Correlation Noise Modeling Algorithm Based on Multiple Probability Distributions for Distributed Video Coding

TANG Zhen-hua; LIANG Xiang-yan; QIN Tuan-fa; CHANG Kan

doi:10.3969/j.issn.0372-2112.2015.02.024

您当前的位置：

首页 >

文章列表页 >

Correlation Noise Modeling Algorithm Based on Multiple Probability Distributions for Distributed Video Coding

更新时间：2025-07-16

- Correlation Noise Modeling Algorithm Based on Multiple Probability Distributions for Distributed Video Coding
- Acta Electronica Sinica Vol. 43, Issue 2, Pages: 365-370(2015)
- 作者机构：
  
  广西大学计算机与电子信息学院,广西,南宁,530004
- 作者简介：
- 基金信息：
- DOI：10.3969/j.issn.0372-2112.2015.02.024
  CLC： TN919.8
- Published：2015
- 稿件说明：
移动端阅览
TANG Zhen-hua, LIANG Xiang-yan, QIN Tuan-fa, et al. Correlation Noise Modeling Algorithm Based on Multiple Probability Distributions for Distributed Video Coding[J]. Acta Electronica Sinica, 2015, 43(2): 365-370.
DOI：

TANG Zhen-hua, LIANG Xiang-yan, QIN Tuan-fa, et al. Correlation Noise Modeling Algorithm Based on Multiple Probability Distributions for Distributed Video Coding[J]. Acta Electronica Sinica, 2015, 43(2): 365-370. DOI： 10.3969/j.issn.0372-2112.2015.02.024.

摘要

相关噪声建模(Correlation Noise Modeling

CNM)的准确度是影响分布式视频编码(Distributed Video Coding

DVC)系统性能的关键因素之一.针对经过88离散余弦变换(Discrete Cosine Transform

DCT)后的相关噪声子带

本文提出了全新的基于多概率混合分布的CNM方法.该方法可根据子带的信息熵与子带在柯西、拉普拉斯和高斯三种概率分布下信息熵的相似度

自适应选择合适的概率分布对子带进行建模

提高了CNM的准确度.实验结果表明

采用本文提出的离线和在线CNM方法与现有典型的CNM方法相比

DVC系统的率失真(Rate-Distortion

R-D)性能均获得显著的提高.

Abstract

Accurate correlation noise modeling (CNM) is one of the key factors affecting the performance of distributed video coding (DVC).This paper presents a novel CNM algorithm based on multiple probability distributions for DVC

which mainly focuses on the subbands of correlation noise after 88 discrete cosine transform (DCT).The proposed CNM method can select the best suitable probability distribution to model the subbands of correlation noise adaptively by comparing the entropy of the subbands with the ones of the candidate probability distributions including Cauchy

Laplace and Gaussian.Experimental results show that compared with the existing typical CNM approaches

the proposed CNM method can improve the rate-distortion (R-D) performance of the DVC system significantly both in offline and online manner.

关键词

Keywords

references

Views

下载量

CSCD

Alert me when the article has been cited

提交

Tools

Publicity Resources

Fast PL-SLAM Algorithm Based on Improved Keyframe Extraction Strategy

An Improved Lion Swarm Algorithm Based on Information Entropy and Its Application in Combinatorial Optimization

Comparison of Various Types of Reductions in Inconsistent Systems

An Improved D-S Algorithm Under the New Measure Criteria of Evidence Conflict

Rate Estimation Algorithm for Distributed Video Coding on Decoder

Related Author

CHEN Meng-yuan

DING Ling-mei

ZHANG Yu-kun

GONG Xiang-yu

LI Yan-cang

LI Xiang-jun

HUANG Hou-kuan

DENG Da-yong

Related Institution

Key Laboratory of Advanced Perception and Intelligent Control of High‑end Equipment

School of Electrical Engineering, Anhui Polytechnic University

College of Civil Engineering, Hebei University of Engineering

School of Information EngineeringNanchang UniversityNanchangJiangxi 330029China

School of Computer and Information TechnologyBeijing Jiaotong UniversityBeijing 100044China

⁰