TY - JOUR
T1 - Industrial Applications of Ultrahigh Definition Video Coding with an Optimized Supersample Adaptive Offset Framework
AU - Wang, Miaohui
AU - Xie, Wuyuan
AU - Zhang, Jia
AU - Qin, Jing
N1 - Funding Information:
Manuscript received December 9, 2019; accepted February 10, 2020. Date of publication February 13, 2020; date of current version September 18, 2020. This work was supported in part by the National Natural Science Foundation of China under Grant 61701310, Grant 61701258, and Grant 61902251, in part by the Natural Science Foundation of Guangdong under Grant 2019A1515010961, in part by the Free Exploration Project for Basic Research of Shenzhen City under GrantJ-CYJ20180305124209486, in part by the Natural Science Foundation of SZU under Grant 2018080, and in part by the Innovation and Technology Fund of Hong Kong under Project ITS/319/17. Paper no. TII-19-5241. (Corresponding author: Wuyuan Xie.) Miaohui Wang is with the Guangdong Key Laboratory of Intelligent Information Processing, Guangdong Laboratory of Artificial Intelligence and Digital Economy (SZ), Shenzhen Institute of Artificial Intelligence and Robotics for Society, and College of Electronics and Information Engineering, Shenzhen University, Shenzhen 518060, China (e-mail: [email protected]).
Publisher Copyright:
© 2005-2012 IEEE.
PY - 2020/12
Y1 - 2020/12
N2 - This article presents an efficient superblock-based sample adaptive offset (superSAO) that jointly exploits block-wise filter partition and probabilistic band interval segmentation to improve quality-of-experience of industrial video applications. Specifically, we investigate the partition flexibility of a superSAO block whose root size is up to 256 × 256, and propose to optimize the block-wise SAO filter by considering computation complexity and compression efficiency. Furthermore, we segment the band interval by equal probability of the sample intensity distributions, which facilitates the computation of better band offsets to attenuate ringing artifact due to quantization errors or encoded motion vectors. Experimental results show that the proposed superSAO method outperforms state-of-the-art approaches by obtaining 4.6% bandwidth reduction on average for the low delay and high-compression video applications.
AB - This article presents an efficient superblock-based sample adaptive offset (superSAO) that jointly exploits block-wise filter partition and probabilistic band interval segmentation to improve quality-of-experience of industrial video applications. Specifically, we investigate the partition flexibility of a superSAO block whose root size is up to 256 × 256, and propose to optimize the block-wise SAO filter by considering computation complexity and compression efficiency. Furthermore, we segment the band interval by equal probability of the sample intensity distributions, which facilitates the computation of better band offsets to attenuate ringing artifact due to quantization errors or encoded motion vectors. Experimental results show that the proposed superSAO method outperforms state-of-the-art approaches by obtaining 4.6% bandwidth reduction on average for the low delay and high-compression video applications.
KW - High efficiency video coding (HEVC)
KW - in-loop filter
KW - sample adaptive offset (SAO)
KW - super-block video coding
KW - ultrahigh definition (UHD)
UR - https://www.scopus.com/pages/publications/85092104995
U2 - 10.1109/TII.2020.2973733
DO - 10.1109/TII.2020.2973733
M3 - Journal article
AN - SCOPUS:85092104995
SN - 1551-3203
VL - 16
SP - 7613
EP - 7623
JO - IEEE Transactions on Industrial Informatics
JF - IEEE Transactions on Industrial Informatics
IS - 12
M1 - 8998152
ER -