Journal of Broadcast Engineering (방송공학회논문지)

The Korean Institute of Broadcast and Media Engineers (한국방송∙미디어공학회)
권호 표지
DOI QR코드

DOI QR Code

A Skip-mode Coding for Distributed Compressive Video Sensing

분산 압축 비디오 센싱을 위한 스킵모드 부호화

  • Nguyen, Quang Hong (Sungkyunkwan University, College of Information & Communication Engineering) ;
  • Dinh, Khanh Quoc (Sungkyunkwan University, College of Information & Communication Engineering) ;
  • Nguyen, Viet Anh (Sungkyunkwan University, College of Information & Communication Engineering) ;
  • Trinh, Chien Van (Sungkyunkwan University, College of Information & Communication Engineering) ;
  • Park, Younghyeon (Sungkyunkwan University, College of Information & Communication Engineering) ;
  • Jeon, Byeungwoo (Sungkyunkwan University, College of Information & Communication Engineering)
  • ;
  • ;
  • ;
  • ;
  • 박영현 (성균관대학교 정보통신대학) ;
  • 전병우 (성균관대학교 정보통신대학)
  • Received : 2014.02.27
  • Accepted : 2014.03.12
  • Published : 2014.03.30
Download PDF
⟨ Previous Next ⟩

Abstract

Distributed compressive video sensing (DCVS) is a low cost sampling paradigm for video coding based on the compressive sensing and the distributed video coding. In this paper, we propose using a skip-mode coding in DCVS under the assumption that in case of high temporal correlation, temporal interpolation can guarantee sufficiently good quality of nonkey frame, therefore no need to transmit measurement data in such a nonkey frame. Furthermore, we extend it to use a hierarchical structure for better temporal interpolation. Simulation results show that the proposed skip-mode coding can save the average subrate of whole video sequence while the PSNR is reduced only slightly. In addition, by using the proposed scheme, the computational complexity is also highly decreased at decoder on average by 43.75% for video sequences that have strong temporal correlation.

분산 압축 비디오 센싱 (DCVS) 기술은 압축센싱 및 분산 비디오 부호화 기술의 결합을 통해 저 비용의 샘플링을 실현하는 새로운 패러다임이다. 본 논문에서는 프레임 간 높은 시간 상관성을 활용한 DCVS에서의 스킵모드 부호화 방법을 제안한다. 제안하는 방법은 일정조건을 만족하는 비 키-프레임에 대한 측정값을 복호화기에 전송하지 않아도 시간적 보간법을 통해 해당 비 키-프레임의 복원이 가능하도록 하여 율-왜곡 측면에서 좋은 압축 성능을 보장한다. 이와 더불어, 더 나은 시간적 보간을 위하여 계층적 구조를 사용하는 방법을 제안한다. 실험 결과, 제안하는 스킵모드 부호화 방법은 약간의 PSNR 감소에 비해 매우 높은 측정율 절약이 되는 것을 확인하였다. 또한, 제안하는 방법을 높은 시간 연관성을 갖는 비디오 영상에 적용할 경우, 복호화기의 연산 복잡도가 평균 43.75% 감소하는 것을 확인하였다.

