ANALYSIS OF THE POSSIBILITY OF USING THE SINGULAR VALUE DECOMPOSITION IN IMAGE COMPRESSION

Edyta ŁUKASIK

e.lukasik@pollub.pl
Department of Computer Science, Faculty of Electrical Engineering and Computer Science, Lublin University of Technology, Lublin, (Poland)

Emilia ŁABUĆ


Department of Computer Science, Faculty of Electrical Engineering and Computer Science, Lublin University of Technology, Lublin (Poland)

Abstract

In today’s highly computerized world, data compression is a key issue to minimize the costs associated with data storage and transfer. In 2019, more than 70% of the data sent over the network were images. This paper analyses the feasibility of using the SVD algorithm in image compression and shows that it improves the efficiency of JPEG and JPEG2000 compression. Image matrices were decomposed using the SVD algorithm before compression. It has also been shown that as the image dimensions increase, the fraction of eigenvalues that must be used to reconstruct the image in good quality decreases. The study was carried out on a large and diverse set of images, more than 2500 images were examined. The results were analyzed based on criteria typical for the evaluation of numerical algorithms operating on matrices and image compression: compression ratio, size of compressed file, MSE, number of bad pixels, complexity, numerical stability, easiness of implementation. 


Keywords:

singular value decomposition, JPEG2000, JPEG, discrete wavelet transform, discrete cosine transform

Anutam, & Rajni. (2014). Comparative Analysis of Filters and Wavelet Based Thresholding Methods for Image Denoising. Computer Science & Amp; Information Technology (CS &Amp; IT ) (pp. 137–148). https://doi.org/10.5121/csit.2014.4515
DOI: https://doi.org/10.5121/csit.2014.4515   Google Scholar

Arps, R., & Truong, T. (1994). Comparison of international standards for lossless still image compression. Proceedings of the IEEE, 82(6), 889–899. https://doi.org/10.1109/5.286193
DOI: https://doi.org/10.1109/5.286193   Google Scholar

Bovik, A. C. (2009). The Essential Guide to Image Processing (1st ed.). Academic Press.
DOI: https://doi.org/10.1016/B978-0-12-374457-9.00001-9   Google Scholar

Britanak, V., Yip, P. C., & Rao, K. (2007). CHAPTER 4 – Fast DCT/DST Algorithms. Discrete Cosine and Sine Transforms. General Properties, Fast Algorithms and Integer Approximations (pp. 73–140). Academic Press. https://doi.org/10.1016/b978-012373624-6/50006-0
DOI: https://doi.org/10.1016/B978-012373624-6/50006-0   Google Scholar

Cao, L. (2006). SVD applied to digital image processing. Division of Computing Studies, Arizona State University Polytechnic Campus.
  Google Scholar

Chen, Y., Mukherjee, D., Han, J., Grange, A., Xu, Y., Parker, S., Chen, C., Su, H., Joshi, U., Chiang, C. H., Wang, Y., Wilkins, P., Bankoski, J., Trudeau, L., Egge, N., Valin, J. M., Davies, T., Midtskogen, S., Norkin, A., de Rivaz, P., Design, A., & Liu, Z. (2020). An Overview of Coding Tools in AV1: the First Video Codec from the Alliance for Open Media. APSIPA Transactions on Signal and Information Processing, 9(1), e6. https://doi.org/10.1017/atsip.2020.2
DOI: https://doi.org/10.1017/ATSIP.2020.2   Google Scholar

Compton, E. A., & Ernstberger, S. L. (2020). Singular Value Decomposition: Applications to Image Processing. Lagrange College. Journal of Undergraduate Research, 17, 99–105.
  Google Scholar

Cormen, T. H., Leiserson, C. E., Rivest, R. L., & Stein, C. (2005). Wprowadzenie do algorytmów. Wydawnictwo Naukowe PWN.
  Google Scholar

Davies, E. R. (2017). Computer Vision: Principles, Algorithms, Applications, Learning. Elsevier Gezondheidszorg. Dhawan, S. (2011). A Review of Image Compression and Comparison of its Algorithms. https://www.semanticscholar.org/paper/A-Review-of-Image-Compression-and-Comparison-of-itsDhawan/034dcf50d99bbd9870c5c2e67201f6d792f96a5f
  Google Scholar

