Discrete Wavelet Transform Based Image Compression using Frequency Band Suppression and Throughput Enhancement


  • Lakshmi Narayanan K




Image Processing, Discrete Wavelet Transform, Image Compression, Segmentation


Discrete Wavelet Transform is an effective and important process in digital image processing. The Discrete Wavelet Transform is used for significant compression, segmentation, classification, image enhancement and image fusion of web images that are to be viewed in short page loading time with bandwidth as major constraint. The DWT provides spatial frequency information and the spatial location. The main advantage of this nature is that concurrent information helps us to reduce redundancy and increases the bandwidth more efficiently. The spatial locations of the image may or may not contain variations and at certain times may be constants also. A signal which is constant does not carry significant information. If the coefficients of constant signal are encoded along with other spatial location coefficients it is memory wastage. Discrete Wavelet Transform if applied to the image as a whole results in better frequency resolution and good spatial resolution. But still this spatial resolution is not that good. This work aims in improving the spatial resolution further. The image is segmented in space into small sub-images and Discrete Wavelet Transform is applied recursively to each and every sub image. Since the target image is spatially small in resolution as well, the same operation can be achieved with smaller Discrete Wavelet Transform Unit. Since there are only few pixels at any given processing time is faster and the bandwidth, throughput is high. Because of large throughput the entire operation can be pipelined and done in a serial manner. The proposed work reduces the number of DWT unit required for the process.


Download data is not yet available.

Author Biography

Lakshmi Narayanan K

Research Scholar, St. Peters University, Chennai - 600054, Tamil Nadu, India.


1. Grgi?, Sonja, Krešimir Kerš, and Mislav Grgi?. "Image compression using wavelets." IEEE International Symposium on. Vol. 1.1999.

2. Mulcahy, Colm. "Image compression using the Haar wavelet transform." Spelman Science and Mathematics Journal 1.1 (1997): 22-31.

3. Talukder, Kamrul Hasan, and Koichi Harada. "Haar wavelet based approach for image compression and quality assessment of compressed image." arXiv preprint arXiv:1010.4084 (2010).

4. Chowdhury, M. Mozammel Hoque, and Amina Khatun. "Image compression using discrete wavelet transform." IJCSI International Journal of Computer Science Issues 9.4 (2012): 327-330.

5. Sukanya, Y., and J. Preethi. "Analysis Of Image Compression Algorithms Using Wavelet Transform With Gui In Matlab." IJERT, eISSN: 2319-1163.

6. Alarcon-Aquino, V., et al. "Lossy image compression using discrete wavelet transform and thresholding techniques." The Open Cybernetics & Systemics Journal

7.1 (2013): 32-38. 7. Akshay Kekre, Dr. Sanjay Pokle, “Image Compression Using Wavelet Transform And Differential Pulse Code Modulation Technique”, International Journal of Engineering Research & Technology (IJERT) Vol. 2 Issue 7, July – 2013 ISSN: 2278-0181.

8. Toda, Hiroshi, Zhong Zhang, and Takashi Imamura. "The wide designed discrete wavelet transforms based on the perfect translation invariance theorem." Wavelet Analysis and Pattern Recognition (ICWAPR), 2011 International Conference on. IEEE, 2011.

9. Huang, Chao-Tsung, Po-Chih Tseng, and Liang-Gee Chen. "Efficient VLSI architectures of lifting-based discrete wavelet transform by systematic design method." Circuits and Systems, 2002. ISCAS 2002. IEEE International Symposium on. Vol. 5. IEEE, 2002.

10. Zhao, Wei, and Raghuveer M. Rao. "A discrete-time wavelet transform based on a continuous dilation framework." Acoustics, Speech, and Signal Processing, 1999. Proceedings., 1999 IEEE International Conference on. Vol. 3. IEEE, 1999.