Lossless Meteorological Images Compression
Keywords:
Compression, Lossless, Radar image, Satellite image, Meteorology.Abstract
Nowadays, with the spread of imaging and examination devices, digital images have become ubiquitous. Satellite and radar images are of particular importance due to their diverse applications. In meteorology, where image resolution and pixel accuracy are critical for accurate rainfall measurements, the use of lossy compression can degrade image quality and distort pixel value, leading to inaccurate results. Hence, maintaining image resolution through lossless compression is essential to maintain the reliability of weather data and ensure the accuracy of forecasts and analyses. Satellite images are often very large, posing significant challenges for their storage and transmission. Image compression addresses this problem using lossless techniques that allow for perfect reconstruction of the original image. Therefore, our study uses a Huffman coding algorithm and two types of predictive coding which are error coding and facsimile coding. For the satellite images, predictive coders achieve a higher compression ratio than the Huffman coder and the compressed bit rate can even drop below the entropy limit. Moreover, and due to the homogenous zones of pixels with the same intensity in the radar image, the facsimile predictive coder generated the lower bit rate than the other coders in relatively shorter time.
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References
R.C. Gonzalez, and R.E. Woods, “Digital Image Processing,” 4th ed.. New York: Pearson Education, 2018.
S. Sehgal, L. Ahuja, and M. H. Bindu, “High Resolution Satellite Image Compression using DCT and EZW,” Int. Conf. on Sustainable Computing in Science, Technology and Management, Amity Univ. Rajasthan, Jaipur – India, pp. 112-115, 2019.
K.S. Gunasheela, H.S. Prasantha, “ Satellite Image Compression-Detailed Survey of the Algorithms,” In Proc. of Int. Conf. on Cognition and Recognition, Singapore : Springer , vol. 14, pp. 187–198, 2018.
S. Dhawan, “A Review of Image Compression and Comparison of its Algorithms,” Int. Journal of Electronics & Communication Technology (IJECT), vol. 2, no. 1 , pp. 22-26, Mar. 2011.
A. Indradjad, A. Nasution, H. Gunawan, and A. Widipaminto, “A comparison of Satellite Image Compression methods in the Wavelet Domain,” IOP Conf. Series: Earth and Environmental Science, 2019.
A. Hussain, A. Al-Fayadh, and N. Radi, “Image Compression Techniques: A Survey in Lossless and Lossy algorithms,” Neurocomputing, vol. 300, no. 1, pp. 44-69, 2018.
K. Sahnoun, and N. Benabadji, “On-board Satellite Image Compression Using the Fourier Transform and Huffman Coding,” The Int. Journal of Computational Science, Information Technology and Control Engineering, vol. 1, no. 1, pp. 17-23, 2014.
D.S. Taubman, and M.W. Marcellin, “JPEG2000: image compression fundamentals, standards, and practice,” New York: Springer, 2002.
G. Held, and T. Marshall, “Data and Image Compression: tools and techniques,” New Jersey: John Wiley & Sons, 1996.
Y.Q. Shi, and H. Sun, “Image and Video Compression for Multimedia Engineering: fundamentals, algorithms and standards,” Boca Raton: CRC Press LLC, 2000.
K. Sayood, “Introduction to Data Compression,” 3rd ed., Burlington: Morgan Kaufmann Publishers, 2006.
G.E. Blelloch, “Introduction to Data Compression,” Computer Science Department, Carnegie Mellon University, Pittsburgh, Pennsylvania, Oct.2001.
T. Acharya and P. S. Tsai, “JPEG2000 Standard for Image Compression: Concepts, Algorithms and VLSI Architectures,” John Wiley & Sons, New York, Oct. 2004.
M. Schindler, (1998), “Practical Huffman coding,” Compression consulting Schindler, http://www.compressconsult.com/huffman/.
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