基于随机森林的HEVC复杂度控制方法

文鹏; 彭宗举; 陈芬; 蒋刚毅; 郁梅

doi:10.11959/j.issn.1000-0801.2019034

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基于随机森林的HEVC复杂度控制方法

研究与开发 | 更新时间：2024-06-05

- 基于随机森林的HEVC复杂度控制方法
- Complexity control method of random forest based HEVC
- 电信科学 2019年35卷第2期页码：14-26
- 作者机构：
- 作者简介：
  
  [ "文鹏（1994- ），男，宁波大学信息科学与工程学院硕士生，主要研究方向为三维视频信号编码。" ]
  [ "彭宗举（1973- ），男，博士，宁波大学信息科学与工程学院教授，主要研究方向为三维视频信号处理与编码。" ]
  [ "陈芬（1973- ），女，博士，宁波大学信息科学与工程学院教授，主要研究方向为三维视频信号处理与编码。" ]
  [ "蒋刚毅（1964- ），男，博士，宁波大学信息科学与工程学院教授，主要研究方向为三维视频信号处理与编码。" ]
  [ "郁梅（1968- ），女，博士，宁波大学信息科学与工程学院教授，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);国家自然科学基金资助项目;The National Natural Science Foundation of China(61671258);浙江省自然科学基金资助项目;The Natural Science Foundation of Zhejiang Province of China(LY17F010005);宁波市自然科学基金资助项目;The Natural Science Foundation of Ningbo of China(2018A610052)
- DOI：10.11959/j.issn.1000-0801.2019034
  中图分类号： TN919.8
- 网络出版日期：2019-02，
  
  纸质出版日期：2019-02-20
- 稿件说明：
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文鹏, 彭宗举, 陈芬, 等. 基于随机森林的HEVC复杂度控制方法[J]. 电信科学, 2019,35(2):14-26.

Peng WEN, Zongju PENG, Fen CHEN, et al. Complexity control method of random forest based HEVC[J]. Telecommunications science, 2019, 35(2): 14-26.
文鹏, 彭宗举, 陈芬, 等. 基于随机森林的HEVC复杂度控制方法[J]. 电信科学, 2019,35(2):14-26. DOI： 10.11959/j.issn.1000-0801.2019034.

Peng WEN, Zongju PENG, Fen CHEN, et al. Complexity control method of random forest based HEVC[J]. Telecommunications science, 2019, 35(2): 14-26. DOI： 10.11959/j.issn.1000-0801.2019034.

摘要

高效视频编码（high efficiency video coding，HEVC）具有较高的计算复杂度，而快速编码算法不能在给定编码时间下完成视频编码，因此提出了一种基于随机森林的HEVC复杂度控制方法。首先训练得到3种不同预测准确率的随机森林分类器，实现编码树单元（coding tree unit，CTU）的多种编码配置；然后，通过建立平均深度—复杂度模型为CTU分配复杂度；最后通过平坦度、平均深度、比特和CTU级累计编码误差确定的CTU编码配置完成复杂度控制。实验结果表明，本文算法复杂度控制精度较高，在率失真性能方面优于当前复杂度控制方法。

Abstract

High efficiency video coding (HEVC) has high computational complexity

and fast algorithm cannot perform video coding under restricted coding time.Therefore

a complexity control method of HEVC based on random forest was proposed.Firstly

three random forest classifiers with different prediction accuracy were trained to provide various coding configurations for coding tree unit (CTU).Then

an average depth-complexity model was built to allocate CTU complexity.Finally

the CTU coding configuration

determined by the smoothness

average depth

bit

and CTU-level accumulated coding error

was used to complete complexity control.The experimental results show that the proposed method has better complexity control precision

and outperforms the state-of-the-art method in terms of video quality.

关键词

Keywords

references

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