Reconstruction algorithm based on multi-reference frames hypothesis optimization for compressive sensing

Yong-hong KUO; Ru-quan WANG; Jian CHEN

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

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Reconstruction algorithm based on multi-reference frames hypothesis optimization for compressive sensing

Papers | 更新时间：2024-06-05

- Reconstruction algorithm based on multi-reference frames hypothesis optimization for compressive sensing
- Journal on Communications Vol. 38, Issue 12, Pages: 1-9(2017)
- 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：
- 基金信息：
  
  The National Natural Science Foundation of China(61771366);The 111 Project(B08038)
- DOI：10.11959/j.issn.1000-436x.2017297
  CLC： TP919.8
- Online First：2017-12，
  
  Published：25 December 2017
- 稿件说明：
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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：

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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