Most of the existing deep learning based end-to-end image/video coding (DLEC) architectures are designed for non-subsampled RGB color format. However, in order to achieve a superior coding performance, many state-of-the-art block-based compression standards such as High Efficiency Video Coding (HEVC/H.265) and Versatile Video Coding (VVC/H.266) are designed primarily for YUV 4:2:0 format, where U and V components are subsampled by considering the human visual system. This paper investigates various DLEC designs to support YUV 4:2:0 format by comparing their performance against the main profiles of HEVC and VVC standards under a common evaluation framework. Moreover, a new transform network architecture is proposed to improve the efficiency of coding YUV 4:2:0 data. The experimental results on YUV 4:2:0 datasets show that the proposed architecture significantly outperforms naive extensions of existing architectures designed for RGB format and achieves about 10% average BD-rate improvement over the intra-frame coding in HEVC.

本研究探究了支持 YUV 4:2:0 格式的各种深度学习编码器设计，并将它们与 HEVC 和 VVC 标准的主要配置进行比较，提出了一种新的转换网络体系结构以提高编码效率，并在 YUV 4:2:0 数据集上进行了实验，结果显示提出的解决方案在性能上显著优于现有 RGB 编码器的朴素扩展，在 HEVC 的帧内编码上平均 BD 码率提高了约 10%。

基于深度学习的亚采样彩色空间端到端图像/视频编码的转换网络架构