  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.
