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

Zhao  Chun-Hui; Liu  Wei

doi:10.3724/SP.J.1146.2010.00400

Volume 33 Issue 2

Mar. 2011

Turn off MathJax

Article Contents

Article Navigation > Journal of Electronics & Information Technology > 2011 > 33(2): 461-465

Zhao Chun-Hui, Liu Wei. Image Multiple Description Coding Method Based on Interleaving Extraction and Block Compressive Sensing Strategy[J]. Journal of Electronics & Information Technology, 2011, 33(2): 461-465. doi: 10.3724/SP.J.1146.2010.00400

Citation:

Zhao Chun-Hui, Liu Wei. Image Multiple Description Coding Method Based on Interleaving Extraction and Block Compressive Sensing Strategy[J]. Journal of Electronics & Information Technology, 2011, 33(2): 461-465. doi: 10.3724/SP.J.1146.2010.00400

Citation:

PDF( 580 KB)

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

doi: 10.3724/SP.J.1146.2010.00400

Received Date: 2010-04-20
Rev Recd Date: 2010-10-07
Publish Date: 2011-02-19

Abstract

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

FullText(HTML)

References(1)

References

[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.

Relative Articles

Supplements(0)

Cited By

Proportional views