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http://dx.doi.org/10.7840/kics.2014.39A.12.746

Block-Based Transform-Domain Measurement Coding for Compressive Sensing of Images  

Nguyen, Quang Hong (Sungkyunkwan Univ. School of Electronic and Electrical Engineering)
Nguyen, Viet Anh (Sungkyunkwan Univ. School of Electronic and Electrical Engineering)
Trinh, Chien Van (Sungkyunkwan Univ. School of Electronic and Electrical Engineering)
Dinh, Khanh Quoc (Sungkyunkwan Univ. School of Electronic and Electrical Engineering)
Park, Younghyeon (Sungkyunkwan Univ. School of Electronic and Electrical Engineering)
Jeon, Byeungwoo (Sungkyunkwan Univ. School of Electronic and Electrical Engineering)
Publication Information
The Journal of Korean Institute of Communications and Information Sciences / v.39A, no.12, 2014 , pp. 746-755 More about this Journal
Abstract
Compressive sensing (CS) has drawn much interest as a new sampling technique that enables signals to be sampled at a much lower than the Nyquist rate. By noting that the block-based compressive sensing can still keep spatial correlation in measurement domain, in this paper, we propose a novel encoding technique for measurement data obtained in the block-based CS of natural image. We apply discrete wavelet transform (DWT) to decorrelate CS measurements and then assign a proper quantization scheme to those DWT coefficients. Thus, redundancy of CS measurements and bitrate of system are reduced remarkably. Experimental results show improvements in rate-distortion performance by the proposed method against two existing methods of scalar quantization (SQ) and differential pulse-code modulation (DPCM). In the best case, the proposed method gains up to 4 dB, 0.9 dB, and 2.5 dB compared with the Block-based CS-Smoothed Projected Landweber plus SQ, Block-based CS-Smoothed Projected Landweber plus DPCM, and Multihypothesis Block-based CS-Smoothed Projected Landweber plus DPCM, respectively.
Keywords
Compressive Sensing; Measurement Coding; Image Compression;
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Times Cited By KSCI : 4  (Citation Analysis)
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1 E. J. Candes and T. Tao, "Decoding by linear programming," IEEE Trans. Inf. Theory, vol. 51, no. 12, pp. 4203-4215, Dec. 2005.   DOI   ScienceOn
2 M. A. Davenport, "Random observations on random observations: sparse signal acquisition and processing," Ph.D. Thesis, Rice Univ., Aug. 2010.
3 H.-W. Chen, L.-W. Kang, and C.-S. Lu, "Dynamic measurement rate allocation for distributed compressive video sensing," in Proc. Visual Commun. Image Process., pp. 1-10, Jul. 2010.
4 J. J. Y. Huang and P. M. Schultheiss, "Block quantization of correlated gaussian random variable," IEEE Trans. Commun. Syst., vol. 11, no. 3, pp. 289-296, Sept. 1963.   DOI
5 H. M Parmar, "Comparison of DCT and wavelet based image compression techniques," Int. J. Eng. Develop. Res., vol. 2, no. 1, pp. 664-669, Mar. 2014.
6 P. Telagarapu, V. J. Naveen, A. L. Prasanthi, and G. V. Santhi, "Image compression using DCT and wavelet transformations," Int. J. Signal Process., Image Process. and Pattern Recognition, vol. 4, no. 3, pp. 61-74, Sept. 2011.
7 M. Kociolek, A. Materka, M. Strzelecki, and P. Szczypinski, "Discrete wavelet transformderiver features for digital image texture analysis," in Proc. Int. Conf. Signal and Electronic Syst., pp. 163-168, Sept. 2001.
8 S. Mun and J. E. Fowler, "Block compressed sensing of images using directional transforms," IEEE Int. Conf. Image Process., pp. 3021-3024, Nov. 2009.
9 C. Chen, E. W. Tramel, and J. E. Fowler, "Compressed-sensing recovery of image and video using multihypothesis predictions," in Proc. Asilomar Conf. Signals, Syst. Comput., pp. 1193-1198, Nov. 2011.
10 A. Schulz, L. Velho, and E. A. B. da Silva, "On the empirical rate-distortion performance of compressive sensing," in Proc. IEEE Int. Conf. Image Process., pp. 3049-3052, Nov. 2009.
11 H. Murakami and H. Yamamoto, "Theoretical comparison between DPCM and transform coding regarding the robustness of coding performance for variation of picture statistics," IEEE Trans. Commun., vol. 32, no. 12, pp. 1351-1358, Dec. 1984.   DOI
12 G. K. Wallace, "The JPEG still picture compression standard," IEEE Trans. Consumer Electron., vol. 38, no. 1, pp. 18-34, Feb. 1992.
13 S. Yang, H. J. Shim, D. Lee, and B. Jeon, "Transform skip mode fast decision method for HEVC encoding," J. KICS, vol. 39, no. 4, pp. 172-179, Apr. 2014.   과학기술학회마을   DOI
14 C. Christopoulos, A. Skodras, and T. Ebrahimi, "The JPEG 2000 still image coding system: An overview," IEEE Trans. Consumer Electron., vol. 46, no. 4, pp. 1103-1127, Nov. 2000.   DOI   ScienceOn
15 G. Gankhuyag, E. G. Hong, G. Kim, Y. Kim, and Y. Choe, "A real-time video stitching algorithm in H. 264/AVC compressed domain," J. KICS, vol. 39, no. 6, pp. 503-511, Jun. 2014.   과학기술학회마을   DOI
16 G. J. Sullivan, J.-R. Ohm, W-J. Han, and T. Wiegand, "Overview of the high efficiency video coding (HEVC) standard," IEEE Trans. Circuits Syst. Video Technol., vol. 22, no. 12, pp. 1649-1668, Dec. 2012.   DOI   ScienceOn
17 J.-A. Choi and Y.-S. Ho, "Intra mode coding using candidate mode table in HEVC," J. KICS, vol. 37, no. 3, pp. 157-162, Mar. 2012.   과학기술학회마을   DOI
18 R. Baraniuk, "Compressed sensing," IEEE Signal Process. Mag., vol. 24, no. 4, pp. 118-124, Jul. 2007.
19 H.-H. Baek, J.-W. Kang, K.-S. Kim, and H.-N. Lee, "Introduction and performance analysis of approximate message passing (AMP) for compressed sensing signal recovery," J. KICS, vol. 38, no. 11, pp. 1029-1043, Nov. 2013.   과학기술학회마을   DOI
20 B. K. Natarajan, "Sparse approximate solution to linear system," Soc. Ind. Appl. Mathematics J. Comput., vol. 24, no. 2, pp. 227-234, Apr. 1995.
21 T. Wiegand, G. J. Sullivan, G. Bjontegaard, and A. Luthra, "Overview of the H.264/AVC video coding standard," IEEE Trans. Circuits Syst. Video Technol., vol. 13, no. 7, pp. 560-576, Jul. 2003.   DOI   ScienceOn
22 W. Dai, H. V. Pham, and O. Milenkovic, "Distortion-rate functions for quantized compressive sensing," IEEE Inf. Theory Workshop Netw. Inf. Theory, pp. 171-175, Jun. 2009.
23 J. N. Laska, P. T. Boufounos, M. A. Davenport, and R. G. Baraniuk, "Democracy in action: Quantization, saturation, and compressive sensing," Applied and Computational Harmonic Analysis, vol. 31, no. 3, pp. 429-443, Nov. 2011.   DOI
24 S. Mun and J. E. Fowler, "DPCM for quantizated block-based compressive sensing of images," in Proc. Eur. Signal Process. Conf., pp. 1424-1428, Aug. 2012.