Determining the Quality of Compression in High Resolution Satellite Images Using Different Compression Methods

Article Preview

Abstract:

Measuring the quality of image is very complex and hard process since the opinion of the humans are affected by physical and psychological parameters. So many techniques are invented and proposed for image quality analysis but none of the methods suits best for it. Assessment of image quality plays an important role in image processing. In this paper we present the experimental results by comparing the quality of different satellite images (ALOS, RapidEye, SPOT4, SPOT5, SPOT6, SPOTMap) after compression using four different compression methods namely Joint Photographic Expert Group (JPEG), Embedded Zero tree Wavelet (EZW), Set Partitioning in Hierarchical Tree (SPIHT), Joint Photographic Expert Group – 2000 (JPEG 2000). The Mean Square Error (MSE), Signal to Noise Ratio (SNR) and Peak Signal to Noise Ratio (PSNR) values are calculated to determine the quality of the high resolution satellite images after compression.

You might also be interested in these eBooks

Info:

Pages:

202-217

Citation:

Online since:

October 2015

Export:

Price:

* - Corresponding Author

[1] Cox, I. J., Kilian, J., Leighton, F. T., & Shamoon, T. (1997). Secure spread spectrum watermarking for multimedia. Image Processing, IEEE Transactions on, 6(12), 1673-1687.

DOI: 10.1109/83.650120

Google Scholar

[2] Sanjith, S., Ganesan, R. (2014). A review on hyperspectral image compression. In Control, Instrumentation, Communication and Computational Technologies (ICCICCT), 2014 International Conference on 1159-1163.

DOI: 10.1109/iccicct.2014.6993136

Google Scholar

[3] Al-Najjar, Yusra AY. "Dr. Der Chen Soong, (2012) Comparison of Image Quality Assessment: PSNR, HVS, SSIM, UIQI, International Journal of Scientific & Engineering Research 8(3), 1-5.

Google Scholar

[4] Sheikh, Hamid Rahim, Muhammad Farooq Sabir, and Alan Conrad Bovik. (2006) A statistical evaluation of recent full reference image quality assessment algorithms, Image Processing, IEEE Transactions on 15 (11), 3440-3451.

DOI: 10.1109/tip.2006.881959

Google Scholar

[5] Geoimage, Established in 1988. http: /www. geoimage. com. au/satellite, Accessed 10 August (2015).

Google Scholar

[6] Shimada, Masanobu, Yasushi Muraki, and Yuichi Otsuka. Discovery of Anoumoulous Stripes over the Amazon by the PALSAR onboard ALOS satellite., IGARSS 2008-2008 IEEE International Geoscience and Remote Sensing Symposium. (2008).

DOI: 10.1109/igarss.2008.4779009

Google Scholar

[7] Tyc, G., Tulip, J., Schulten, D., Krischke, M., & Oxfort, M. (2005). The RapidEye mission design. Acta Astronautica, 56(1), 213-219.

DOI: 10.1016/j.actaastro.2004.09.029

Google Scholar

[8] Magnusson, M., & Fransson, J. E. (2004). Combining airborne CARABAS-II VHF SAR data and optical SPOT-4 satellite data for estimation of forest stem volume. Canadian Journal of Remote Sensing, 30(4), 661-670.

DOI: 10.5589/m04-027

Google Scholar

[9] Wolter, P. T., Townsend, P. A., & Sturtevant, B. R. (2009). Estimation of forest structural parameters using 5 and 10 meter SPOT-5 satellite data. Remote Sensing of Environment, 113(9), 2019-(2036).

DOI: 10.1016/j.rse.2009.05.009

Google Scholar

[10] Bruton, M. J., Maron, M., Levin, N., & McAlpine, C. A. (2015). Testing the relevance of binary, mosaic and continuous landscape conceptualisations to reptiles in regenerating dryland landscapes. Landscape Ecology, 30(4), 715-728.

DOI: 10.1007/s10980-015-0157-9

Google Scholar

[11] Wang, Z., & Bovik, A. C. (2002). A universal image quality index. Signal Processing Letters, IEEE, 9(3), 81-84.

DOI: 10.1109/97.995823

Google Scholar

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

DOI: 10.1109/lgrs.2006.888109

Google Scholar

[13] Turaga, D. S., Chen, Y., & Caviedes, J. (2004). No reference PSNR estimation for compressed pictures. Signal Processing: Image Communication, 19(2), 173-184.

DOI: 10.1016/j.image.2003.09.001

Google Scholar

[14] Sanjith, S., Ganesan, R., & Isaac, R. S. (2015). Experimental Analysis of Compacted Satellite Image Quality Using Different Compression Methods. Advanced Science, Engineering and Medicine, 7(3), 227-233.

DOI: 10.1166/asem.2015.1673

Google Scholar

[15] Wallace, G. K. (1991). The JPEG still picture compression standard. Communications of the ACM, 34(4), 30-44.

DOI: 10.1145/103085.103089

Google Scholar

[16] Shapiro, J. M. (1993, November). Smart compression using the embedded zerotree wavelet (EZW) algorithm. In Signals, Systems and Computers, 1993. 1993 Conference Record of the Twenty-Seventh Asilomar Conference on (pp.486-490). IEEE.

DOI: 10.1109/acssc.1993.342561

Google Scholar

[17] Spaulding, J., Noda, H., Shirazi, M. N., & Kawaguchi, E. (2002). BPCS steganography using EZW lossy compressed images. Pattern Recognition Letters, 23(13), 1579-1587.

DOI: 10.1016/s0167-8655(02)00122-8

Google Scholar

[18] Dragotti, P. L., Poggi, G., & Ragozini, A. R. (2000). Compression of multispectral images by three-dimensional SPIHT algorithm. Geoscience and Remote Sensing, IEEE Transactions on, 38(1), 416-428.

DOI: 10.1109/36.823937

Google Scholar

[19] Skodras, A., Christopoulos, C., & Ebrahimi, T. (2001). The JPEG 2000 still image compression standard. Signal Processing Magazine, IEEE, 18(5), 36-58.

DOI: 10.1109/79.952804

Google Scholar

[20] Sanjith S, Ganesan R (2015) Performance evaluation of basic compression methods for different satellite Imagery. Indian Journal of Science and Technology. Accepted Manuscript.

Google Scholar

[21] Sanjith S, Ganesan R, Evaluating the Quality of Compression in Very High Resolution Satellite Images Using Different Compression Methods, International Journal of Engineering Research in Africa. Accepted Manuscript.

DOI: 10.4028/www.scientific.net/jera.19.91

Google Scholar