混合观测压缩感知图像多描述编码

王良君; 石光明; 李甫; 史思琦

doi:10.3788/OPE.20132103.0724

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混合观测压缩感知图像多描述编码

信息科学 | 更新时间：2020-08-12

- 混合观测压缩感知图像多描述编码
- Compressive Sensing Multiple Description Image Coding with Hybrid Sampling
- 光学精密工程 2013年21卷第3期页码：724-733
- 作者机构：
  
  西安电子科技大学电子工程学院2. 西安电子科技大学
- 作者简介：
- 基金信息：
  
  大尺寸有障碍空间角度与基面位置测量的关键技术()
- DOI：10.3788/OPE.20132103.0724
  中图分类号： TP391.4
- 收稿日期：2012-09-28，
  
  修回日期：2012-12-03，
  
  网络出版日期：2013-03-20，
  
  纸质出版日期：2013-03-15
- 稿件说明：
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王良君石光明李甫史思琦. 混合观测压缩感知图像多描述编码[J]. 光学精密工程, 2013,21(3): 724-733

WANG Liang-jun SHI Guang-ming LI Fu SHI Si-qi. Compressive Sensing Multiple Description Image Coding with Hybrid Sampling[J]. Editorial Office of Optics and Precision Engineering, 2013,21(3): 724-733
王良君石光明李甫史思琦. 混合观测压缩感知图像多描述编码[J]. 光学精密工程, 2013,21(3): 724-733 DOI： 10.3788/OPE.20132103.0724.

WANG Liang-jun SHI Guang-ming LI Fu SHI Si-qi. Compressive Sensing Multiple Description Image Coding with Hybrid Sampling[J]. Editorial Office of Optics and Precision Engineering, 2013,21(3): 724-733 DOI： 10.3788/OPE.20132103.0724.

摘要

提出一种混合观测压缩感知多描述编码方案，用于提升传统的该类编码系统的编码性能并保留其抗丢包能力。该方案采用二维离散余弦变换（DCT）观测矩阵和高斯矩阵分别对图像信号进行观测

并分别使用哥伦布码(Golomb code)及其改进的编码方案对两种观测系数进行熵编码，得到包含完整码字的二维DCT码流和仅包含部分码字的高斯观测系数码流。在解码端，利用二维DCT系数和高斯观测系数之间的相关性进行最大后验概率估计解码，成功估计出高斯观测系数的缺失码字。最后再将两种观测系数合并，采用1范数优化算法重构出原信号。针对自然图像和遥感图像的实验均表明：不同丢包情况下，用本文提出的编码方案获得的重构图像的峰值信噪比（PSNR）值比传统高斯观测压缩感知编码方案提高了2~4dB，该方案同时还具有鲁棒的抗丢包能力。

Abstract

A Compressive Sensing(CS) multiple description coding scheme with hybrid sampling was proposed to improve the coding efficiency of the traditional CS coding system and to maintain the ability of resisting packet loss. In the scheme

both 2-D Discrete Cosine Transformation( DCT) matrix and sub-Gaussian matrix were used to measure the image signal simultaneously. Then

a Golomb code and its improved version were used to encode for the resulted measurements

respectively. As a result

the 2-D DCT measurement bit streams with complete code words and the Gaussian measurement bit streams with incomplete code words were obtained respectively. In the decoder

these incomplete code words could be decoded successfully with a Maximum A posteriori Probability (MAP) estimator

and the deficient code words could be estimated by the relevance between 2-D DCT and Gaussian measurements. Finally

these decoded measurements were grouped together again to reconstruct the image signal by solving a 1-norm optimization problem. Experimental results on both natural and remote sensing images show that the Peak Signal to Noise Ratio(PSNRs) of the images reconstructed by proposed method can be superior to that of traditional CS coding scheme by 2~4 dB at different packet loss rates

meanwhile

it has a robust resisting packet loss ability.

关键词

Keywords

references

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