基于统计建模的HEVC快速率失真估计算法

孟翔; 殷海兵; 黄晓峰

doi:10.11959/j.issn.1000-0801.2021021

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基于统计建模的HEVC快速率失真估计算法

研究与开发 | 更新时间：2024-06-05

- 基于统计建模的HEVC快速率失真估计算法
- Statistical modeling based fast rate distortion estimation algorithm for HEVC
- 电信科学 2021年37卷第1期页码：58-68
- 作者机构：
- 作者简介：
  
  [ "孟翔（1996- ），男，杭州电子科技大学通信工程学院硕士生，主要研究方向为视频编解码。" ]
  [ "殷海兵（1974- ），男，杭州电子科技大学通信工程学院教授、在站博士后，主要研究方向为数字视频编解码、多媒体信号处理、芯片结构设计验证。" ]
  [ "黄晓峰（1988-），男，博士，杭州电子科技大学通信工程学院讲师，主要研究方向为数字视频编解码与芯片架构设计。" ]
- 基金信息：
  
  国家自然科学基金资助项目;The National Natural Science Foundation of China(61972123);国家自然科学基金资助项目;The National Natural Science Foundation of China(61901150);浙江省自然科学基金资助项目;Zhejiang Provincial Natural Science Foundation of China(LY19F020043);浙江省自然科学基金资助项目;Zhejiang Provincial Natural Science Foundation of China(Q19F010030)
- DOI：10.11959/j.issn.1000-0801.2021021
  中图分类号： TN919.81
- 网络出版日期：2021-01，
  
  纸质出版日期：2021-01-20
- 稿件说明：
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孟翔, 殷海兵, 黄晓峰. 基于统计建模的HEVC快速率失真估计算法[J]. 电信科学, 2021,37(1):58-68.

Xiang MENG, Haibing YIN, Xiaofeng HUANG. Statistical modeling based fast rate distortion estimation algorithm for HEVC[J]. Telecommunications science, 2021, 37(1): 58-68.
孟翔, 殷海兵, 黄晓峰. 基于统计建模的HEVC快速率失真估计算法[J]. 电信科学, 2021,37(1):58-68. DOI： 10.11959/j.issn.1000-0801.2021021.

Xiang MENG, Haibing YIN, Xiaofeng HUANG. Statistical modeling based fast rate distortion estimation algorithm for HEVC[J]. Telecommunications science, 2021, 37(1): 58-68. DOI： 10.11959/j.issn.1000-0801.2021021.

摘要

针对高效视频编码（HEVC）率失真优化过程中复杂的码率与失真计算，提出一种高准确性的快速率失真估计模型。该算法评估熵编码中不同语法元素的贡献百分比，并逐个开发语法元素的码率模型以构建整个 TU 级模型。首先，对系数进行自适应加权，以区分不同系数对码率的不均匀贡献。然后，定义位置参数以描述一个块内非零系数可能的分布模式。通过统计方式对大量样本的模型参数进行微调，得出最终线性码率模型。最后，建立了变换域失真模型来省略不必要的重构过程。实验结果表明，所提算法在 RA 配置下，可以实现49.76％的复杂度降低，而BD-BR仅增加1.74％。

Abstract

To reduce the coding computational complexity of the rate and distortion calculations in HEVC

a fast TU level rate model with higher accuracy was proposed.The contribution percentages of different syntax elements in entropy coding were evaluated

and syntax element-wise accurate rate models were individually developed to construct the whole TU level model.In addition

coefficient levels were weighted adaptively to distinguish the nonuniform contributions of different coefficients in terms of rate profiling.Moreover

position-wise parameter was defined to depict the distribution patterns for possible non-zero coefficients within one block.The final linear rate model was developed by fine-tuning the model parameters from great amounts of samples in a statistical way.Finally

the transform domain distortion model was also established to bypass unnecessary reconstruction process.Experimental results show that the proposed algorithm can achieve 49.76% complexity reduction with 1.74% BD-BR increase for RA configuration.

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references

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