IEIE Transactions on Smart Processing and Computing

The Institute of Electronics and Information Engineers (대한전자공학회)
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Measurement Coding for Compressive Sensing of Color Images

  • Dinh, Khanh Quoc (College of Information and Communication Engineering, Sungkyunkwan University) ;
  • Trinh, Chien Van (College of Information and Communication Engineering, Sungkyunkwan University) ;
  • Nguyen, Viet Anh (College of Information and Communication Engineering, Sungkyunkwan University) ;
  • Park, Younghyeon (College of Information and Communication Engineering, Sungkyunkwan University) ;
  • Jeon, Byeungwoo (College of Information and Communication Engineering, Sungkyunkwan University)
  • Received : 2013.10.20
  • Accepted : 2013.11.15
  • Published : 2014.02.28
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Abstract

From the perspective of reducing the sampling cost of color images at high resolution, block-based compressive sensing (CS) has attracted considerable attention as a promising alternative to conventional Nyquist/Shannon sampling. On the other hand, for storing/transmitting applications, CS requires a very efficient way of representing the measurement data in terms of data volume. This paper addresses this problem by developing a measurement-coding method with the proposed customized Huffman coding. In addition, by noting the difference in visual importance between the luma and chroma channels, this paper proposes measurement coding in YCbCr space rather than in conventional RGB color space for better rate allocation. Furthermore, as the proper use of the image property in pursuing smoothness improves the CS recovery, this paper proposes the integration of a low pass filter to the CS recovery of color images, which is the block-based ${\ell}_{20}$-norm minimization. The proposed coding scheme shows considerable gain compared to conventional measurement coding.

