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Editor-in-Chief
Nikiforov
Vladimir O.
D.Sc., Prof.
Partners
doi: 10.17586/2226-1494-2023-23-1-79-87
Computational methods to increase the speed of FPGA-based discrete wavelet transforms
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Article in Russian
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Abstract
For citation:
Sai S.V., Zinkevich A.V. Computational methods to increase the speed of FPGA-based discrete wavelet transforms. Scientific and Technical Journal of Information Technologies, Mechanics and Optics, 2023, vol. 23, no. 1, pp. 79–87 (in Russian). doi: 10.17586/2226-1494-2023-23-1-79-87
Abstract
The article considers the computational methods and features of the construction of a complex functional block for the implementation of the discrete wavelet transform (DWT) Dobeshie 9/7 in digital image signal processing systems based on FPGA. We proposed a mathematical model and algorithms for the implementation of parallel and series-convector methods of signal processing to calculate the coefficients of a discrete bi-orthogonal Dobeshie wavelet 9/7 taking into account the architecture of used FPGA. The model is based on wavelet transform factorization methods using lifting schemes. In contrast to conventional lifting schemes, the proposed method and algorithms can increase the speed of FPGA calculations with simplified hardware implementation. CAD Quartus II and ModelSim are used as a development environment. The behavioral model is written in Verilog HDL. Altera Cyclone® IV 4CE115 was used as FPGA. On the basis of the obtained behavioral model the testing module was developed and the simulation of digital circuit in the ModelSim environment was carried out. The formula for estimating the number of clock cycles of the forward and reverse DWT has been proposed; on its basis the estimate of the number of parallel computations depending on the number of input elements and the characteristics of the FPGA was obtained. As a result of experiments the dependences of the number of cycles for DWT computation depending on the size of the side of a square image with different variants of the number of parallel processing blocks were obtained. It is shown that parallel work of several independent modules gives a possibility to conduct concurrent processing of several input columns (rows) from input 2D array, and unification of the multiplier-summing module allows to increase efficiency of calculations and to reduce volume of occupied hardware resources. Conveyor based DWT structure is characterized by less hardware costs in terms of implementation of the calculator unit and memory allocation. As a result of testing the digital circuit, it was found that the developed block structure can significantly increase the DWT speed as well as reduce the cost of the system on a chip. The proposed realization of the block of two-dimensional forward and reverse wavelet transform for the Dobeshi 9/7 filter bank forms a complete module and can be used as a ready-made complex functional block for further development of high quality image transmission systems in real time.
Keywords: wavelet transform, lifter circuit, FPGA, complex function block, digital signal processing
Acknowledgements. The research supported financially by the Russian Science Foundation within the scientific project no. 22-21-00394 “Development of neural network methods to improve the quality of digital image transmission in intelligent video systems”.
References
Acknowledgements. The research supported financially by the Russian Science Foundation within the scientific project no. 22-21-00394 “Development of neural network methods to improve the quality of digital image transmission in intelligent video systems”.
References
1. Taubman D., Marcellin M.W. JPEG2000: Image Compression Fundamentals, Standards and Practice. Kluwer Academic Publishers, 2002, 774 p.
