基于多参考帧假设优化的压缩感知重构算法

阔永红; 王薷泉; 陈健

doi:10.11959/j.issn.1000-436x.2017297

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基于多参考帧假设优化的压缩感知重构算法

学术论文 | 更新时间：2024-06-05

- 基于多参考帧假设优化的压缩感知重构算法
- Reconstruction algorithm based on multi-reference frames hypothesis optimization for compressive sensing
- 通信学报 2017年38卷第12期页码：1-9
- 作者机构：
  
  西安电子科技大学通信工程学院，陕西西安 710071
- 作者简介：
  
  [ "阔永红（1967-），女，陕西宝鸡人，西安电子科技大学教授，主要研究方向为信号处理、认知无线电、无线传感器网络等。" ]
  [ "王薷泉（1991-），男，陕西西安人，西安电子科技大学博士生，主要研究方向为视频编解码、压缩感知等。" ]
  [ "陈健（1968-），男，江苏如东人，西安电子科技大学教授、博士生导师，主要研究方向为认知无线电、OFDM、无线传感器网络等。" ]
- 基金信息：
  
  国家自然科学基金资助项目(61771366);高等学校引智计划基金资助项目(B08038)
- DOI：10.11959/j.issn.1000-436x.2017297
  中图分类号： TP919.8
- 网络首发：2017-12，
  
  纸质出版：2017-12-25
- 稿件说明：
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阔永红, 王薷泉, 陈健. 基于多参考帧假设优化的压缩感知重构算法[J]. 通信学报, 2017,38(12):1-9.

Yong-hong KUO, Ru-quan WANG, Jian CHEN. Reconstruction algorithm based on multi-reference frames hypothesis optimization for compressive sensing[J]. Journal on Communications, 2017, 38(12): 1-9.
阔永红, 王薷泉, 陈健. 基于多参考帧假设优化的压缩感知重构算法[J]. 通信学报, 2017,38(12):1-9. DOI： 10.11959/j.issn.1000-436x.2017297.

Yong-hong KUO, Ru-quan WANG, Jian CHEN. Reconstruction algorithm based on multi-reference frames hypothesis optimization for compressive sensing[J]. Journal on Communications, 2017, 38(12): 1-9. DOI： 10.11959/j.issn.1000-436x.2017297.

摘要

在多假设分布式压缩视频感知系统中，多假设的质量对重构性能意义重大。现有工作中，对于多假设集合获取的研究并未得到关注。提出一种多参考帧假设集合优化选择（MRHO）算法，增加参考帧数目以扩大假设选择范围，通过假设优化选择，在相同假设集合尺寸下提高了集合质量。仿真表明，MRHO算法有效提高了视频重构质量。

Abstract

In multi-hypothesis based distributed compressed video sensing systems

the quality of the multi-hypothesis set has important influence on the reconstruction performance of decoder.However

the acquiring of the hypothesis set has not been concerned in existing works.A reconstruction algorithm based on multi-reference frames hypothesis optimization (MRHO) was proposed.This algorithm expanded the selection of hypothesis vectors by increasing the number of reference frames.The quality of the prediction set was improved by hypotheses optimization selection under the same size with the original hypothesis set.Simulation results show that the proposed MRHO algorithm effectively improves the reconstructed quality of the distributed compressed video sensing scheme.

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references

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