Research Article | Open Access | Download PDF
Volume 3 | Issue 3 | Year 2013 | Article Id. IJCOT-V3I6P109 | DOI : https://doi.org/10.14445/22492593/IJCOT-V3I6P109
Precision-Aware and Quantization of Lifting Based DWT Hardware Architecture
Rekha. N ,Dr. K B Shivakumar , M.Z Kurian
Citation :
Rekha. N ,Dr. K B Shivakumar , M.Z Kurian, "Precision-Aware and Quantization of Lifting Based DWT Hardware Architecture," International Journal of Computer & Organization Trends (IJCOT), vol. 3, no. 3, pp. 38-43, 2013. Crossref, https://doi.org/10.14445/22492593/ IJCOT-V3I6P109
Abstract
This paper presentsprecision-aware approaches and associated hardware implementations for performing the DWT. By implementing BP architecture and also presents DS design methodologies. These methods enable use of an optimal amount of hardware resources in the DWT computation. Experimental measurements of design performance in terms of area, speed, and power for 90-nm complementary metal–oxidesemiconductor implementation are presented. Results indicate that BP designs exhibit inherent speed advantages than DS design.
Keywords
Fixed point arithmetic, image coding, very largescale integration (VLSI), wavelet transforms.
References
[1] M. Rabbani and R. Joshi, “An overview of the JPEG 2000 still image compression standard,” Signal Process.: Image Commun., vol. 17, no. 1, pp. 3–48, Jan. 2002.
[2] C. Huang, P. Tseng, and L. Chen, “Flipping structure: An efficient VLSI architecture for lifting based discrete wavelet transform,” IEEETrans. Signal Process., vol. 52, no. 4, pp. 1080–1089, Apr. 2004
[3] C. Cheng and K. Parhi, “High-speed VLSI implementation of 2-D discrete wavelet transform,” IEEE Trans. Signal Process., vol. 56, no. 1, pp. 393–403, Jan. 2008.
[4] C. Xiong, J. Tian, and J. Liu, “Efficient architectures for two-dimensional discrete wavelet transform using lifting scheme,” IEEE Trans.Image Process., vol. 16, no. 3, pp. 607–614, Mar. 2007.
[5] S. Barua, K. Kotteri, A. Bell, and J. Carletta, “Optimal quantized lifting coefficients for the 9/7 wavelet,” in Proc. IEEE Int. Conf.Acoust., Speech, Signal Process., 2004, vol. 5, pp. 193–196.
[6] V. Spiliotopoulos, N. Zervas, Y. Andreopoulos, G. Anagnostopoulos, and C. Goutis, “Quantization effect on VLSI implementations for the 9/7 DWT filters,” in Proc. IEEE Int. Conf. Acoust., Speech, SignalProcess., 2001, vol. 2, pp. 1197–1200.
[7] M. Weeks, “Precision for 2-D discrete wavelet transform processors,” in Proc. IEEE Workshop Signal Process. Syst., 2000, pp. 80–89.
[8] M. Marcellin, M. Lepley, A. Bilgin, T. Flohr, T. Chinen, and J. Kasner, “An overview of quantization in JPEG 2000,” Signal Process.: ImageCommun., vol. 17, no. 1, pp. 73–84, Jan. 2002.
[9] T. Acharya and C. Chakrabarti, “A survey on lifting-based discrete wavelet transform architectures,” J. VLSI Signal Process. vol. 42, no. 3, pp. 321–339, Mar. 2006.
[10] Dong-U Lee, Member, IEEE, Lok-Won Kim, Student Member, IEEE, and John D. Villasenor, Senior Member “precision aware self quantizing hardware architecture for DWT”, in proc IEEETrans. Image processing , VOL. 21, NO. 2, February 2012.