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http://dx.doi.org/10.5909/JBE.2015.20.3.398

Rate Allocation for Block-based Compressive Sensing  

Nguyen, Quang Hong (Sungkyunkwan University, College of Information & Communication Enginerring)
Dinh, Khanh Quoc (Sungkyunkwan University, College of Information & Communication Enginerring)
Nguyena, Viet Anh (Sungkyunkwan University, College of Information & Communication Enginerring)
Trinh, Chien Van (Sungkyunkwan University, College of Information & Communication Enginerring)
Park, Younghyeon (Sungkyunkwan University, College of Information & Communication Enginerring)
Jeon, Byeungwoo (Sungkyunkwan University, College of Information & Communication Enginerring)
Publication Information
Journal of Broadcast Engineering / v.20, no.3, 2015 , pp. 398-407 More about this Journal
Abstract
Compressive sensing (CS) has drawn much interest as a novel sampling technique that enables sparse signal to be sampled under the Nyquitst/Shannon rate. By noting that the block-based CS can still keep spatial correlation in measurement domain, this paper proposes to adapt sampling rate of each block in frame according to its characteristic defined by edge information. Specifically, those blocks containing more edges are assigned more measurements utilizing block-wise correlation in measurement domain without knowledge about full sampling frame. For natural image, the proposed adaptive rate allocation shows considerable improvement compared with fixed subrate block-based CS in both terms of objective (up to 3.29 dB gain) and subjective qualities.
Keywords
Block-based Compressive Sensing; Adaptive Measurement Rate; Edge Detection;
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Times Cited By KSCI : 3  (Citation Analysis)
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1 H. W. Chen, L. W. Kang, and C. S. Lu, "Dynamic measurement rate allocation for distributed compressive video sensing," in Proc. SPIE Visual Communication and Image Processing, pp. 1-10, July 2010.
2 J. P. Nebot, Y. Ma, and T. Huang, “Distributed Video Coding Using Compressive Sampling,” in Proc. Picture Coding Symposium, pp. 1-4, May 2009.
3 M. Azghani, A. Aghagolzadeh, and M. Aghagolzadeh, "Compressed Video Sensing Using Adaptive Sampling Rate," in Proceeding of International Symposium on Telecommunications, pp. 710-714, Dec. 2010.
4 Z, Hai-bo and Z. Xiu-chang, "Sampling Adaptive Block Compressed Sensing Reconstruction Algorithm for Images Based on Edge Detection," The Journal of China Universities of Post sand Telecommunications, vol. 20, pp. 97-103, June 2013.
5 R. Baraniuk, "Compressed sensing," IEEE Signal Processing Magazine, vol. 24, pp. 118-121, July 2007.   DOI   ScienceOn
6 E. Candes and T. Tao, "Decoding by linear programming," IEEE Trans. Information Theory, vol. 51, pp. 4203-4215, Dec. 2005.   DOI   ScienceOn
7 M. A. Davenport, "Random Observation on Random Observation: Sparse Signal Acquisition and Processing," PhD. Thesis, Rice University, Aug. 2010.
8 S. Marcia and Z. Zhang, "Matching pursuits with time-frequency dictionaries," IEEE Trans. Signal Processing, vol. 41, pp. 3397-4415, Dec. 1993.   DOI   ScienceOn
9 J. Tropp and A. C. Gilbert, "Signal recovery from random measurement via orthogonal marching pursuit," IEEE Trans. Inform. Theory, vol. 53, pp. 4655-4666, Dec. 2007.   DOI   ScienceOn
10 S. Mun and J. E. Fowler, “DPCM for quantized block-based compressive sensing of image,” Proceedings of the 20th European Signal Processing Conference, pp. 1424-1328, Aug. 2012.
11 S. Kwon and B. Shim, "Multiple Candidate Matching Pursuit," Journal of Broadcasting Engineering, vol. 17, pp. 954-963, Nov. 2012.   DOI   ScienceOn
12 S. Mun and J. E. Fowler, "Block Compressive Sensing of Images Using Directional Transforms," in Proceedings of the International Conference of Image Processing, pp. 3021-3024, Nov. 2009.
13 R. G. Keys, "Cubic Convolution Interpolation for Digital Image Processing," IEEE Trans. on Acoustics, Speech, and Signal Processing, vol. 29, pp. 1153-1160, Dec. 1981.   DOI
14 Y. Park, H. J. Shim and B. Jeon, "Convergence Complexity Reduction for Block-based Compressive Sensing Reconstruction," Journal of Broadcasting Engineering , vol. 19, pp. 240-249, Mar. 2014.   DOI   ScienceOn
15 K. Q. Dinh, H. J. Shim, and B. Jeon, "Measurement Coding for Compressive Imaging Using a Structural Measurement Matrix," in Proceeding of IEEE Int. Conf. on Image Proc., pp. 10-13, Sept. 2013.
16 Q. H. Nguyen, K. Q. Dinh, V. A. Nguyen, C. V. Trinh, "A Skip-mode Coding for Distributed Compressive Video Sensing," Journal of Broadcasting Engineering, vol. 19, pp. 257-267, Mar. 2014.   DOI   ScienceOn
17 Y. Wang and W. Yin, "Sparse Signal Reconstruction via Iterative Support Detection," SIAM Journal on Imaging Sciences, vol. 3, pp. 462-491, July 2010.   DOI
18 G. H. Mohimani, M. Babaie-Zadeh, and C. Jutten, "A Fast Approach for Overcomplete Sparse Decomposition Based on Smoothed L0 Norm," IEEE Trans. on Signal Processing, vol. 57, pp. 289-301, Jan. 2009.   DOI   ScienceOn
19 C. Li, "Compressive Sensing for 3D Data Processing, Tasks: Application, Models and Algorithms," PhD. Thesis, Rice University, Apr. 2011.
20 R. Maini and H. Aggarwal, "Study and Comparison of Various Image Edge Detection Technique," International Journal of Image Processing, vol. 3, pp. 1-12, Jan. 2009.   DOI   ScienceOn