i-manager Publications

Compression of Hyperspectral Images by Using DWT and SPIHT

Veera Navendra Reddy*, 0**

* PG Scholar, Department of Electronics and Communication Engineering, JNTUACE, Pulivendula, Andra Pradesh, India.

** Associate Professor, Department of Electronics Communication Engineering, JNTUACE, Pulivendula, Andra Pradesh, India.

Periodicity:March - May'2017
DOI : https://doi.org/10.26634/jpr.4.1.13643

Abstract

The major aim of compression is to remove insignificance attached in redundancy of image-information in order to store or transmit information efficiently. Compression is a way of writing secret information using less fragments than an original picture would use. The Discrete-Wavelet-Transform (DWT) is comparatively recent compared to Discrete-Cosine Transform (DCT) with necessary properties. It achieves this with the redundancy problem of only for 2-dimensional signals that are considerably not up to the non-decimated DCT. A new compression technique supported DWT and Set Partitioning In Hierarchical Trees (SPIHT) has been discussed. Approximate shift is not varied, smart directional-property process influences the properties of DWT making it a better object for compression. A varied SPIHT process is introduced to expand its efficiency for compression. To increase security of the hosts and raise the effectiveness of the image, the DWT and SPIHT methods have been employed.

Keywords

Medical Image, DWT with SPIHT Encoding Process, Reconstruct Image, Data Extraction

How to Cite this Article?

Reddy, K. V. N., and Sivappagari, C. M. R. (2017). Compression of Hyperspectral Images by Using DWT and SPIHT. i-manager’s Journal on Pattern Recognition, 6(1), 40-62. https://doi.org/10.26634/jpr.4.1.13643

References

[1]. Chang, S., & Carin, L. (2006). A modified SPIHT algorithm for image coding with a joint MSE and classification distortion measure. IEEE Transactions on Image Processing, 15(3), 713-725.

[2]. Chen J. C. (2008). Medical Image Compression by using SPIHT algorithm. Expert System with Applications. 34(11), 337-346.

[3]. Christophe, E., Mailhes, C., & Duhamel, P. (2008). Hyperspectral image compression: Adapting SPIHT and EZW to anisotropic 3-D wavelet coding. IEEE Transactions on Image Processing, 17(12), 2334-2346.

[4]. Du, Q., & Fowler, J. E. (2007). Hyperspectral image compression using JPEG2000 and principal component analysis. IEEE Geoscience and Remote Sensing Letters, 4(2), 201-205.

[5]. Javed. M. Y, & Khan. M.H. (2008). Wavelet based hyperspectral image compression from side to side prediction. Multitopic Conference, 2008. INMIC 2008. IEEE International Conference (pp. 155-159).

[6]. Lewis, A. S., & Knowles, G. (1992). Image compression using the 2-D wavelet transform. IEEE Transactions on Image Processing, 1(2), 244-250.

[7]. Ponomarenko, N., Lukin, V., Egiazarian, K., & Astola, J. (2005). DCT based high quality image compression. Image Analysis, 45-60.

[8]. Xiu, C., & Zhu, H. (2010). A modified SPIHT algorithm based on wavelet coefficient blocks for robust image transmission over noisy channel. In Information Science and Engineering (ISISE), 2010 International Symposium on (pp. 58-61). IEEE.

If you have access to this article please login to view the article or kindly login to purchase the article

Single Article

	North Americas,UK, Middle East,Europe		India	Rest of world
	USD	EUR	INR	USD-ROW
Online	15	15

ADD TO CART

Options for accessing this content:

If you would like institutional access to this content, please recommend the title to your librarian.
If you already have i-manager's user account: Login above and proceed to purchase the article.
New Users: Please register, then proceed to purchase the article.