TY - GEN
T1 - Fast QTMT Partition Decision Algorithm in VVC Intra Coding based on Variance and Gradient
AU - Chen, Junan
AU - Sun, Heming
AU - Katto, Jiro
AU - Zeng, Xiaoyang
AU - Fan, Yibo
N1 - Funding Information:
ACKNOWLEDGEMENT This work was supported in part by the National Natural Science Foundation of China under Grant 61674041, in part by Alibaba Innovative Research (AIR) Program, in part by IBM Faculty Award, in part by the Innovation Program of Shanghai Municipal Education Commission, in part by the pioneering project of academy for engineering and technology and Fudan-CIOMP joint fund.
Publisher Copyright:
© 2019 IEEE.
PY - 2019/12
Y1 - 2019/12
N2 - Quadtree with nested multi-Type tree (QTMT) partition structure in Versatile Video Coding (VVC) contributes to superior encoding performance compared to the basic quad-Tree (QT) structure in High Efficiency Video Coding (HEVC). However, the improvement of performance leads to an un-Avoidable increase of computational complexity. To achieve a balance between coding efficiency and compression quality, we propose a fast intra partition algorithm based on variance and gradient to solve the rectangular partition problem in VVC. First, further splitting of smooth areas is terminated. Then, QT partition is chosen depending on the gradient features extracted by Sobel operator. Finally, one partition from five possible QTMT partitions is directly chosen by computing the variance of variance of sub-CUs. The theoretical basis of our method is that a homogeneous area tends to be predicted with a larger coding unit (CU), and sub-parts of a split CU are prone to have different textures from each other. To our knowledge, this is the first attempt to apply traditional method to accelerating the rectangular partition problem in VVC intra prediction. Experimental results show that the proposed method can save averagely 53.17% encoding time with only 1.62% BDBR increase and 0.09dB BDPSNR loss compared to anchor VTM4.0.
AB - Quadtree with nested multi-Type tree (QTMT) partition structure in Versatile Video Coding (VVC) contributes to superior encoding performance compared to the basic quad-Tree (QT) structure in High Efficiency Video Coding (HEVC). However, the improvement of performance leads to an un-Avoidable increase of computational complexity. To achieve a balance between coding efficiency and compression quality, we propose a fast intra partition algorithm based on variance and gradient to solve the rectangular partition problem in VVC. First, further splitting of smooth areas is terminated. Then, QT partition is chosen depending on the gradient features extracted by Sobel operator. Finally, one partition from five possible QTMT partitions is directly chosen by computing the variance of variance of sub-CUs. The theoretical basis of our method is that a homogeneous area tends to be predicted with a larger coding unit (CU), and sub-parts of a split CU are prone to have different textures from each other. To our knowledge, this is the first attempt to apply traditional method to accelerating the rectangular partition problem in VVC intra prediction. Experimental results show that the proposed method can save averagely 53.17% encoding time with only 1.62% BDBR increase and 0.09dB BDPSNR loss compared to anchor VTM4.0.
KW - Versatile video coding
KW - fast block size decision
KW - gradient
KW - intra prediction
KW - quadtree with multi-Type tree
KW - variance
UR - http://www.scopus.com/inward/record.url?scp=85079223991&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85079223991&partnerID=8YFLogxK
U2 - 10.1109/VCIP47243.2019.8965674
DO - 10.1109/VCIP47243.2019.8965674
M3 - Conference contribution
AN - SCOPUS:85079223991
T3 - 2019 IEEE International Conference on Visual Communications and Image Processing, VCIP 2019
BT - 2019 IEEE International Conference on Visual Communications and Image Processing, VCIP 2019
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 34th IEEE International Conference on Visual Communications and Image Processing, VCIP 2019
Y2 - 1 December 2019 through 4 December 2019
ER -