Dumka, A., Ashok, A., Verma, P., & Verma, P. (2020). Advanced Digital Image Processing and Its Applications in Big Data (1st ed.). CRC Press.
DOI: https://doi.org/10.1201/9780429351310   Google Scholar

Gandhi, T., Patel, H., & Prajapati, D. (2015). Image Compression Using Fractal: Image compression based upon the self-similarities present in the image. LAP LAMBERT Academic Publishing.
  Google Scholar

Gong, L., Deng, C., Pan, S., & Zhou, N. (2018). Image compression-encryption algorithms by combining hyper-chaotic system with discrete fractional random transform. Optics &Amp; Laser Technology, 103, 48–58. https://doi.org/10.1016/j.optlastec.2018.01.007
DOI: https://doi.org/10.1016/j.optlastec.2018.01.007   Google Scholar

Hoffman, R. (1997). Data Compression in Digital Systems. Springer Publishing.
DOI: https://doi.org/10.1007/978-1-4615-6031-9   Google Scholar

Hoffman, R. (2012). Data Compression in Digital Systems. Springer Publishing.
  Google Scholar

Jackson, D., & Hannah, S. (1993). Comparative analysis of image compression techniques. 1993 (25th) Southeastern Symposium on System Theory (pp. 513–517). IEEE. https://doi.org/10.1109/ssst.1993.522833
DOI: https://doi.org/10.1109/SSST.1993.522833   Google Scholar

Jankowska, J., & Jankowski, M. (1988). Przegląd metod i algorytmów numerycznych. Wydawnictwa Naukowo-Techniczne.
  Google Scholar

Jinchuang, Z., Yan, T., & Wenli, F. (2009). Research of image compression based on Wireless visual sensor networks. 4th International Conference on Computer Science & Education (pp. 353–356). IEEE. https://doi.org/10.1109/iccse.2009.5228430
DOI: https://doi.org/10.1109/ICCSE.2009.5228430   Google Scholar

Karwowski, D. (2019). Zrozumieć kompresję obrazu: podstawy technik kodowania stratnego oraz bezstratnego obrazów. Damian Karwowski.
  Google Scholar

Kostrikin, A. I. (2004). Wstęp do algebry cz. 1 i cz. 2 Podstawy algebry. Wydawnictwo Naukowe PWN.
  Google Scholar

Lu, Z., & Guo, S. (2016). Lossless Information Hiding in Images (1st ed.). Syngress.
DOI: https://doi.org/10.1016/B978-0-12-812006-4.00001-2   Google Scholar

Mammeri, A., Hadjou, B., & Khoumsi, A. (2012). A Survey of Image Compression Algorithms for Visual Sensor Networks. International Scholarly Research Notices, 2012, 760320. https://doi.org/10.5402/2012/760320
DOI: https://doi.org/10.5402/2012/760320   Google Scholar

Miano, J. (1999). Compressed Image File Formats: JPEG, PNG, GIF, XBM, BMP. Addison-Wesley Professional.
  Google Scholar

Murray, J. D., & VanRyper, W. (1996). Encyclopedia of Graphics File Formats. O’Reilly & Associates.
  Google Scholar

Nasri, M., Helali, A., Sghaier, H., & Maaref, H. (2010). Energy-efficient wavelet image compression in Wireless Sensor Network. 2010 International Conference on Wireless and Ubiquitous Systems (pp. 1–7). IEEE. https://doi.org/10.1109/icwus.2010.5670430
DOI: https://doi.org/10.1109/ICWUS.2010.5670430   Google Scholar

Nixon, M., & Aguado, A. (2019). Feature Extraction and Image Processing for Computer Vision (4th ed.). Academic Press.
DOI: https://doi.org/10.1016/B978-0-12-814976-8.00003-8   Google Scholar

Parekh, D. (2021, April 25). Image Compression Standards | Digital Image Processing [Video file]. YouTube. https://www.youtube.com/watch?v=6IuKH7IGspU
  Google Scholar

