基于交织抽取与分块压缩感知策略的图像多描述编码方法

赵春晖; 刘巍

doi:10.3724/SP.J.1146.2010.00400

基于交织抽取与分块压缩感知策略的图像多描述编码方法

doi: 10.3724/SP.J.1146.2010.00400

赵春晖,
刘巍

基金项目:

国家自然科学基金(61077079)资助课题

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出版历程
- 收稿日期: 2010-04-20
- 修回日期: 2010-10-07
- 刊出日期: 2011-02-19

Image Multiple Description Coding Method Based on Interleaving Extraction and Block Compressive Sensing Strategy

摘要

摘要: 该文基于交织抽取和分块压缩感知(Interleaving Extraction and Block Compressive Sensing，IEBCS)理论，提出了一种可以在成像过程中实时实现的多描述编码方法(IEBCS-MDC)。首先利用交织抽取将图像划分成若干个子图像，然后对各个子图像进行分块压缩感知形成多个描述码流，接收端通过求解优化问题重建原图像。分块策略保证了观测过程的复杂程度不因图像尺寸而改变，所以该方法结构简单易于实现，适合处理高分辨率图像，另外特有的自恢复能力提升了算法的抗丢包性能。实验表明，在相同的硬件环境下，该文方法可以处理的图像尺寸远远大于CS-MDC方法，在同样的丢包率下重构质量也优于CS-MDC方法。
- 多描述编码 /
- 压缩感知 /
- 交织抽取 /
- 分块策略 /
- 自恢复
Abstract: Based on Interleaving Extraction and Block Compressive Sensing (IEBCS), a new Multiple Description Coding method (IEBCS-MDC) which can be achieved real-timely during imaging process is presented. The method is first partitions an image into several sub-images using interleaving extraction, then measures each sub-image with block compressive sensing and forms multiple descriptions. At the decoding terminal, the method reconstructs the original image by solving an optimization problem. Block strategy ensures that the complexity of measurement process does not change due to image size, so the method is simple and easy to implement, suitable for handling high-resolution images, and the characteristic self-recovery capability enhances the ability against packet loss. Experimental results show that, compared to CS-MDC, the proposed method can handle much bigger images in the same hardware environment and the reconstruction quality is also better than CS-MDC with the same packet loss probability.
- Multiple Description Coding (MDC) /
- Compressive Sensing (CS) /
- Interleaving extraction /
- Block strategy /
- Self-recovery

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参考文献(1)

[1] Goyal V K. Multiple description coding: compression meets the network [J].IEEE Signal Processing Magazine.2001, 18(5):74-93 [2] Donoho D. Compressed sensing [J].IEEE Transactions on Information Theory.2006, 52(4):1289-1306 [3] Cands E and Wakin M. An introduction to compressive sampling: a sensing/sampling paradigm that goes against the common knowledge in data acquisition [J].IEEE Signal Processing Magazine.2008, 25(2):21-30 [4] Baraniuk R. Compressive sensing [J]. IEEE Signal Processing Magazine, 2007, 24(4): 118-121. [5] 刘丹华, 石光明, 周佳社, 等. 基于Compressed Sensing框架的图像多描述编码方法[J]. 红外与毫米波学报, 2009, 28(4): 298-302. Liu Dan-hua, Shi Guang-ming, and Zhou Jia-she, et al.. New method of multiple description coding for image based on compressed sensing [J].Journal of Infrared and Millimeter. Waves.2009, 28(4):298-302 [6] Donoho D. For most large underdetermined systems of linear equations, the minimal ell-1 norm near-solution approximates the sparsest near-solution[J].Communications on Pure and Applied Mathematics.2006, 59(7):907-934 [7] Cands E and Romberg J. Quantitative robust uncertainty principles and optimally sparse decompositions [J].Foundation of Computational Mathematics.2006, 6(2):227-254 [8] Gan L. Block compressed sensing of natural images [C]. The 15th International Conference on Digital Signal Processing, Cardiff, UK, 2007: 403-406. [9] Duarte M, Davenport M, and Takhar D, et al.. Single-pixel imaging via compressive sampling [J].IEEE Signal Processing Magazine.2008, 25(2):83-91 [10] Donoho D, Tsaig Y, and Drori I, et al.. Sparse solution of underdetermined linear equations by stage wise orthogonal matching pursuit [R]. Tech. Report. 2006, Stanford, Department of Statistics, 2006.

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