Paper Info
Title
Uhd Video Coding: A Light-Weight Learning-Based Fast Super-Block Approach
Abstract
The ultra high-definition (UHD) video format, which has recently become popular, aims to provide high spatial resolution, high temporal frame rate, high sample bit-depth, and wide pixel color gamut. Despite the continued development of global network capacities, it inevitably causes the increased bandwidth cost of catering to the requirement of delivering UHD video services. To address such challenges, this paper presents an improved super coding unit (SCU) method for UHD video coding in High Efficiency Video Coding (HEVC). Initially, the medium coding unit (MCU) is proposed to avoid unnecessary brute-force coding unit (CU) partitions of SCU. Furthermore, the SCU is proposed to be encoded by Direct-MCU and SCU-to-MCU modes: the Direct-MCU mode is intended to better adapt to the texture-rich region, which guarantees the compression efficiency by avoiding extra-size CU partition; the SCU-to-MCU mode is designed for the homogeneous region of UHD content, which saves the encoding time by skipping fine-grained CU partition search. Moreover, a learning-based fast SCU decision approach is proposed to speed up the determination process of Direct-MCU and SCU-to-MCU, where three representative handcrafted features are extracted. Experimental results show that our method achieves an affordable complexity and excellent coding efficiency (up to 7.30% Bjontegaard Delta rate savings) in UHD video coding compared to recent HEVC reference software.
Year
DOI
Venue
2019
10.1109/TCSVT.2018.2873910
IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS FOR VIDEO TECHNOLOGY
Keywords
DocType
Volume
UHD video coding, machine learning, feature extraction, fast mode decision, HEVC
Journal
29
Issue
ISSN
Citations 
10
1051-8215
0
PageRank 
References 
Authors
0.34
5
5
Name
Order
Citations
PageRank
Miaohui Wang111417.57
Xie Wuyuan2145.63
Xiandong Meng3156.71
Huanqiang Zeng439536.94
King Ngi Ngan52383185.21