The Journal of Korean Institute of Communications and Information Sciences (한국통신학회논문지)

The Korean Institute of Commucations and Information Sciences (한국통신학회)
권호 표지
DOI QR코드

DOI QR Code

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

영상 압축센싱을 위한 블록기반 변환영역 측정 부호화

  • 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)
  • Received : 2014.10.14
  • Accepted : 2014.12.09
  • Published : 2014.12.31
Download PDF
⟨ Previous Next ⟩

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.

압축센싱은 신호의 성긴 (Sparse) 성질을 활용하여 Nyquist 표본화율 보다 낮은 측정 율만으로도 신호의 완벽 복원이 가능하다는 측면에서 새로운 샘플링 기술로 주목 받고 있다. 블록기반의 압축센싱 기술을 사용하여 영상을 샘플링 하는 경우, 측정신호 영역에서도 공간 영역의 유사도가 보존되므로, 본 논문에서는 블록기반 압축센싱 기술을 사용하여 획득한 자연영상의 측정 신호에 대한 새로운 부호화 기술을 제안한다. 측정신호 간 유사성을 제거하기 위해 이산 웨이블릿 변환(DWT)을 적용한 후, 각 DWT 계수에 적절한 양자화를 수행한다. 이를 통해, 측정 신호 내의 중복성을 제거하고, 측정 신호의 비트 율 또한 절약할 수 있었다. 실험 결과, 기존의 블록기반 평활 Projected Landweber 알고리즘에 스칼라 양자화를 적용한 방법, DPCM 방법을 적용한 방법, 그리고 Multihypothesis 기반 블록기반 평활알고리즘에 DPCM을 적용한 방법과 비교할 때, 제안방법의 PSNR이 각각 최대 4dB, 0.9dB, 그리고 2.5dB 더 높은 성능을 보이는 것을 확인 할 수 있었다.

Keywords

References

  1. 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.
  2. 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. https://doi.org/10.1016/j.acha.2011.02.002
  3. 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.
  4. 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. https://doi.org/10.1109/TCOM.1984.1096008
  5. G. K. Wallace, "The JPEG still picture compression standard," IEEE Trans. Consumer Electron., vol. 38, no. 1, pp. 18-34, Feb. 1992.
  6. 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. https://doi.org/10.1109/30.920468
  7. 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. https://doi.org/10.1109/TCSVT.2003.815165
  8. 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. https://doi.org/10.7840/kics.2014.39C.6.503
  9. 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. https://doi.org/10.1109/TCSVT.2012.2221191
  10. 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. https://doi.org/10.7840/kics.2014.39A.4.172
  11. 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. https://doi.org/10.7840/KICS.2012.37A.3.157
  12. R. Baraniuk, "Compressed sensing," IEEE Signal Process. Mag., vol. 24, no. 4, pp. 118-124, Jul. 2007.
  13. 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. https://doi.org/10.7840/kics.2013.38C.11.1029
  14. B. K. Natarajan, "Sparse approximate solution to linear system," Soc. Ind. Appl. Mathematics J. Comput., vol. 24, no. 2, pp. 227-234, Apr. 1995.
  15. E. J. Candes and T. Tao, "Decoding by linear programming," IEEE Trans. Inf. Theory, vol. 51, no. 12, pp. 4203-4215, Dec. 2005. https://doi.org/10.1109/TIT.2005.858979
  16. M. A. Davenport, "Random observations on random observations: sparse signal acquisition and processing," Ph.D. Thesis, Rice Univ., Aug. 2010.
  17. 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.
  18. 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. https://doi.org/10.1109/TCOM.1963.1088759
  19. 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.
  20. 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.
  21. 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.
  22. S. Mun and J. E. Fowler, "Block compressed sensing of images using directional transforms," IEEE Int. Conf. Image Process., pp. 3021-3024, Nov. 2009.
  23. 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.
  24. 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.

Cited by

  1. Low-Complexity Sub-Pixel Motion Estimation Utilizing Shifting Matrix in Transform Domain vol.11, pp.4, 2016, https://doi.org/10.5370/JEET.2016.11.4.1020