2. Vorobev V.I., Gribunin V.G. Principles and Practices of the Wavelet Transform. St.Petersburg, 1999, 208 p. (in Russian)
3. Mallat S. A theory for multiresolution signal decomposition: The wavelet representation. IEEE Transactions on Pattern Analysis and Machine Intelligence, 1989, vol. 11, no. 7, pp. 674–693. https://doi.org/10.1109/34.192463
4. Cohen A., Daubechies I., Feauveau J.-C. Biorthogonal bases of compactly supported wavelets. Communications on Pure and Applied Mathematics, 1992, vol. 45, no. 5, pp. 485–560. https://doi.org/10.1002/cpa.3160450502
5. Daubechies I., Sweldens W. Factoring wavelet transforms into lifting steps. Journal of Fourier Analysis and Applications, 1998, vol. 4, no. 3, pp. 247–269. https://doi.org/10.1007/BF02476026
6. Sameen I., Chang Y.C., Ng M.S., Goi B.-M., Ooi C.-P. A Unified FPGA-based system architecture for 2-D discrete wavelet transform. Journal of Signal Processing Systems, 2013, vol. 71, no. 2, pp. 123–142. https://doi.org/10.1007/s11265-012-0687-1
7. Rekha K., Ravi K. Design of high speed lifting based DWT using 9/7 wavelet transform for image compression. Proc. of the International Conference on Recent Advances in Electronics and Communication Technology (ICRAECT), 2017, pp. 132–137. https://doi.org/10.1109/ICRAECT.2017.38
8. Zhong X., Jiang H., Cao H., Yang R. Efficient lifting based CDF9/7 wavelet transform using fixed point. Proc. of the 3rd International Congress on Image and Signal Processing, 2010, pp. 3094–3097. https://doi.org/10.1109/CISP.2010.5648075
9. Dimitroulakos G., Zervas N., Sklavos N., Goutis C. An efficient VLSI implementation for forward and inverse wavelet transform for JPEG2000. Proc. of the 14th International Conference on Digital Signal Processing Proceedings (DSP). V. 1, 2002, pp. 233–236. https://doi.org/10.1109/ICDSP.2002.1027877
10. Grzeszczak A., Yeap T., Panchanathan S. VLSI architecture for discrete wavelet transform. Proc of the Canadian Conference on Electrical and Computer Engineering. V. 2, 1994, pp. 461–464. https://doi.org/10.1109/CCECE.1994.405788
11. Grzeszczak A., Mandal M., Panchanathan S. VLSI implementation of discrete wavelet transform. IEEE Transactions on Very Large Scale Integration (VLSI) Systems, 1996, vol. 4, no. 4, pp. 421–433. https://doi.org/10.1109/92.544407
12. Swami S., Mulani A. An efficient FPGA implementation of discrete wavelet transform for image compression. Proc. of the International Conference on Energy, Communication, Data Analytics and Soft Computing (ICECDS), 2017, pp. 385–3389. https://doi.org/10.1109/ICECDS.2017.8390088
13. Nagaraj P., Rajasekaran M., Muneeswaran V., Sudar K.M., Gokul K. VLSI implementation of image compression using TSA optimized discrete wavelet transform techniques. Proc. of the Third International Conference on Smart Systems and Inventive Technology (ICSSIT), 2020, pp. 667–670. https://doi.org/10.1109/ICSSIT48917.2020.9214220
14. Sakthivel S. Sankar A. Real time watermarking of grayscale images using integer DWT transform. Proc. of the International Conference on VLSI Systems, Architectures, Technology and Applications (VLSI-SATA), 2016, pp. 7593056. https://doi.org/10.1109/VLSI-SATA.2016.7593056
15. Xiong C., Tian J., Liu J. A fast VLSI architecture for two-dimensional discrete wavelet transform based on lifting scheme [image compression applications]. Proc. of the 7th International Conference on Solid-State and Integrated Circuits Technology. V. 3, 2004, pp. 1661–1664. https://doi.org/10.1109/ICSICT.2004.1435150
16. Xiong C., Tian J., Liu J. Efficient architectures for two-dimensional discrete wavelet transform using lifting scheme. IEEE Transactions on Image Processing, 2007, vol. 16, no. 3, pp. 607–614. https://doi.org/10.1109/TIP.2007.891069
17. Zhang C., Long Y., Kurdahi F. A hierarchical pipelining architecture and FPGA implementation for lifting-based 2-D DWT. Journal of Real-Time Image Processing, 2007, vol. 2, no. 4, pp. 281–291. https://doi.org/10.1007/s11554-007-0057-6
18. Sai S.V., Shoberg A.G. Quality control method of the transmission of fine image details in the JPEG2000. Computer Optics, 2020, vol. 44, no. 3, pp. 401–408. (in Russian). https://doi.org/10.18287/2412-6179-co-616