An Algorithm with Low Complexity for Image Compression and its Hardware Implementation using VHDL

  • Shaktijeet Mahapatra Department of ECE, ITER, Siksha ‘O‘ Anusandhan University, Bhubaneswar, Odisha, India
  • Mihir Narayan Mohanty
Keywords: Image compression, real-time processing, VHDL implementation, 2D-DWT.

Abstract

Image compression is highly essential for efficient transmission and storage of images in the field of communication engineering, bio-medical applications. Also, the compression technology is of special interest for the fast transmission and real-time processing on the internet. For reduced form and less capacity, the area of research growing day by day. The objective of image compression is to find a new representation in which pixels are less correlated, but with the original contents. In this paper, the existing as well as new algorithms are applied for compression for evaluation. The results have been compared for both techniques. On the basis of evaluating and analyzing the image compression techniques it presents the VHDL implementation of low complexity 2D-DWT approach applied to image compression. The decompression has to invert the transformations applied by the compression to the image data. When using the wavelet transform it is possible to exploit the unique properties of the wavelet coefficients to efficiently encode them.

 

References

G. K. Wallace, “The JPEG Still Picture Compression Standard”, Vol. 38, no. 1, IEEE Transactions on Consumer Electronics, pp. xviii – xxxiv, Feb. 1992.

J. M. Shapiro, “Embedded image coding using zerotrees of wavelet coefficients,” IEEE Transactions on Signal Processing, vol. 41, no. 12, pp. 3445-3462, December 1993.

S.W. Chiang, and L.M. Po, “Adaptive Lossy LZW Algorithm for Palettised Image Compression”, Electronics Letters, IEE, pp. 852-854, March 1997.

R. W. Buccigrossi, E.P. Simoncelli, “Image Compression via Joint Statistical Characterization in Wavelet Domain”, Vol. 8, No. 12, pp. 1688-1701, December 1999.

M. Boliek, M. J. Gormish, E. L. Schwartz and A. Keith, “Next generation image compression and manipulation using CREW,” Proceedings of the IEEE International Conference on Image Processing, October 26-29, Santa Barbara, CA, pp. III-567-III-357, 1997.

M. J. Gormish, E. L. Schwartz, A. Keith, M. Boliek and A. Zandi, “Lossless and nearly lossless compression for high quality images,” Proceedings of the SPIE/IS&T Conference on Very High Resolution and Imaging II, vol. 3025, San Jose, CA, pp. 62-70, February 1997.

C. Souani, M. Abid, K. Torki, R. Tourki, “VLSI design of 1-D DWT architecture with parallel filters”, INTEGRATION, the VLSI Journal 29, pp. 181-207,2000.

S-K Paek, L-S Kim, “A Real-Time Wavelet Vector Quantization Algorithm and Its VLSI Architecture”, pp. 475-489, Vol. 10, No. 3, IEEE Transactions on Circuits and Systems for Video Technology, April 2000.

Urriza, et al., “VLSI Implementation of Discrete Wavelet Transform for Lossless Compression of Medical Images”, Real-Time Imaging 7, pp. 203-217, 2001.

A. Said and W. Pearlman, “A new fast and efficient image codec based on set partitioning in hierarchical trees,” IEEE Transactions on Circuits and Systems for Video Technology, vol. 6, no. 3, pp. 243-50, June 1996.

D. Taubmann, E. Ordentlich, M. Weinberger, G. Seroussi, “Embedded Block Coding In JPEG2000”, Signal Processing: Image Communication 17, pp. 49-72, 2002.

B. J. Falkowski, “Lossless Binary Image Compression Using Logic Functions and Spectra”, Computers and Electrical Engineering 30, pp. 17-43, 2004.

Y-T Chen, D-C Tseng, “Wavelet Based Medical Image Compression with Adaptive Prediction”, Computerized Medical Imaging and Graphics 31, pp. 1-8, 2007.

Rasmita lenka, Swagatika Padhi, Minakshee Behera, Naresh Patnaik, Mihir N. Mohanty, “Design of Neuro-wavelet based vector quantizer for image compression”, Special Issue of International Journal of Computer and Communication Technology, Volume 1 Issue 2, 3, 4; pp- 214-221,August- 2010.

Asit Kumar Subudhi, Biswajit Mishra, Mihir N. Mohanty, “VLSI Design and Implementation for Adaptive Filter using LMS Algorithm”, IJCCT, Vol-2, Issue-6, 23rd -24th Feb, 2011.

Mihir Narayan Mohanty, Biswajit Mishra, Aurobinda Routray, “FPGA implementation of CLMS Algorithm”, ICEAS, IEEE Conference, Bhubaneswar, 28th -30th December, 2011.

Mihir N. Mohanty, Hemanta Kumar Sahu, “FPGA Implementation of Variable Step-size LMS Algorithm”, IEEE Conf.- ICCSP' 13, Melmaruvathur, TN, 03- 05 April 2013.

Panchami Padmasana, Mihir Narayan Mohanty, Hemanta Kumar Sahu, “VHDL Implementation of Spatial Filter for Image Enhancement”, IEEE Conf., Chennai, ICCSP – 3-5 April 2014.

Panchami Padmasana, Mihir Narayan Mohanty, P. Kabisatpathy, “FPGA Implementation of Modified Median Filter for Impulse Noise Removal from Image”, IJECT Vol. 5.3 - Spl 1, July - September, 2014.

P. Turzca, M. Duplaga, “Low Power FPGA-Based Image Processing Core for Wireless Capsule Endoscopy”, Sensors and Actuators A: Physical 172, pp. 552-560, 2011.

S.M. Aziz, D.M. Pham, “Efficient Parallel Architecture for Multi-Level Forward Discrete Wavelet Transform Processors”, Computers and Electrical Engineering 38, pp. 1325-1335, 2012.

S. Mallat, “A theory for multiresolution signal decomposition: the wavelet representation,” IEEE Transactions on Pattern Analysis and Machine intelligence, vol. 11, no. 7, pp. 674-693, July 1989.

F. Javier Diaz, A. M. Buron, J. M. Solana, “Haar wavelet based processor scheme for image coding with low circuit complexity”, Computers and Electrical Engineering 33, pp. 109-126, 2007.

http://www.imageprocessingplace.com/root_files_V3/image_databases.htm

Published
2015-02-28