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基于深度学习的快速QTMT划分
Fast QTMT partition decision based on deep learning
- 电信科学 2021年37卷第4期 页码：73-81
- 作者机构：
1. 宁波大学信息科学与工程学院，浙江 宁波 315211
2. 重庆理工大学电气与电子学院，重庆 400054
- 作者简介：
[ "彭双（1995- ），男，宁波大学信息科学与工程学院硕士生，主要研究方向为视频信号编码" ]
[ "王晓东（1972- ），男，宁波大学信息科学与工程学院教授，主要研究方向为视频信号处理与通信" ]
[ "彭宗举（1973- ），男，博士，重庆理工大学电气与电子学院教授，主要研究方向为三维视频信号处理与编码" ]
[ "陈芬（1973- ），女，博士，重庆理工大学电气与电子学院教授，主要研究方向为三维视频信号处理与编码" ]
- 基金信息：国家自然科学基金资助项目;The National Natural Science Foundation of China(61771269);国家自然科学基金资助项目;The National Natural Science Foundation of China(61620106012);浙江省自然科学基金资助项目;Zhejiang Provincial Natural Science Foundation of China(LY20F010005);宁波市自然科学基金资助项目;The Natural Science Foundation of Ningbo(2019A610107);重庆理工大学科研启动基金资助项目;The Scientific Research Foundation of Chongqing University of Technology(2020ZDZ029);重庆理工大学科研启动基金资助项目;The Scientific Research Foundation of Chongqing University of Technology(2020ZDZ030)
DOI：10.11959/j.issn.1000-0801.2021062
中图分类号： TP393-
网络出版日期：2021-04，
纸质出版日期：2021-04-20
- 稿件说明：
移动端阅览
彭双, 王晓东, 彭宗举, 等. 基于深度学习的快速QTMT划分[J]. 电信科学, 2021,37(4):73-81.
Shuang PENG, Xiaodong WANG, Zongju PENG, et al. Fast QTMT partition decision based on deep learning[J]. Telecommunications science, 2021, 37(4): 73-81.
彭双, 王晓东, 彭宗举, 等. 基于深度学习的快速QTMT划分[J]. 电信科学, 2021,37(4):73-81. DOI： 10.11959/j.issn.1000-0801.2021062.
Shuang PENG, Xiaodong WANG, Zongju PENG, et al. Fast QTMT partition decision based on deep learning[J]. Telecommunications science, 2021, 37(4): 73-81. DOI： 10.11959/j.issn.1000-0801.2021062.
摘要
与之前的编码标准相比，多功能视频编码（versatile video coding，VVC）进一步提高了压缩效率。嵌套多类树的四叉树（quadtree with nested multi-type tree，QTMT）结构是提高编码增益的关键之一，同时极大地增加了编码复杂度。为降低VVC编码复杂度，提出了一种基于深度学习的快速QTMT划分方法。首先，提出了注意力-非对称卷积结构来预测划分模式的概率。然后，基于阈值提出了快速划分模式决策。最后，提出了编码性能与时间的代价函数来求解最优阈值，提出了阈值决策方法。实验表明，算法在不同档次下的时间节省分别为48.62%、52.93%、62.01%，BDBR分别为1.05%、1.33%、2.38%。结果表明，算法的时间节省和率失真性能优于其他快速算法。
Abstract
Compared with the predecessor standards
versatile video coding (VVC) significantly improves compression efficiency by a quadtree with nested multi-type tree (QTMT) structure but at the expense of extremely high coding complexity.To reduce the coding complexity of VVC
a fast QTMT partition method was proposed based on deep learning.Firstly
an attention-asymmetric convolutional neural network was proposed to predict the probability of partition modes.Then
the fast decision of partition modes based on the threshold was proposed.Finally
the cost of coding performance and time was proposed to obtain the optimal threshold
and the threshold decision method was proposed.Experimental results at different levels show that the proposed method achieves an average time saving of 48.62%/52.93%/62.01% with the negligible BDBR of 1.05%/1.33%/2.38%.Such results demonstrate that the proposed method significantly outperforms other state-of-the-art methods.
关键词
Keywords
references
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