基于学习模型的3D-HEVC提前Merge模式终止算法

doi:10.11959/j.issn.1000-436x.2019165

Abstract

Abstract:

3D-high efficiency video coding (3D-HEVC) standard is an extension of HEVC.Though 3D-HEVC effectively improves the compression efficiency of 3D video,it also brings huge computational complexity.To greatly reduce the 3D-HEVC coding complexity,an early Merge mode decision approach was proposed.The residual signal that encoded by the Merge mode was firstly extracted as feature information.A learning model was established in terms of the residual signals of the coding unit (CU) in current frame that used early Merge mode as the optimal mode.Finally,the residual signal was extracted for the Merge mode of current CU,and the learning model was used to predict whether the Merge mode was the optimal mode or not.Experimental results show that the proposed early Merge mode decision approach reduces the coding times of 3D-HEVC texture views and depth maps about 41.9% and 24.3%,respectively,and the coding performance degradation is almost negligible.Compared with existing early Merge mode decision approaches,the proposed approach further reduces the coding time,and can be easily integrated into the 3D-HEVC test model due to its design simplicity.

Key words: 3D-high efficiency video coding, early Merge mode decision, residual signal, learning model

CLC Number:

TN919.8

Yue LI,Gaobo YANG,Xiangling DING,Yapei ZHU. Early Merge mode decision algorithm for 3D-HEVC based on learning model[J]. Journal on Communications, 2019, 40(7): 104-113.

Figures/Tables 11

References 28

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视频序列	分辨率	帧率/(frame.s^-1)	独立视点＆非独立视点
Balloons	1 024×768	30	V₃＆V₁,V₅
Kendo	1 024×768	30	V₃＆V₁,V₅
Newspaper	1 024×768	30	V₄＆V₂,V₆
GT_Fly	1 920×1 088	25	V₅＆V₁,V₉
Poznan_Hall2	1 920×1 088	25	V₆＆ V₅,V₇
Poznan_Street	1 920×1 088	25	V₄＆ V₃,V₅
Undo_Dancer	1 920×1 088	25	V₅＆V₁,V₉
Shark	1 920×1 088	30	V₅＆V₁,V₉

视频序列	video₀	video₁	video₂	V/V	V/T	S/T	TS_texture	TS_depth	TS_total
Balloons	0.5%	0.8%	0.8%	0.6%	0.6%	0.7%	39.3%	21.3%	32.1%
Kendo	0.6%	0.9%	0.5%	0.7%	0.6%	0.7%	37.1%	18.2%	29.2%
Newspaper	0.5%	0.5%	0.3%	0.5%	0.3%	0.7%	44.1%	21.3%	33.9%
GT_Fly	1.8%	1.9%	2.1%	1.9%	1.8%	1.8%	48.7%	26.9%	39.7%
Poznan_Hall2	0.6%	0.8%	0.9%	1.0%	0.9%	0.8%	50.1%	23.1%	38.6%
Poznan_Street	0.2%	0.7%	0.3%	0.6%	0.7%	0.4%	47.5%	26.5%	38.1%
Undo_Dancer	1.0%	0.7%	0.6%	1.1%	0.9%	1.2%	37.3%	30.6%	34.6%
Shark	0.2%	-0.1%	-0.1%	0.2%	-0.1%	0.4%	31.2%	26.4%	28.9%
平均值	0.7%	0.8%	0.7%	0.8%	0.7%	0.8%	41.9%	24.3%	34.4%

视频序列	video₀	video₁	video₂	V/V	V/T	S/T	TS_texture	TS_depth	TS_total
alloons	0.1%	1.1%	1.2%	0.6%	0.5%	0.6%	20.8%	11.3%	17.0%
Kendo	0.2%	1.9%	1.8%	0.9%	0.8%	0.7%	19.6%	8.9%	15.1%
Newspaper	0.1%	2.9%	0.8%	0.9%	0.6%	1.0%	30.0%	10.2%	21.2%
GT_Fly	0.7%	-1.0%	-0.8%	0.8%	0.6%	0.4%	20.6%	12.0%	23.0%
Poznan_Hall2	0.1%	1.4%	3.1%	0.9%	0.8%	0.8%	26.9%	11.4%	20.2%
Poznan_Street	0.1%	0.4%	0.5%	0.4%	0.3%	0.4%	33.0%	13.2%	24.2%
Undo_Dancer	0.4%	-0.8%	-0.9%	0.5%	0.3%	0.2%	26.2%	16.8%	22.5%
Shark	0.3%	-1.7%	-1.5%	0.2%	0.0%	0.2%	28.8%	11.9%	21.5%
平均值	0.3%	0.5%	0.5%	0.7%	0.5%	0.5%	25.7%	12.0%	20.6%

视频序列	video₀	video₁	video₂	V/V	V/T	S/T	TS_texture	TS_depth	TS_total
Balloons	0.5%	1.0%	0.9%	0.7%	0.7%	0.5%	36.3%	6.0%	24.5%
Kendo	0.5%	1.2%	0.9%	0.8%	0.8%	0.5%	35.2%	3.7%	22.4%
Newspaper	0.3%	0.4%	0.6%	0.4%	0.4%	0.3%	37.9%	5.1%	23.6%
GT_Fly	1.5%	2.6%	2.8%	2.0%	1.9%	1.6%	37.0%	6.1%	24.6%
Poznan_Hall2	0.6%	0.8%	0.6%	0.7%	0.7%	0.6%	33.1%	4.1%	20.9%
Poznan_Street	0.2%	0.9%	1.3%	0.6%	0.6%	0.4%	34.2%	5.7%	21.8%
Undo_Dancer	0.7%	4.1%	4.7%	2.0%	1.9%	1.3%	32.4%	9.1%	23.5%
Shark	0.7%	1.6%	1.8%	1.1%	1.0%	0.8%	31.2%	5.6%	20.3%
平均值	0.6%	1.6%	1.7%	1.0%	1.0%	0.8%	34.6%	5.7%	22.7%

视频序列	本文提出的方法		文献[16]方法		文献[17]方法
视频序列	S/T	TS_Depth	S/T	TS_Depth	S/T	TS_depth
Balloons	0.1%	33.6%	2.4%	41.5%	0.8%	16.3%
Kendo	0.1%	28.4%	4.0%	35.5%	0.3%	16.2%
Newspaper	0.3%	32.4%	2.0%	45.0%	0.7%	15.2%
GT_Fly	0.1%	39.4%	1.6%	42.1%	—	—
Poznan_Hall2	0.2%	40.0%	0.9%	44.8%	-0.6%	17.3%
Poznan_Street	0.1%	40.0%	2.1%	47.2%	0.9%	17.0%
Undo_Dancer	0.4%	44.8%	2.6%	48.5%	—	—
Shark	0.2%	39.0%	3.0%	31.6%	0%	16.5%
平均值	0.2%	37.2%	2.3%	42.0%	0.4%	16.4%

Early Merge mode decision algorithm for 3D-HEVC based on learning model

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