically adapting the feature distributions according to the B-frame type. Our B*-frames allow greater flexibility in specifying the group-of-pictures (GOP) structure by reusing the B-frame codec to mimic P-frame coding, without the need for an additional, separate P-frame codec. On commonly used datasets, B-CANF achieves the state-of-the-art compression performance as compared to the other learned B-frame codecs and shows comparable BD-rate results to HM-16.23 under the random access configuration in terms of PSNR. When evaluated on different GOP structures, our B*-frames achieve similar performance to the additional use of a separate P-frame codec.
