in black and white
Main menu
Home About us Share a book
Biology Business Chemistry Computers Culture Economics Fiction Games Guide History Management Mathematical Medicine Mental Fitnes Physics Psychology Scince Sport Technics

- Acharya T.

Acharya T. - John Wiley & Sons, 2000. - 292 p.
ISBN 0-471-48422-9
Download (direct link): standardforImagecompressioncon2000.pdf
Previous << 1 .. 48 49 50 51 52 53 < 54 > 55 56 57 58 59 60 .. 100 >> Next

35 23,3 + 25,3 RAl RS+2/2,8 = 22,8
36 - RAl RS-2/4,1 = 24,1
MEM1 consists of two banks and MEM2 consists of four banks. The multibank structure increases the memory bandwidth and helps support highly pipelined operation. Details of the memory organization and size, register file, and schedule for the overall architecture with specific details for each constituent filter have been included in [27].
Total time required to transform an N x N block using (5, 3) wavelet filter using this architecture is 2[iV/2j + 3Ta + 2Ts + N + 5 + |_iV/2jN clock cycles, where Ta is delay of an adder and Ts is delay of a shifter. Any other type of wavelet filters can be efficiently executed in this architecture as well. Details of these filters and their timing for execution in this architecture have been presented in [27].
In this chapter, we presented VLSI algorithms and architectures for discrete wavelet transforms. We described the traditional convolution (filtering) approach for computation of discrete wavelet transform and described how a systolic architecture can be designed for wavelet filters by exploiting the symmetric relationship of the filter coefficients. Since the lifting-based wavelet transform is a development of the late 1990s and is new in the VLSI community, we emphasized more on the VLSI architectures for the lifting-based DWT computation in this chapter. Lifting-based DWT has many advantages over the traditional convolution-based approach. It requires less memory and computation for implementation compared to the convolution-based approach. We reviewed and presented the VLSI architectures that have been reported very recently for lifting-based DWT. We presented how the data-dependency diagram for the lifting computation can be mapped into pipelined architectures for suitable VLSI implementation, and proposed enhancement of the pipeline architectures by applying different schemes reported in the literature. We described in greater detail a highly folded VLSI architecture for computation of both one-dimensional and two-dimensional transformations.
1. S. Mallat, “A Theory for Multiresolution Signal Decomposition: The Wavelet Representation,” IEEE Trans. Pattern Analysis And Machine Intelligence, Vol. 11, No. 7, pp. 674-693, July 1989.
2. R. M. Rao and A. S. Bopardikar, Wavelet Transforms: Introduction to Theory and Applications. Addison-Wesley, MA, 1998.
3. M. Antonini, M. Barlaud, P. Mathieu, and I. Daubechies, “Image Coding Using Wavelet Transform,” IEEE Trans, on Image Processing, Vol. 1, No. 2, pp. 205-220, April 1992.
4. JPEG 2000 Final Committee Draft (FCD), “JPEG 2000 Committee Drafts,”
5. S. G. Mallat and W. L. Hwang, “Singularity detection and processing with wavelets,” IEEE Transactions on Information Theory, Vol. 37, pp. 617-643, March 1992.
6. J. O. Chapa and M. R. Raghuveer, “Matched Wavelets — Their Construction, and Application to Object Detection”. IEEE International Conference on Acoustics, Speech and Signal Processing, Atlanta, GA, April 1996.
7. D. L. Donoho, De-noising via soft thresholding. Technical report 409. Stanford, CA:Department of Statistics, Stanford University, November 1992.
8. H. Guo, J. E. Odegard, M. Lang, R. A. Gopinath, I. W. Selesnick, and C. S. Burrus, “Wavelet based Speckle Reduction with Applications to SAR based ATD/R,” Proceedings of 1st International Conference on Image Processing, Vol. 1, pp. 75-79, Austin, TX, November 1994.
9. D. Berman, J. Bartell, and D. Salesin, “Multiresolution Painting and Compositing,” Proceedings of SIGGRAPH, ACM, pp. 85-90, New York, 1994.
10. H. Li, B. S. Manjunath, and S. K. Mitra, “Multisensor Image Fusion using the Wavelet Transform,” Graphical Models and Image Processing, Vol. 57, pp.235-245, May 1995.
11. W. Sweldens, “The Lifting Scheme: A Custom-Design Construction of Biorthogonal Wavelets,” Applied and Computational Harmonic Analysis, Vol. 3, No. 15, pp. 186-200, 1996.
12. I. Daubechies and W. Sweldens, “Factoring Wavelet Transforms into Lifting Schemes,” The J. of Fourier Analysis and Applications, Vol. 4, pp. 247-269, 1998.
13. A. R. Calderbank, I. Daubechies, W. Sweldens, and B.-L. Yeo, “Wavelet Transforms that Map Integers to Integers,” Applied and Computational Harmonic Analysis, Vol. 5, pp. 332-369, July, 1998.
14. M. D. Adams and F. Kossentini, “Reversible Integer-to-Integer Wavelet Transforms for Image Compression: Performance Evaluation and Analysis,” IEEE Trans, on Image Processing, Vol. 9, pp. 1010-1024, June 2000.
15. G. Knowles, “VLSI Architectures for the Discrete Wavelet Transform,” Electronics Letters, Vol. 26, No. 15, pp. 1184-1185, July 1990.
16. A. S. Lewis and G. Knowles, “VLSI Architecture for 2-D Daubechies Wavelet Transform without Multipliers,” Electronics Letters, Vol. 27, No.
2, pp. 171-173, January 1991.
Previous << 1 .. 48 49 50 51 52 53 < 54 > 55 56 57 58 59 60 .. 100 >> Next