Image sequence compressed sensing by minimizing prediction errors

SHI Wen-xuan; LI Jie

doi:10.3788/OPE.20122009.2095

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Image sequence compressed sensing by minimizing prediction errors

Article | 更新时间：2020-08-12

- Image sequence compressed sensing by minimizing prediction errors
- Optics and Precision Engineering Vol. 20, Issue 9, Pages: 2095-2102(2012)
- 作者机构：
  
  武汉大学电子信息学院,湖北武汉,中国,430072
- 作者简介：
- 基金信息：
- DOI：10.3788/OPE.20122009.2095
  CLC： TP751
- Received：04 May 2012，
  
  Revised：02 July 2012，
  
  Published：10 September 2012
- 稿件说明：
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石文轩, 李婕. 最小化预测残差的图像序列压缩感知[J]. 光学精密工程, 2012,20(9): 2095-2102

SHI Wen-xuan, LI Jie. Image sequence compressed sensing by minimizing prediction errors[J]. Editorial Office of Optics and Precision Engineering, 2012,20(9): 2095-2102
石文轩, 李婕. 最小化预测残差的图像序列压缩感知[J]. 光学精密工程, 2012,20(9): 2095-2102 DOI： 10.3788/OPE.20122009.2095.

SHI Wen-xuan, LI Jie. Image sequence compressed sensing by minimizing prediction errors[J]. Editorial Office of Optics and Precision Engineering, 2012,20(9): 2095-2102 DOI： 10.3788/OPE.20122009.2095.

摘要

提出了一种最小化预测残差的图像序列压缩感知算法以实现高速相机输出图像的实时压缩。首先

在编码端仅使用映射矩阵对原始输出图像进行压缩

将压缩得到的观测向量通过信道传输到解码端。接着

在解码端对相邻帧进行运动估计和运动补偿

得到一幅待重建图像的预测图像

利用压缩感知算法对原始图像和预测图像之间存在的预测残差图像进行重建。最后

用迭代的方法优化预测残差图像的重建结果

直到连续两次的重建结果之差小于设定阈值

从而获得重建的原始图像。采用DALSA公司的CR-GEN0-H6400相机进行的实验表明

该算法可以实现1 000 frame/s图像的实时压缩

并且图像重建质量比独立地重建每张图像至少提高了2~6 dB

有效地实现了对高速相机输出图像的实时压缩与高质量重建。

Abstract

An image sequence compressed sensing algorithm by minimizing prediction errors was proposed for high speed camera image compression in real-time. First

an original image was compressed only by a projection matrix on the encoder side. The observed vector obtained by compressing was transferred to the decoder through a channel. Then

motion estimation and motion compensation were performed on correlated images on the decoder side

and a prediction image was generated in this way. Furthermore

the prediction error image which is the difference between original image and prediction image was reconstructed by compressed sensing. Finally

the reconstruction of prediction error image was improved by an iterative procedure

until the difference between two consecutive reconstruction results was smaller than a predetermined threshold. Therefore

the original image was reconstructed by the prediction error image. Experiments by CR-GEN0-H6400 camera from DALSA indicate that the proposd algorithm can compress 1 000 frame/s images in real-time

and image reconstruction result is improved by 2-6 dB at least as compared with that of independent reconstruction. The proposed algorithm can compress high speed camera images in real-time

and can reconstruct the images in high quality.

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Keywords

references

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