Skip to main content
Log in

Implementation and Comparison of the 5/3 Lifting 2D Discrete Wavelet Transform Computation Schedules on FPGAs

  • Published:
Journal of Signal Processing Systems Aims and scope Submit manuscript

Abstract

The suitability of the 2D Discrete Wavelet Transform (DWT) as a tool in image and video compression is nowadays indisputable. For the execution of the multilevel 2D DWT, several computation schedules based on different input traversal patterns have been proposed. Among these, the most commonly used in practical designs are: the row–column, the line-based and the block-based. In this work, these schedules are implemented on FPGA-based platforms for the forward 2D DWT by using a lifting-based filter-bank implementation. Our designs were realized in VHDL and optimized in terms of throughput and memory requirements, in accordance with the principles of both the schedules and the lifting decomposition. The implementations are fully parameterized with respect to the size of the input image and the number of decomposition levels. We provide detailed experimental results concerning the throughput, the area, the memory requirements and the energy dissipation, associated with every point of the parameter space. These results demonstrate that the choice of the suitable schedule is a decision that should be dependent on the given algorithmic specifications.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Similar content being viewed by others

References

  1. ISO/IEC FCD15444-1: 2000, “JPEG 2000 image coding system,” 2000.

  2. ISO/IEC JTC1/SC29/WG11, FCD 14496-1, “Coding of moving pictures and audio,” 1998.

  3. S. Mallat, “A theory for multiresolution signal decomposition: The wavelet representation,” IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 11, no. 7, 1989, pp. 674–693.

    Article  MATH  Google Scholar 

  4. I. Daubechies and W. Sweldens, “Factoring wavelet transforms into lifting steps,” Journal of Fourier Analysis and Applications, vol. 4, no. 3, 1998, pp. 247–269.

    Article  MATH  MathSciNet  Google Scholar 

  5. K. Andra, C. Chakrabarti, and T. Acharya, “A VLSI architecture for lifting-based forward and inverse wavelet transform,” IEEE Transactions on Signal Processing, vol. 50, no. 4, 2002, pp. 966–977.

    Article  Google Scholar 

  6. R. Calderbank, I. Daubechies, W. Sweldens, and B.-L. Yeo, “Wavelet Transforms That Map Integers To Integers,” Journal of Applied Computational Harmonics Analysis, vol. 5, no. 3, 1998, pp. 332–369.

    Article  MATH  MathSciNet  Google Scholar 

  7. S. Dewitte and J. Cornelis, “Lossless Integer Wavelet Transform,” IEEE Signal Processing Letters, vol. 4, no. 6, 1997, pp. 158–160.

    Article  Google Scholar 

  8. M. D. Adams and F. Kossentini, “Reversible integer-to-integer wavelet transforms for image compression: performance evaluation and analysis,” IEEE Transanctions on Image Processing, vol. 9, no. 6, 2000, pp. 1010-1024.

    Article  MATH  MathSciNet  Google Scholar 

  9. C. Chrysafis and A. Ortega, “Line-based, reduced memory, wavelet image compression,” IEEE Transactions on Image Processing, vol. 9, no. 3, 2000, pp. 378–389.

    Article  MATH  MathSciNet  Google Scholar 

  10. G. Lafruit, L. Nachtergaele, B. Vanhoof, and F. Catthoor, “The Local Wavelet Transform: a memory-efficient, high-speed architecture optimized to a Region-Oriented Zero-Tree coder,” Integrated Computer-Aided Engineering, vol. 7, no. 2, 2000, pp. 89–103.

    Google Scholar 

  11. Cast, Inc., http://www.cast-inc.com.

  12. Amphion Semiconductor, Ltd., http://www.amphion.com.

  13. G. Dillen, B. Georis, J.-D. Legat, and O. Cantineau, “Combined Line-Based Architecture for the 5–3 and 9–7 Wavelet Transform of JPEG2000,” IEEE Transactions on Circuits and Systems for Video Technology, vol. 13, no. 9, 2003, pp. 944–950.

    Article  Google Scholar 

  14. N. D. Zervas, G. P. Anagnostopoulos, V. Spiliotopoulos, Y. Andreopoulos, and C. E. Goutis, “Evaluation of design alternatives for the 2-d-discrete wavelet transform,” IEEE Transactions on Circuits and Systems for Video Technology, vol. 11, no. 12, 2001, pp. 1246–1262.

    Article  Google Scholar 

  15. M. Weeks and M. Bayoumi, “Discrete Wavelet Transform: Architectures, Design and Performance Issues,” Journal of VLSI Signal Processing, vol. 35, no. 2, 2003, pp. 155–178.

    Article  MATH  Google Scholar 

  16. C. Chakrabarti, M. Vishwanath, and R. M. Owens, “Architectures for wavelet transforms: A survey,” Journal of VLSI Signal Processing, vol. 14, no. 2, 1996, pp. 171–192.

    Article  Google Scholar 

  17. Y. Andreopoulos, P. Schelkens, G. Lafruit, K. Masselos, and J. Cornelis, “High-level cache modeling for 2-D discrete wavelet transform implementations,” Journal of VLSI Signal Processing (special issue on Signal Processing Systems), vol. 34, no. 3, 2003, pp. 209–226.

    Article  MATH  Google Scholar 

  18. K. Masselos, Y. Andreopoulos, and T. Stouraitis, “Performance comparison of two-dimensional discrete wavelet transform computation schedules on a VLIW digital signal processor,” in IEE Proceedings Vision, Image & Signal Processing, vol. 153, no. 2, 2006, pp. 173–180.

    Article  Google Scholar 

  19. C.-T. Huang, P.-C. Tseng, and L.-G. Chen, “Analysis and VLSI Architecture for 1-D and 2-D Discrete Wavelet Transform,” IEEE Transactions on Signal Processing, vol. 53, no. 4, 2005, pp. 1575–1586.

    Article  MathSciNet  Google Scholar 

  20. M. Angelopoulou, K. Masselos, P. Cheung, and Y. Andreopoulos, “A Comparison of 2-D Discrete Wavelet Transform Computation Schedules on FPGAs,” in IEEE International Conference on Field Programmable Technology, 2006, pp. 181-188.

  21. Micron Technologies, “SDRAM System-Power Calculator,” http://www.micron.com/support/designsupport/tools.

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Maria E. Angelopoulou.

Rights and permissions

Reprints and permissions

About this article

Cite this article

Angelopoulou, M.E., Masselos, K., Cheung, P.Y.K. et al. Implementation and Comparison of the 5/3 Lifting 2D Discrete Wavelet Transform Computation Schedules on FPGAs. J Sign Process Syst Sign Image 51, 3–21 (2008). https://doi.org/10.1007/s11265-007-0139-5

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11265-007-0139-5

Keywords

Navigation