Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: A Combined Deep Learning based End-to-End Video Coding Architecture for
  YUV Color Space   Most of the existing deep learning based end-to-end video coding (DLEC)
architectures are designed specifically for RGB color format, yet the video
coding standards, including H.264/AVC, H.265/HEVC and H.266/VVC developed over
past few decades, have been designed primarily for YUV 4:2:0 format, where the
chrominance (U and V) components are subsampled to achieve superior compression
performances considering the human visual system. While a broad number of
papers on DLEC compare these two distinct coding schemes in RGB domain, it is
ideal to have a common evaluation framework in YUV 4:2:0 domain for a more fair
comparison. This paper introduces a new DLEC architecture for video coding to
effectively support YUV 4:2:0 and compares its performance against the HEVC
standard under a common evaluation framework. The experimental results on YUV
4:2:0 video sequences show that the proposed architecture can outperform HEVC
in intra-frame coding, however inter-frame coding is not as efficient on
contrary to the RGB coding results reported in recent papers.

8
The categories of this paper are: hep-ex
9
The categories of this paper are: nlin
16
The categories of this paper are: stat
31
The categories of this paper are: ao-sci
14
The categories of this paper are: math-ph
21
The categories of this paper are: adap-org
27
The categories of this paper are: comp-gas
13
The categories of this paper are: quant-ph
23
The categories of this paper are: solv-int
6
The categories of this paper are: astro-ph
29
The categories of this paper are: funct-an
22
The categories of this paper are: chao-dyn
11
The categories of this paper are: hep-lat
0
The categories of this paper are: hep-ph
28
The categories of this paper are: alg-geom
32
The categories of this paper are: atom-ph