Cover
Vol. 19 No. 2 (2023)

Published: December 31, 2023

Pages: 128-137

Original Article

Separate and Combined Effective Coding of Bit Planes of Grayscale Images

Oday Jasim Mohammed Al-Furaiji 1 Viktar Yurevich Tsviatkou 2 Baqir Jafar Sadiq 1
1Computer Science Department, Shatt Al-Arab University College, Basra, Iraq
2Department of Infocommunication Technologies, Belarusian State University of Informatics
History
  • Received: March 27, 2023
  • Revised: May 7, 2023
  • Accepted: May 19, 2023
  • Available Online: July 19, 2023
DOI

https://doi.org/10.37917/ijeee.19.2.15

Abstract

Currently, an approach involving a coder with a combined structure for compressing images combining several different coders, the system for connecting them to various bit planes, and the control system for these connections have not been studied. Thus, there is a need to develop a structure and study the effectiveness of a combined codec for compressing images of various types without loss in the spatial domain based on arithmetic and (Run-Length Encoding) RLE-coding algorithms. The essence of separate effective coding is to use independent coders of the same type or one coder connected to the planes alternately in order to compress the higher and lower bit planes of the image or their combinations. In this paper, the results of studying the effectiveness of using a combination of arithmetic and RLE coding for several types of images are presented. As a result of developing this structure, the effectiveness of combined coding for compressing the differences in the channels of hyperspectral images (HSI) has been established, as hyperspectral images consist of multi-spectral bands, instead of just the typical three bands (RGB) or (YCbCr) found in regular images. Where, each pixel in a hyperspectral image represents the entire spectrum of light reflected by the object or scene at that particular location.

Keywords

References

  1. H. Irmak, G. B. Akar, and S. E. Y. uksel, “Image fusion for hyperspectral image super-resolution,” in 2018 9th Workshop on Hyperspectral Image and Signal Process- ing: Evolution in Remote Sensing (WHISPERS), pp. 1–5, 2018.
  2. O. Ozdil, A. Gunes, Y. E. Esin, S. Ozturk, and B. Demirel, “4-stage target detection approach in hy- perspectral images,” in 2018 9th Workshop on Hyper- spectral Image and Signal Processing: Evolution in Remote Sensing (WHISPERS), pp. 1–5, 2018.
  3. M. Zhao, C. Yu, M. Song, and C.-I. Chang, “A seman- tic feature extraction method for hyperspectral image classification based on hashing learning,” in 2018 9th Workshop on Hyperspectral Image and Signal Process- ing: Evolution in Remote Sensing (WHISPERS), pp. 1–5, 2018.
  4. F. Arias, H. Sierra, and E. Arzuaga, “A framework for an artificial neural network enabled single pixel hyperspec- tral imager,” in 2019 10th Workshop on Hyperspectral Imaging and Signal Processing: Evolution in Remote Sensing (WHISPERS), pp. 1–5, 2019.
  5. O. J. M. Al-Furaiji, “Resolution enhancement of im- ages pair based on block cross interpolation,” Periodi- cals of Engineering and Natural Sciences, vol. 8, no. 2, pp. 1067–1074, 2020.
  6. O. J. M. Al-Furaiji, V. V. Rabtsevich, V. Y. Tsviatkou, T. A. Kuznetsova, and S. A. Chizhik, “Segmentation of afm-images based on wave region growing of local maxima,” Engineering Letters, vol. 28, no. 3, pp. 681– 698, 2020.
  7. S. Shirani, “Data compression: The complete reference (by d. salomon; 2007)[book review],” IEEE Signal Pro- cessing Magazine, vol. 25, no. 2, pp. 147–149, 2008.
  8. V. Bhaskaran and K. Konstantinides, Image and video compression standards: algorithms and architectures. Springer Science & Business Media, 1997.
  9. R. C. Gonzalez, Digital image processing. Pearson education india, 2009.
  10. J. Ziv and A. Lempel, “A universal algorithm for sequen- tial data compression,” IEEE Transactions on informa- tion theory, vol. 23, no. 3, pp. 337–343, 1977.
  11. D. S. Taubman, M. W. Marcellin, and M. Rabbani, “Jpeg2000: Image compression fundamentals, standards and practice,” Journal of Electronic Imaging, vol. 11, no. 2, pp. 286–287, 2002.
  12. J. M. Shapiro, “Embedded image coding using zerotrees of wavelet coefficients,” IEEE Transactions on signal processing, vol. 41, no. 12, pp. 3445–3462, 1993. 135 | Al-Furaiji, Tsviatkou & Sadiq
  13. Y. Xu and J. Zhang, “Invertible resampling-based lay- ered image compression,” in 2021 Data Compression Conference (DCC), pp. 380–380, IEEE, 2021.
  14. E. Abderraouf, M. R. Lahcene, M. S. Bendelhoum, S. A. Zegnoun, A. A. Tadjeddine, and F. Menezla, “Transmis- sion performance in compressed medical images using turbo code,” Indonesian Journal of Electrical Engineer- ing and Computer Science, vol. 27, no. 1, pp. 318–327, 2022.
  15. D. Sivaraman, J. Jebanazer, and B. Balasubramanian, “Discriminative analysis of wavelets for efficient medi- cal image compression,” Indonesian Journal of Electri- cal Engineering and Computer Science, vol. 30, no. 1, pp. 510–517, 2023.
  16. Z. Abood, S. Abood, and T. Ismaeel, “Comparison hy- brid techniques-based mixed transform using compres- sion and quality metrics,” Indonesian Journal of Electri- cal Engineering and Computer Science, vol. 30, no. 2, pp. 807–816, 2023.
  17. A. Said and 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, no. 3, pp. 243–250, 1996.
  18. A. Islam and W. Pearlman, “Set partitioned sub-block coding (speck),” ISO/IEC/JTC1/SC29, WG1, no. 873, pp. 312–326, 1998.
  19. H. K. Al-Bahadily, A. A. J. Altaay, V. Tsviatkou, and V. Konopelko, “New modified RLE algorithms to com- press grayscale images with lossy and lossless compres- sion,” International Journal of Advanced Computer Sci- ence and Applications, vol. 7, no. 7, pp. 250–255, 2016.
  20. N. Abramson, Information Theory and Coding. McGraw Hill; First Edition, 1963.
  21. M. K. Abdmouleh, A. Masmoudi, and M. S. Bouhlel, “A new method which combines arithmetic coding with RLE for lossless image compression,” Journal of Software Engineering and Applications, no. 5, 2012.
  22. S. Golomb, “Run-length encodings (corresp.),” IEEE transactions on information theory, vol. 12, no. 3, pp. 399–401, 1966.
  23. F. Rubin, “Arithmetic stream coding using fixed preci- sion registers,” IEEE Transactions on Information The- ory, vol. 25, no. 6, pp. 672–675, 1979.
  24. [NULL], “AVIRIS - Airborne Visible / Infrared Imaging Spectrometer - Data — aviris.jpl.nasa.gov.” https:// aviris.jpl.nasa.gov/data/index.html. [Accessed 16-Jul-2023].