Keywords

References

  1. B. Girod, A. Aaron, S. Rane, and D. Rebollo-Monedero, "Distributed Video Coding," in Proc. IEEE Special Issue On Advance In Video Coding And Delivery, vol. 93, pp. 71-83, June 2005.
  2. J. Park, B. Jeon, D. Wang, and A. Vincent, "Wyner-Ziv Video Coding with Region Adaptive Quantization and Progressive Channel Noise Modeling," in Proc. IIEEE International Symposium on Broadband Multimedia Systems and Broadcasting, pp. 1-6, May 2009.
  3. H. Cho, H. Eun, H. J. Shim, and B. Jeon, "Transform-domain Wyner-Ziv Residual Coding using Temporal Correlation," Journal of Broadcasting Engineering, vol. 17, no. 1, pp. 140-151, Jan. 2012. https://doi.org/10.5909/JEB.2012.17.1.140
  4. D.-Y. Kim, G.-H. Park, K.-H. Kim, and D.-Y. Suh, "A study on performance evaluation of DVCs with different coding method and feasibility of spatial scalable DVC," Journal of Broadcasting Engineering, vol. 12, no. 6, pp. 585-595, Nov. 2007. https://doi.org/10.5909/JBE.2007.12.6.585
  5. J. Park and B. Jeon, "Fast Distributed Video Coding using Parallel LDPCA Encoding," Journal of Broadcasting Engineering, vol. 16, no. 1, pp. 144-154, Jan. 2011. https://doi.org/10.5909/JEB.2011.16.1.144
  6. M. Duarte, M. Davenport, D. Takhar, J. Laska, T. Sun, K. Kelly, and R. Baraniuk, "Single-pixel Imaging via Compressive Sampling," IEEE Signal Processing Magazine, vol. 25, pp. 83-91, March 2008. https://doi.org/10.1109/MSP.2007.914730
  7. J. Prades-Nebot, Y. Ma, and T. Huang, "Distributed Video Coding using Compressive Sampling," in Proc. of the Picture Coding Symposium, pp. 1-4, May 2009.
  8. S. Mun and J. E. Flower, "Residual Reconstruction for Block-based compressed sensing of Video," in Proc. Data Compression Conference (DCC), pp. 183-192, March 2011.
  9. T. T. Do, Y. Chen, D. T. Nguyen, N. Nguyen, L. Gan, and T. D. Tran, "Distributed Compressive Video Sensing," IEEE International Conference on Image Processing, pp. 1393-1396, Nov. 2009.
  10. L. W. Kang and C. S. Lu, "Distributed Compressive Video Sensing," in Proc. IEEE International Conference on Acoustic, Speech and Signal Processing, pp. 325-330, July 2012.
  11. Y. Baig, E. M. K. Lai, and A. Punchihewa, "Distributed Video Coding Based on Compressive Sensing," in Proc. IEEE International Conference on Multimedia and Expo Workshops (ICMEW), pp. 325-330, July 2012.
  12. R. Baraniuk, "Compressed sensing," IEEE Signal Processing Magazine, vol. 24, pp. 118-121, July 2007.
  13. E. Candes, J. Romberg, and T. Tao, "Decoding by linear programing," IEEE Trans. Inform. Theory, vol. 51, pp. 4203-4215, Dec. 2005. https://doi.org/10.1109/TIT.2005.858979
  14. B. K. Natarajan, "Sparse approximate solution to linear systems," SIAM J. Comput., vol. 24, pp. 227-234, April 1995. https://doi.org/10.1137/S0097539792240406
  15. S. Marcia and Z. Zhang, "Matching pursuits with time-frequency dictionaries," IEEE Trans. Signal Processing, vol. 41, pp. 3397-4415, Dec. 1993. https://doi.org/10.1109/78.258082
  16. J. Tropp and A. C. Gilbert, "Signal recovery from random measurements via orthogonal marching pursuit," IEEE Trans. Inform. Theory, vol. 53, pp. 4655-4666, Dec. 2007. https://doi.org/10.1109/TIT.2007.909108
  17. J. Ascenso and F. Pereira, "Hierarchical Motion Estimation for Side Information Creation in Wyner-Ziv Video Coding," in Proc. of 2nd International Conference on Ubiquitous Information Management and Communication. pp. 347-352, 2008.
  18. K. D. Quoc, X. H. Van, and B. Jeon, "An Iterative Algorithm for Efficient Adaptive GOP Size in Transform Domain Wyner-Ziv Video Coding," in Advances in Image and Video Technology Lecture Notes in Computer Science, vol. 7088, pp. 347-358, 2012.
  19. K. D. Quoc, H. J. Shim, and B. Jeon, "Measurement Coding for Compressive Imaging using a structural measuremnet matrix," in Proc. 20th IEEE Internal Conference on Image Processing (ICIP), pp. 10-13, September 2013.
  20. R. Baraniuk, M. Davenport, R. D. Vore, and M. Wakin, "A simple proof of the restricted isometry property for random matrices," Constructive Approximation, vol. 28, pp. 253-263, December 2008. https://doi.org/10.1007/s00365-007-9003-x

Cited by

  1. Rate Allocation for Block-based Compressive Sensing vol.20, pp.3, 2015, https://doi.org/10.5909/JBE.2015.20.3.398