论文标题
YUV 4：2：0内容的学习层次B框架编码具有自适应功能调制
Learned Hierarchical B-frame Coding with Adaptive Feature Modulation for YUV 4:2:0 Content
论文作者
论文摘要
本文介绍了一个学识渊博的层次B框架编码方案，以应对ISCAS 2023的基于神经网络的视频编码的巨大挑战。我们特别讨论了三个问题，包括（1）B-frame编码，（2）YUV 4：2：2：2：2：2：2：2），（3）仅具有一个单个模型的内容适应性可变率编码。大多数博学的视频编解码器在RGB域内部运行，用于P框架编码。 YUV 4：2：0的B框架编码很大程度上探索了。此外，尽管有条件卷积的可变速率编码进行了先前的作品，但其中大多数未能考虑内容信息。我们在条件增强归一化流（CANF）上构建了计划。它具有有条件的运动和框架间编解码器，用于有效的B框架编码。为了应对YUV 4：2：0内容，使用两个条件框架间的编解码器分别处理Y和UV组件，并在Y组件上添加了UV组件的编码。此外，我们在每个卷积层中介绍自适应特征调制，同时考虑到内容信息和B框架的编码水平，以实现内容自适应可变率编码。实验结果表明，我们的模型在PSNR-YUV方面优于X265和去年对常用数据集的挑战的获胜者。
This paper introduces a learned hierarchical B-frame coding scheme in response to the Grand Challenge on Neural Network-based Video Coding at ISCAS 2023. We address specifically three issues, including (1) B-frame coding, (2) YUV 4:2:0 coding, and (3) content-adaptive variable-rate coding with only one single model. Most learned video codecs operate internally in the RGB domain for P-frame coding. B-frame coding for YUV 4:2:0 content is largely under-explored. In addition, while there have been prior works on variable-rate coding with conditional convolution, most of them fail to consider the content information. We build our scheme on conditional augmented normalized flows (CANF). It features conditional motion and inter-frame codecs for efficient B-frame coding. To cope with YUV 4:2:0 content, two conditional inter-frame codecs are used to process the Y and UV components separately, with the coding of the UV components conditioned additionally on the Y component. Moreover, we introduce adaptive feature modulation in every convolutional layer, taking into account both the content information and the coding levels of B-frames to achieve content-adaptive variable-rate coding. Experimental results show that our model outperforms x265 and the winner of last year's challenge on commonly used datasets in terms of PSNR-YUV.