Pratt, W., Kane, J., & Andrews, H. (1969). Hadamard transform image coding. Proceedings of the IEEE, 57(1), 58–68. https://doi.org/10.1109/proc.1969.6869
DOI: https://doi.org/10.1109/PROC.1969.6869   Google Scholar

Pu, I. M. (2005). Fundamental Data Compression (1st ed.). Butterworth-Heinemann.
DOI: https://doi.org/10.1016/B978-075066310-6/50004-0   Google Scholar

Salomon, D., Motta, G., & Bryant, D. (2007). Data Compression: The Complete Reference. Springer Publishing.
  Google Scholar

Sayood, K. (2002). Lossless Compression Handbook. Elsevier Gezondheidszorg.
DOI: https://doi.org/10.1201/9781420041163-101   Google Scholar

Shih, C. W., Chu, H. C., Chen, Y. M., & Wen, C. C. (2012). The effectiveness of image features based on fractal image coding for image annotation. Expert Systems With Applications, 39(17), 12897–12904. https://doi.org/10.1016/j.eswa.2012.05.003
DOI: https://doi.org/10.1016/j.eswa.2012.05.003   Google Scholar

Short, M. N., Manohar, M., & Tilton, J. C. (1994). Planning/Scheduling Techniques for VQ-Based Image Compression. Science Information Management and Data Compression Workshop. 1994 Science Information Management and Data Compression Workshop (pp. 95–104). US Government.
  Google Scholar

Shukla, K. K., & Prasad, M. V. (2011). Lossy Image Compression: Domain Decomposition-Based Algorithms. Springer Publishing.
DOI: https://doi.org/10.1007/978-1-4471-2218-0   Google Scholar

Stewart, G. W. (2001). Matrix Algorithms: Volume 2, Eigensystems (1st ed.). SIAM: Society for Industrial and Applied Mathematics.
DOI: https://doi.org/10.1137/1.9780898718058   Google Scholar

Swathi, H. R., Sohini, S., Surbhi, & Gopichand, G. (2017). Image compression using singular value decomposition. IOP Conference Series: Materials Science and Engineering, 263, 042082. https://doi.org/10.1088/1757-899x/263/4/042082
DOI: https://doi.org/10.1088/1757-899X/263/4/042082   Google Scholar

Tanwar, S., Ramani, T., & Tyagi, S. (2018). Dimensionality Reduction Using PCA and SVD in Big Data: A Comparative Case Study. Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering (pp. 116–125). Springer. https://doi.org/10.1007/978-3-319-73712-6_12
DOI: https://doi.org/10.1007/978-3-319-73712-6_12   Google Scholar

Wayner, P. (1999). Compression Algorithms for Real Programmers. Elsevier Gezondheidszorg. What’s the difference between ‘visually lossless’ and real lossless and what does this mean for future encodes? (2019, May 18). Video Production Stack Exchange. Retrieved May 2022 from https://video.stackexchange.com/questions/27656/whats-the-difference-between-visually-lossless-andreal-lossless-and-what-doe
  Google Scholar

Xiao, F., Zhang, P., Sun, L. J., Wang, J., & Wang, R. C. (2011). Research on image compression and transmission mechanism for wireless multimedia sensor networks. 2011 International Conference on Electrical and Control Engineering (pp. 788–791). IEEE. https://doi.org/10.1109/iceceng.2011.6057601
DOI: https://doi.org/10.1109/ICECENG.2011.6057601   Google Scholar

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Published
2022-12-03

Cited by

ŁUKASIK, E., & ŁABUĆ, E. (2022). ANALYSIS OF THE POSSIBILITY OF USING THE SINGULAR VALUE DECOMPOSITION IN IMAGE COMPRESSION. Applied Computer Science, 18(4), 53–67. https://doi.org/10.35784/acs-2022-28

Authors

Edyta ŁUKASIK 
e.lukasik@pollub.pl
Department of Computer Science, Faculty of Electrical Engineering and Computer Science, Lublin University of Technology, Lublin, Poland

Authors

Emilia ŁABUĆ 

Department of Computer Science, Faculty of Electrical Engineering and Computer Science, Lublin University of Technology, Lublin Poland

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