A Novel Meteosat Second Generation Image Compression Method Based on Radon Transform, Linear Predictive Coding with Filtering and Sorted Run Length Coding
(*) Corresponding author
The monitoring of the earth by meteorological satellites became an indispensable process. The main mission of these satellites consists to improve daily weather forecasts and anticipate severe weather. But they also follow a few activities such as: the monitoring of urban spaces, the evolution of the vegetation cover, and the detection of forest fires etc. The Meteosat Second Generation satellite is equipped with a radiometric sensor SEVIRI (Spinning Enhanced Visible & Infrared Imager), allows the acquisition of 12 image files that correspond to the 12 channels of visible and mid-infrared, thermal infrared every 15 minutes. The accumulation of these images confront us with problems of storage and transmission. There are many interesting techniques of image compression that allow an efficient minimization of the size of the images with a good rate-distortion compromise. This paper presents a novel Meteosat Second Generation image compression method based on Radon transform and two novel techniques, which are Linear Predictive Coding (LPC) with filtering and the Sorted Run Length Coding (SRLC). The Radon transform is applied to MSG images to retrieve the Radon points. The residual predictions are extracted first from the filtered Radon points and then encoded by the arithmetic encoding to recover a single codeword using the LPC coding with filtering. Finally, the SRLC coding is performed to achieve a high and fixe compression ratio by the generation of a sorted sequence of the codeword and a sorted occurrence table. This method ensures an excellent rate-distortion compromise: high and fixed compression ratio of 99.90 % while preserving the integrity and the quality of the MSG images after decompression.
Copyright © 2015 Praise Worthy Prize - All rights reserved.
EUMETSAT, monitoring weather and climate from space, http://www.eumetsat.int/website/home/index.html, 2015.
T. Pellarin, A. Ali, F. Chopin, I. Jobard, J. C. Bergès, Using spaceborne surface soil moisture to constrain satellite precipitation estimates over West Africa, Geophysical Research Letters, Vol. 35, n. 2, pp. 1-5, 2008.
Lacaze, B., Bergès, J.C., Contribution of Météosat Second Generation (MSG) imagery to drought early warning, 2006. Proceedings of the 1st International Conference on Remote Sensing and Geoinformation Processing in the Assessment and Monitoring of Land Degradation and Desertification. Trier, Germany (A. Röder & J. Hill eds), 7-9 September 2005, pp. 406-412.
Szantai, A., Héas, P., Mémin, E., Comparison of atmospheric motion vectors and dense vector fields calculated from MSG images. Proc. Int. Winds Workshop, Beijing, China, 2006, pp.1-8.
D. Aojie, X. Yong, L. Chi, G. Jie, M. Linlu, P. Peiyuan, Estimation of soil thermal inertia from geostationary Meteosat Second Generation (MSG) data, Remote Sensing Letters, Vol. 5, n. 8, pp. 763-772, 2014.
N. Ghilain, F. De Roo, F. Gellens-Meulenberghs, Evapotranspiration monitoring with Meteosat Second Generation satellites: improvement opportunities from moderate spatial resolution satellites for vegetation, International Journal of Remote Sensing, Vol. 35, n. 7, pp. 2654-2670, 2014.
K. Stavros, G. George, S. Chrysostomos, Achieving downscaling of Meteosat thermal infrared imagery using artificial neural networks, International Journal of Remote Sensing, Vol. 34, n. 21, pp. 7706-7722, 2013.
Mohia, Y., Ameur, S., Lazri, M., Brucker, J.M., Rainfall intensity classification method based on textural and spectral parameters from MSG-SEVIRI, (2014) International Review on Computers and Software (IRECOS), 9 (7), pp. 1302-1313.
D. A. Huffman, A method for the construction of minimum redundancy codes, Proceedings of the IRE, Vol. 40, n. 9, pp. 1098-1101, 1952.
Vasanthi Kumari, P., Thanushkodi, K., A secure fast 2D-discrete fractional Fourier transform based medical image compression using SPIHT algorithm with Huffman encoder, (2013) International Review on Computers and Software (IRECOS), 8 (7), pp. 1702-1710.
P. Devaki, D. G. Raghavenrda, Lossless reconstruction of secret image using threshold secret sharing and transformation, International Journal of Network Security & Its Applications (IJNSA), Vol. 4, n. 3, pp. 111-119, 2012.
M. K. Abdmouleh, A. Masmoudi, M. S. Bouhlel, A New Method Which Combines Arithmetic Coding with RLE for Lossless Image Compression, Journal of Software Engineering and Applications, Vol. 5, n. 1, pp. 41-44, 2012.
B. P. Tunstall, Synthesis of noiseless compression codes, PhD thesis, Georgia Institute of Technology, 1967.
R. F. Rice, R. Plaunt, Adaptive Variable-Length Coding for Efficient Compression of Spacecraft Television Data, IEEE Transactions on Communications, Vol. 16, n. 9, pp. 889–897, 1971.
A. Devi, M. G. Mini, Gray scale image compression based on wavelet transform and linear prediction, The International Journal of Multimedia & Its Applications (IJMA), Vol. 4, n. 1, pp. 47-62, 2012.