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References

  1. C. E. Shannon, "Communication in the presence of noise", Proc. Institute of Radio Engineers, vol. 37(1), pp. 10-21, 1949. Article (CrossRef Link)
  2. H. Nyquist, "Certain topics in telegraph transmission theory," Trans. AIEE, vol. 47, pp. 617-644, 1928. Article (CrossRef Link)
  3. D. L. Donoho, "Compressive sensing," IEEE Trans. on Inform. Theory, vol. 52(4), pp. 1289-1306, Apr. 2006. Article (CrossRef Link) https://doi.org/10.1109/TIT.2006.871582
  4. E. Candes and T. Tao, "Near-optimal signal recovery from random projections and universal encoding strategies," IEEE Trans. Inf. Theory, vol. 52(12), pp. 5406-5425, 2006. Article (CrossRef Link) https://doi.org/10.1109/TIT.2006.885507
  5. H. Lee, S. Park, S. Park, "Introduction to Compressive Sensing," The magazine of IEEK, vol. 38(1), pp. 19-30, 2011. Article (CrossRef Link)
  6. Y. M. Cho, "Compressive Sensing - Mathematical Principles and Practical Implications," The magazine of IEEK, vol. 38(1), pp. 31-43, 2011. Article (CrossRef Link)
  7. L. Gan, "Block compressed sensing of natural images," Proc. Intern. Conf. on Digital Signal Process., pp. 403-406, UK, 2007. Article (CrossRef Link)
  8. S. Mun and J. E. Fowler, "Block compressed sensing of image using directional transforms," Proc. IEEE Intern. Conf. on Image Process. (ICIP), pp. 3021-3024, 2009. Article (CrossRef Link)
  9. J. Xu, J. Ma, D. Zhang, Y. D. Zhang, and S. Lin, "Improved total variation minimization method for compressive sensing by intra-prediction," Signal Process., vol. 92(11), pp. 2614-2623, 2012. Article (CrossRef Link) https://doi.org/10.1016/j.sigpro.2012.04.001
  10. J. Bigot, C. Boyer, and P. Weiss, "An analysis of block sampling strategies in compressed sensing," arXiv:1305.4446 [cs.IT], 2013. Article (CrossRef Link)
  11. S. Mun and J. E. Fowler, "DPCM for quantized block-based compressive sensing of images," Proc. European Signal Process. Conf., pp. 1424-1428, Romania, 2012. Article (CrossRef Link)
  12. K. Q. Dinh, H. J. Shim, and B. Jeon, "Measurement coding for compressive imaing based on structured measurement matrix," Proc. IEEE Intern. Conf. on Image Process. (ICIP), pp. 10-13, 2013. Article (CrossRef Link)
  13. J. Zhang, D. Zhao, and F. Jiang, "Spatially directional predictive coding for block-based compressive sensing of natural images," Proc. IEEE Intern. Conf. on Image Process. (ICIP), pp. 1021-1025, 2013. Article (CrossRef Link)
  14. W. B. Pennebaker and J. L. Mitchell, JPEG still image data compression standard, Van Nostrand Reinhold, 1993. Article (CrossRef Link)
  15. S. Shin, H. Go, H. Park, and B. Jeon, "DPCM-Based Image Pre-Analyzer and Quantization Method for Controlling the JPEG File Size," IEEK for Conf., vol. 28(2), pp. 561-564, 2005. Article (CrossRef Link)
  16. G. J. Sullivan, WA. Redmond, J. Ohm, W. Han, and T. Wiegand, "Overview of the High Efficiency Video Coding (HEVC) standard," IEEE Trans. Circuits and System. for Video Technology, vol. 22(12), pp. 1649-1668, 2012. Article (CrossRef Link) https://doi.org/10.1109/TCSVT.2012.2221191
  17. K. N. Plataniotis and A. N. Venetsanopoulos, Color Image Processing and Applications, Springer, Berlin, 2000. Article (CrossRef Link)
  18. H. Ahn, H. Jeong, J. Ha, K. Kim, and B. Kang, "Improvement Structure of RGB to YCbCr Conversion block of ISP Platform in Mobile Phones," Proc. Interm. Soc Design Conf., pp. 448-451. 2009. Article (CrossRef Link)
  19. A. M.D. Zahangir and H. J. Lee, "A comparative study of different color space for paddy disease segmentation," The Institute of Electronics Engineers of Korea - Signal Processing, vol. 3, pp. 90-98, 2001. Article (CrossRef Link)
  20. P. Nagesh and B. Li, "Compressive imaging of color images," IEEE Int. Conf. Acoust., Speech Signal Process. (ICASSP), pp. 1261-1264, 2009. Article (CrossRef Link)
  21. G. Coluccia, D. Valsesia, and E. Magli, "Smoothness-Constrained Image Recovery from Block-based Random Projections," Proc. IEEE Intern. Workshop on Multimedia Signal Process., pp. 129-134, Italia, 2013. Article (CrossRef Link)
  22. H. T. Kung and S. J. Tarsa, "Partitioned compressive sensing with neighbor-weighted decoding," Proc. IEEE Military Comm. Conf., pp. 149-156, USA, 2011. Article (CrossRef Link)
  23. K. Q. Dinh, H. J. Shim, and B. Jeon, "Deblocking filter for artifact reduction in distributed compressive video sensing," Proc. IEEE Visual Comm. and Image Process., pp. 1-5, USA, 2012. Article (CrossRef Link)
  24. G. Bjontegaard, Calculation of average PSNR differences between RD-curves, (VCEG-M33). Article (CrossRef Link)
  25. L. Zhang, L. Zhang, X. Mou, and D. Zhang, "FSIM: a feature similarity index for image quality assessment," IEEE Trans. Image Process., vol. 20(8), pp. 2378-2386, 2011. Article (CrossRef Link) https://doi.org/10.1109/TIP.2011.2109730
  26. C. Brites, J. Ascenso, and F. Pereira, "Studying temporal correlation noise modeling for pixel based Wyner-Ziv video coding" Proc. of IEEE Int. Conf. on Image Processing, pp. 273-276, USA, 2006. Article (CrossRef Link)

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