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

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.

arXiv comment5 pages, submitted to as a conference paper. arXiv admin note: text overlap with arXiv:2103.01760

References in corpus (6)

• Auto-Encoding Variational Bayes
• Joint Autoregressive and Hierarchical Priors for Learned Image Compression
• CompressAI: a PyTorch library and evaluation platform for end-to-end compression research
• Asymmetric numeral systems: entropy coding combining speed of Huffman coding with compression rate of arithmetic coding
• Transform Network Architectures for Deep Learning based End-to-End Image/Video Coding in Subsampled Color Spaces
• End-to-End Rate-Distortion Optimization for Bi-Directional Learned Video Compression