A Combined Deep Learning based End-to-End Video Coding Architecture for YUV Color Space

• URL: http://arxiv.org/abs/2104.00807v1
• Date: Thu, 1 Apr 2021 23:41:06 GMT
• Title: A Combined Deep Learning based End-to-End Video Coding Architecture for YUV Color Space
• Authors: Ankitesh K. Singh, Hilmi E. Egilmez, Reza Pourreza, Muhammed Coban, Marta Karczewicz, Taco S. Cohen
• Abstract summary: Most of the existing deep learning based end-to-end video coding (DLEC) architectures are designed specifically for RGB color format. 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.
• Score: 14.685161934404123
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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