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UHD Video Coding: A Light-Weight Learning-Based Fast Super-Block Approach.

Authors :
Wang, Miaohui
Xie, Wuyuan
Meng, Xiandong
Zeng, Huanqiang
Ngan, King Ngi
Source :
IEEE Transactions on Circuits & Systems for Video Technology; Oct2019, Vol. 29 Issue 10, p3083-3094, 12p
Publication Year :
2019

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% Bjøntegaard Delta rate savings) in UHD video coding compared to recent HEVC reference software. [ABSTRACT FROM AUTHOR]

Subjects

Subjects :
VIDEO coding
FEATURE extraction

Details

Language :
English
ISSN :
10518215
Volume :
29
Issue :
10
Database :
Complementary Index
Journal :
IEEE Transactions on Circuits & Systems for Video Technology
Publication Type :
Academic Journal
Accession number :
138960718
Full Text :
https://doi.org/10.1109/TCSVT.2018.2873910