KSII Transactions on Internet and Information Systems (TIIS)

Korean Society for Internet Information (한국인터넷정보학회)
권호 표지
DOI QR코드

DOI QR Code

Analysis of Implementing Mobile Heterogeneous Computing for Image Sequence Processing

  • BAEK, Aram (The Department of Multimedia Engineering, Hanbat National University) ;
  • LEE, Kangwoon (The Department of Multimedia Engineering, Hanbat National University) ;
  • KIM, Jae-Gon (School of Electronics, Telecommunication, and Computer Engineering, Korea Aerospace University) ;
  • CHOI, Haechul (The Department of Multimedia Engineering, Hanbat National University)
  • Received : 2017.04.13
  • Accepted : 2017.06.17
  • Published : 2017.10.31
Download PDF
⟨ Previous Next ⟩

Abstract

On mobile devices, image sequences are widely used for multimedia applications such as computer vision, video enhancement, and augmented reality. However, the real-time processing of mobile devices is still a challenge because of constraints and demands for higher resolution images. Recently, heterogeneous computing methods that utilize both a central processing unit (CPU) and a graphics processing unit (GPU) have been researched to accelerate the image sequence processing. This paper deals with various optimizing techniques such as parallel processing by the CPU and GPU, distributed processing on the CPU, frame buffer object, and double buffering for parallel and/or distributed tasks. Using the optimizing techniques both individually and combined, several heterogeneous computing structures were implemented and their effectiveness were analyzed. The experimental results show that the heterogeneous computing facilitates executions up to 3.5 times faster than CPU-only processing.

Keywords

References

  1. General Purpose GPU Programming (GPGPU). http://www.gpgpu.org/
  2. NVIDIA Corporation, Compute Unified Device Arhictecture (CUDA). https://developer.nvidia.com/cuda-zone
  3. Khronos Group, Open Computing Language. https://www.khronos.org/opencl/
  4. J. Fung, S. Mann, and C. Aimone, "OpenVIDIA : Parallel GPU computer vision," in Proc. of ACM International Conference on Multimedia, pp. 849-852, November, 2005.
  5. Khronos Group, Open Graphics Library for Embedded Systems. https://www.khronos.org/opengles/
  6. R. J. Rost, OpenGL Shading Language, 2ed, Addision-Wesley Professional, 2006.
  7. A. Ensor and S. Hall, "GPU-based image analysis on mobile devices," in Proc. of International Conference on Image and Vision Computing, New Zealand (IVCNZ), 2011.
  8. N. Singhal, J. W. Yoo, H. Y. Choi and I. K. Park, "Implementation and optimization of image processing algorithms on embedded GPU," IEICE Transactions on Information and Systems, vol. 95, no. 5, pp. 1475-1484, 2012.
  9. G. Wang et al., "Accelerating computer vision algorithms using OpenCL framework on the mobile GPU-A case study," in Proc. of IEEE international Conference on Acoustics, Speech, and Signal Processing (ICASSP), pp. 2629-2634, 2013.
  10. K. Pulli et al., "Real-time computer vision with OpenCV," Commun. ACM, vol. 55, no. 6, pp. 61-69, June 2012. https://doi.org/10.1145/2184319.2184337
  11. Open Source Computer Vision (OpenCV). http://opencv.org/
  12. J. Leskela, J. Nikula, and M. Salmela, "OpenCL embedded profile prototype in mobile device," in Proc. of IEEE Workshop on Signal Processing Systems (SiPS), pp. 279-284, 2009.
  13. M. B. Lopez et al, "Accelerating image recognition on mobile devices using GPGPU," in Proc. of SPIE, Parallel Processing for Image Applications, vol. 7872, pp. 78720R-78720R-10, 2011.
  14. K. Cheng and Y. Wang. "Using mobile GPU for general-purpose computing-A case study of face recognition on smartphones," in Proc. of International Symposium on VLSI Design, Automation and Test (VLSI-DAT), pp. 1-4, 2011.
  15. B. Rister et al, "A fast and efficient SIFT detector using the mobile GPU," in Proc. of IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP), pp. 2674-2679, 2013.
  16. Apple Inc, iOS Developer Library, OpenGL ES Programming Guide for iOS, 2016.
  17. A. Baek, K. Lee, and H. Choi, "CPU and GPU parallel processing for mobile augmented reality," in Proc. of IEEE International Congress on Image and Signal Processing (CISP), vol. 01, pp. 133-137, 2013.
  18. ITU-T and ISO/IEC JTC 1, Advanced Video Coding for Generic Audiovisual Services, ITU-T Rec. H.264 and ISO/IEC 14496-10 (AVC), version 1, 2003, version 2, 2004, versions 3, 4, 2005, versions 5, 6, 2006, versions 7, 8, 2007, versions 9, 10, 11, 2009, versions 12, 13, 2010, versions 14, 15, 2011, version 16, 2012.
  19. Joint Collaborative Team on Video Coding (JCT-VC), "High Efficiency Video Coding (HEVC) text specification draft 10(for FDIS & Consent)," JCTVC-L1003, Geneva, January 2013.
  20. A. Baek, K. Lee, and H. Choi, "Speed-up image processing on mobile CPU and GPU," in Proc. of IEEE Asia Pacific Conference on Multimedia and Broadcasting (APMediaCast), pp. 79-81, April, 2015.
  21. T. Akenine-Moller and J. Strom, "Graphics processing units for handhelds," in Proc. of the IEEE, vol. 96, Issue. 5, pp. 779-789, May 2008. https://doi.org/10.1109/JPROC.2008.917719
  22. Dominik Goddeke: GPGPU Basic Math Tutorial, Fachbereich Mathematik, Universitat Dortmund, Ergebnisberichte des Instituts fur Angewandte Mathematik, Nummer 300, November, 2005. http://www.mathematik.uni-dortmund.de/-goeddeke/gpgpu/tutorial.html