N. Ahmed, T. Natarajan, K. R. Rao, Discrete Cosine Transform, IEEE Computers Transactions, Vol. 23, n. 1, pp. 90-94, 1974.
Ellappan, V., Samson Ravindran, R., An effective selection of DCT and DWT coefficients for an adaptive medical image compression technique using multiple kernel FCM, (2014) International Review on Computers and Software (IRECOS), 9 (4), pp. 628-637.
Thiruveni, M., Shanthi, D., Design of vedic architecture for high speed DCT, (2014) International Review on Computers and Software (IRECOS), 9 (2), pp. 330-336.
P. Courmontagne, An improvement of ship wake detection based on the radon transform, Signal Processing, Vol. 85, n. 8, pp. 1634–1654, 2005.
J. Radon, On the determination of functions from their integrals along certain manifolds, translation of Radon's 1917 paper by R. Lohner, The Radon transform and some of its applications, Annexe A, John Wiley & Sons, 204-217, 1983.
J. V. Guedon, D. Barba, N. Burger, Psychovisual image coding via an exact discrete Radon transform, Proc. SPIE 2501, Visual Communications and Image Processing '95, Vol. 25.01, pp. 562-572, 1995,
M. Lahdir, S. Ameur, A. Adane, Algorithme non intératif basé sur les ondelettes biorthogonales et les fractales pour la compression d'images satellitaires, Télédétection, Vol. 6, n. 4, pp. 345-360, 2007.
J. M. Shapiro, Embedded image coding using zerotrees of wavelet coefficients, IEEE transactions on signal processing, Vol. 41, n. 12, pp. 3445 – 3462, 1993.
A. Said, 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, n. 3, pp. 243–250, 1996.
Mohan, V., Venkataramani, Y., Medical image compression in WBCT domain using fast FIC and SPIHT coding, (2014) International Review on Computers and Software (IRECOS), 9 (6), pp. 1035-1042.
Rammohan, T., Sankaranarayanan, K., Vijayakumari, V., An efficient image compression technique with dead zone quantization through wavelet-based contourlet transform with Modified SPIHT encoding, (2013) International Review on Computers and Software (IRECOS), 8 (6), pp. 1313-1320.
M. Lahdir, A. Nait-ali, S. Ameur, Fast Encoding-Decoding of 3D Hyperspectral Images Using a Non-Supervised Multimodal Compression Scheme, Journal of Signal and Information Processing, Vol. 2, n. 04, pp. 316-321, 2011.
T. Acharya, P. S. Tsai, JPEG2000 Standard for Image Compression: Concepts, Algorithms and VLSI Architectures Wiley, 2004.
S. Medouakh, Z. E. Baarir, Study of the Standard JPEG2000 in Image Compression, International Journal of Computer Applications, Vol. 18, n. 1, pp. 27-33, 2011.
C. Chapin, Differential quantization of communication signals, U. S. patent 2605361, filed June 29, 1950, issued July 29, 1952.
Achkar, R., Haidar, G.A., Mansour, C, Real-time application of DPCM and ADM systems, Communication Systems, Networks & Digital Signal Processing (CSNDSP), 8th International Symposium, pp. 1-6, 2012, Print ISBN: 978-1-4577-1472-6.
M. Cherifi, M. Lahdir, S. Ameur, Z. Ameur, The effectiveness of the Radon transform against the quantization noise, European Scientific Journal, Vol. 11, n. 6, pp. 70-81, 2015.
L. M. Murphy, Linear feature detection and enhancement in noisy images via the Radon transform, Pattern Recognition Letters, Vol. 4, n. 4, pp. 279-284, 1986.
Haddad, Z., Beghdadi, A., Serir, A., Mokraoui, A., Fingerprint Identification using Radon Transform, Image Processing Theory, Tools and Applications, 2008. IPTA 2008 IEEE, pp. 1-7, 2008, E-ISBN: 978-1-4244-3322-3.
D'Acunto, M., Benassi, A., Moroni, D., Salvetti, O., Radon transform: Image reconstruction and identification of noise and instrumental artifacts, Signal Processing and Communications Applications Conference (SIU) IEEE, pp. 2280 – 2284, 2014,
Tania A., Miguel M., Marco A., Ulises H., Miscalibration detection in phase-shifting algorithms by applying radon transform, Proc. SPIE 9131, Optical Modelling and Design III, 91311G, p. 7, 2014.
D. Harwood, M. Subbarao, H. Hakalahti, L. S. Davis, A New Class of Edge-Preserving Smoothing Filters, Pattern Recognition Letters, Vol. 6, n. 3, pp. 155-162, 1987.
O. Marques, Practical image and video processing using Matlab A John Wiley & Sons, Publication and IEEE Press, 2011.
K. S. Thyagarajan, Still image and video compression with Matlab John Wiley & Sons publication, 2011.
J. Fessler, Analytical tomographic image reconstruction methods, Chapter 3, p. 47, 2009.
- There are currently no refbacks.
Please send any question about this web site to firstname.lastname@example.org
Copyright © 2005-2023 Praise Worthy Prize