iosr-JVSP   Volume-1 ~ Issue-2

ABSTRACT:Image compression plays an important role in the compression of medical images. Medical imagings are mainly used for diagnosis of diseases and surgical planning. Medical images are usually stored digitally. Medical Image compression plays an important role in telematics especially in telemedicine. It is necessary that medical images need to be compressed for reliability to be transmitted. In the medical image compression diagnosis is effective only when the compressed image preserves all the information of the original image. This results in a lossless compression technique. While lossy compression techniques, are more efficient in terms of storage and transmission needs but there is no warranty that they can preserve the characteristics needed in medical image processing and diagnosis. Compression plays an important role as medical imaging moves to film less imaging.CTI or MRI medical imaging are used nowadays which can produce pictures of the human body in digital form. The lifting scheme is used for the design of both orthogonal and bi-orthogonal filters .It is implemented for the orthogonal filters using two lifting steps .The performance of the proposed filters are then compared with the conventional filters in terms of compression ratio, PSNR etc. The bi-orthogonal filters are implemented using the SPIHT algorithm replacing the EZW coding for image compression. SPIHT algorithm requires fewer bits to capture the same amount of information when compared with EZW proposed.
Keywords: Bi-orthogonal wavelet transforms, Lifting scheme, SPIHT algorithm, EZW coding, orthogonal filters.

[1] F. Argenti and E. Del Re, "Design of biorthogonal M-channel cosine modulated FIR/IIR filter banks," IEEE Trans. Signal
Process., vol. 48, no. 3, pp. 876–881, Mar. 2000.
[2] A. Z. Averbuch and V. A. Zheludev, "A new family of spline-based biorthogonal wavelet transforms and their application to
image compression," IEEE Trans. Image Process., vol. 13, no. 7, pp. 993–1007,Jul. 2004.
[3] X. Zhang, W. Wang, T. Yoshikawa, and Y. Takei, "Design of IIR orthogonal wavelet filter banks using lifting scheme," IEEE
Trans. Signal Process., vol. 54, no. 7, pp. 2616–2624, Jul. 2006.
[4] J. M. Shapiro, "Embedded image coding using zerotrees of wavelets coefficients," IEEE Trans. Signal Process., vol. 41, no. 12,
pp.3445–3462, Dec. 1993.
[5] J. Villasenor, B. Belzer, and J. Liao, "Wavelet filter evaluation for image compression," IEEE Trans. Image Process., vol. 4, no.
8, pp.1053–1060, Aug. 1995.
[6] A. Said and W. A. Pearlman, "A new fast and efficient image codec based on set partitioning in hierarchical trees," IEEE
Trans. Circuits Syst. Video Technol., vol. 6, no. 3, pp. 243–250, May 1996.
[7] M. Antonini, M. Barlaud, P. Mathieu, and I. Daubechies, "Image coding using wavelet transform," IEEE Trans. Image Process.,
vol. 1,no. 2, pp. 205–220, Apr. 1992.
[8] C. W. Kim and R. Ansari, "FIR/IIR exact reconstruction filter banks with applications to subband coding of images," in Proc. 34th
Midwest Symp. Circuits Syst., May 1991, pp. 227–230.
[9] M. J. T. Smith and S. L. Eddins, "Analysis/synthesis techniques for subband image coding," IEEE Trans. Acoust., Speech, Signal
Process., vol. 38, no. 8, pp. 1446–1456, Aug. 1990.
[10] S. Rout and A. E. Bell, "Narrowing the performance gap between orthogonal and biorthogonal wavelets," in Conf. Rec. 38th
Asilomar Conf. Signals, Syst. Comput. 2004, vol. 2, pp. 1757–1761.

ABSTRACT:In this paper, a scheme for the design of pipeline architecture for a real-time computation of the 2- D DCT has been presented. The objective has been to achieve a low computation time by maximizing the operational frequency and minimizing the number of clock cycles required for the DCT computation, which, in turn, have been realized by developing a scheme for enhanced inter- and intra stage parallelisms for the pipeline architecture. it is most efficient to map the overall task of the DCT computation to only two pipeline stages, i.e., one for performing the task of the level-1DCT computation and the other for performing that of all the remaining decomposition levels. In view of the fact that the amount and nature of the computation performed by the two stages are the same, their internal designs ought to be the same. There are two main ideas that have been employed for the internal design of each stage in order to enhance the intra stage parallelism. The first idea is to decompose the filtering operation into two subtasks that operate independently on the evenand odd-numbered input samples, respectively. This idea stems from the fact that the DCT computation is a twosub band filtering operation, and for each consecutive decomposition level, the input data are decimated by a factor of two.
Keywords: DCT, Pipeline Architecture, 2-D DCT, Intra Stage Parallelisms, Filtering

[1] M. J. Weinberger, G. Sapiro and G. Seroussi, "The LOCOI Lossless image compression algorithm: Principle and standardization
into JPEG-LS," IEEE Trans. on Image Processing, vol. 9, pp. 1309-1324, Aug. 2000.
[2] SA.Martucci, "Reversible compression of HDTV images using median adaptive prediction and arithmetic coding," Proc. IEEE
intern'l Symp. On Circuits and Syst., pp. 1310-1313,1990.
[3] Golomb S W. "Run-length encodings," IEEE Trans. Inform. Theory, vol. IT-12, pp. 399-401, July 1966.
[4] Rice R F. "Some practical universal noiseless coding techniques," Tech. Rep. JPL- 79-22, Jet Propulsion Laboratory, Pasadena, CA,
Mar. 1979.
[5] A. Savakis and M. Pioriun, "Benchmarking and Hardware Implementation of JPEG-LS," ICIP '02, Rochester, NY, Sep. 2002.
[6] M. Klimesh, V. Stanton, and D. Watola, HardwareImplementation of a Lossless Image Compression Algorithm Using a Field
Programmable Gate Array," NASA JPL TMO Progress Report 42-144, 2001.
[7] M. Ferretti, M. Boffadossi, "A Parallel PipelinedImplementation of LOCO-I for JPEG-LS," 17th International Conference on
Pattern Recognition (ICPR'04), vol. 1, pp. 769- 772. 2004.
[8] Xiang Xie, GuoLin Li, and XinKai Chen, "A Low Power Digital IC Design Inside the Wireless Endoscopy Capsule," Asian Solid-
State Circuits Conference (A-SSCC 2005), pp. 217-220, 2005.
[9] K. A. Kotteri, S. Barua, A. E. Bell, and J. E. Carletta, "A comparison of hardware implementations of the biorthogonal 9/7 DCT:
Convolution versus lifting," IEEE Trans. Circuits Syst. II, Exp. Briefs, vol. 52, no.5, pp. 256–260, May 2006.
[10] C. Wang and W. S. Gan, "Efficient VLSI architecture for lifting-based discrete wavelet packet transform," IEEE Trans. Circuits
Syst. II, Exp. Briefs, vol. 54, no. 5, pp. 422–426, May 2007.