Hardware Design of the Discrete Wavelet Transform: an Analysis of Complexity, Accuracy and Operating Frequency
Main Article Content
Keywords
Discrete Wavelet Transform, method, topology, structure, quantization format
Abstract
The purpose of this paper is to present a comparative analysis of hardware design of the Discrete Wavelet Transform (DWT) in terms of three design goals: accuracy, hardware cost and operating frequency. Every design should take into account the following facts: method (non-polyphase, polyphase and lifting), topology (multiplier-based and multiplierless-based), structure (conventional or pipelined), and quantization format (floatingpoint, fixed-point, CSD or integer). Since DWT is widely used in several applications (e.g. compression, filtering, coding, pattern recognition among others), selection of adequate parameters plays an important role in the performance of these systems.
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References
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[10] Z. Fang, N. Xiong, L. T. Yang, X. Sun, and Y. Yang, “Interpolation- Based Direction-Adaptive Lifting DWT and Modified SPIHT for Image Compression in Multimedia Communications,” IEEE Systems Journal, vol. 5, no. 4, pp. 584–593, Dec. 2011. [Online]. Available: http: //ieeexplore.ieee.org/document/6044698/ 130
[11] H. Demirel and G. Anbarjafari, “Improved face recognition system using probability distribution functions extracted from wavelet subbands,” in 2009 24th International Symposium on Computer and Information Sciences, IEEE. Ankara: IEEE, Sep. 2009, pp. 94–98. [Online]. Available: http://ieeexplore.ieee.org/document/5291859/ 130
[12] N. Begum, M. Alam, and M. I. Islam, “Application of Canny filter and DWT in fingerprint detection a new approach,” in 2010 13th International Conference on Computer and Information Technology (ICCIT), IEEE. Dhaka, Bangladesh: IEEE, Dec. 2010, pp. 256–260. [Online]. Available: http://ieeexplore.ieee.org/document/5723865/ 130
[13] Y.-T. Chou, S.-M. Huang, S.-H. Wu, and J.-F. Yang, “DWT and Sub-pattern PCA for Face Recognition Based on Fuzzy Data Fusion,” in 2011 International Conference on Intelligent Computation and Bio-Medical Instrumentation, IEEE. Wuhan, China: IEEE, Dec. 2011, pp. 296–299. [Online]. Available: http://ieeexplore.ieee.org/document/6131767/ 130
[14] R. Zhang and J. Ding, “Facial recognition based on wavelet transform,” in World Automation Congress (WAC), 2012. Puerto Vallarta, Mexico: IEEE, 2012, pp. 1–4. [Online]. Available: http://ieeexplore.ieee.org/document/6321574/ 130
[15] J. Andrew, “Coding gain and spatial localisation properties of discrete wavelet transform filters for image coding,” IEE Proceedings - Vision, Image, and Signal Processing, vol. 142, no. 3, p. 133, 1995. [Online]. Available: http://digital-library.theiet.org/content/journals/10.1049/ip-vis_19951938 130
[16] D. Marpe and H. Cycon, “Very low bit-rate video coding using wavelet-based techniques,” IEEE Transactions on Circuits and Systems for Video Technology, vol. 9, no. 1, pp. 85–94, 1999. [Online]. Available: http://ieeexplore.ieee.org/document/744277/ 130
[17] S. Li and W. Li, “Shape-adaptive discrete wavelet transforms for arbitrarily shaped visual object coding,” IEEE Transactions on Circuits and Systems for Video Technology, vol. 10, no. 5, pp. 725–743, 2000. [Online]. Available: http://ieeexplore.ieee.org/document/856450/ 130
[18] K. Ferguson and N. Allinson, “Psychophysically derived quantisation model for efficient DWT image coding,” IEE Proceedings - Vision, Image, and Signal Processing, vol. 149, no. 1, p. 51, 2002. [Online]. Available: http: //digital-library.theiet.org/content/journals/10.1049/ip-vis_20020073 130
[19] J. Yang, Y.Wang, W. Xu, and Q. Dai, “Image coding using dual-tree discrete wavelet transform.” IEEE transactions on image processing : a publication of the IEEE Signal Processing Society, vol. 17, no. 9, pp. 1555–69, Sep. 2008. [Online]. Available: http://www.ncbi.nlm.nih.gov/pubmed/18701394 130
[20] H. Mota, N. Volpini, G. Rodrigues, and F. Vasconcelos, “A realtime processing system for denoising of partial discharge signals using the wavelet transform,” in Conference Record of the 2008 IEEE International Symposium on Electrical Insulation, IEEE. Vancouver, British Colombia, Canada: IEEE, Jun. 2008, pp. 391–395. [Online]. Available: http://ieeexplore.ieee.org/document/4570356/ 130
[21] J. Chilo and T. Lindblad, “Hardware Implementation of 1D Wavelet Transform on an FPGA for Infrasound Signal Classification,” IEEE Transactions on Nuclear Science, vol. 55, no. 1, pp. 9–13, 2008. [Online]. Available: http://ieeexplore.ieee.org/document/4448457/ 130
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