Page 138 - 2023-Vol19-Issue2
P. 138
134 | Al-Furaiji, Tsviatkou & Sadiq
TABLE IV.
THE MOST EFFECTIVE COMBINATIONS OF ARITHMETIC
AND RLE CODERS AND COMPRESSION RATIOS
Combinations of arithmetic and RLE coders
Bit planes for differences of HSI channels
Difference 1 Difference 2 Difference 3 Difference 4
15|7 RLE RLE AC RLE AC RLE
14|6 RLE RLE RLE RLE RLE RLE AC
13|5 RLE RLE RLE RLE RLE AC RLE
12|4 RLE AC RLE RLE RLE NC
11|3 RLE RLE AC RLE RLE NC
10|2 RLE NC RLE RLE NC RLE NC
9|1 RLE NC RLE NC RLE NC RLE NC
8|0 RLE NC RLE NC RLE NC NC
CRC 3.72917 3.16748 3.15484 2.80657
CRAC 2.53492 2.29310 2.16147 2.18485
CRRLE 1.02866 0.97902 1.04260 1.03313
CRC 2.46175 3.16773 3.07913 1.93187
is developed. [5] O. J. M. Al-Furaiji, “Resolution enhancement of im-
ages pair based on block cross interpolation,” Periodi-
CONFLICT OF INTEREST cals of Engineering and Natural Sciences, vol. 8, no. 2,
pp. 1067–1074, 2020.
The authors have no conflict of relevant interest to this article.
[6] O. J. M. Al-Furaiji, V. V. Rabtsevich, V. Y. Tsviatkou,
REFERENCES T. A. Kuznetsova, and S. A. Chizhik, “Segmentation
of afm-images based on wave region growing of local
[1] H. Irmak, G. B. Akar, and S. E. Y. uksel, “Image fusion maxima,” Engineering Letters, vol. 28, no. 3, pp. 681–
for hyperspectral image super-resolution,” in 2018 9th 698, 2020.
Workshop on Hyperspectral Image and Signal Process-
ing: Evolution in Remote Sensing (WHISPERS), pp. 1–5, [7] S. Shirani, “Data compression: The complete reference
2018. (by d. salomon; 2007)[book review],” IEEE Signal Pro-
cessing Magazine, vol. 25, no. 2, pp. 147–149, 2008.
[2] O. Ozdil, A. Gunes, Y. E. Esin, S. Ozturk, and
B. Demirel, “4-stage target detection approach in hy- [8] V. Bhaskaran and K. Konstantinides, Image and video
perspectral images,” in 2018 9th Workshop on Hyper- compression standards: algorithms and architectures.
spectral Image and Signal Processing: Evolution in Springer Science & Business Media, 1997.
Remote Sensing (WHISPERS), pp. 1–5, 2018.
[9] R. C. Gonzalez, Digital image processing. Pearson
[3] M. Zhao, C. Yu, M. Song, and C.-I. Chang, “A seman- education india, 2009.
tic feature extraction method for hyperspectral image
classification based on hashing learning,” in 2018 9th [10] J. Ziv and A. Lempel, “A universal algorithm for sequen-
Workshop on Hyperspectral Image and Signal Process- tial data compression,” IEEE Transactions on informa-
ing: Evolution in Remote Sensing (WHISPERS), pp. 1–5, tion theory, vol. 23, no. 3, pp. 337–343, 1977.
2018.
[11] D. S. Taubman, M. W. Marcellin, and M. Rabbani,
[4] F. Arias, H. Sierra, and E. Arzuaga, “A framework for an “Jpeg2000: Image compression fundamentals, standards
artificial neural network enabled single pixel hyperspec- and practice,” Journal of Electronic Imaging, vol. 11,
tral imager,” in 2019 10th Workshop on Hyperspectral no. 2, pp. 286–287, 2002.
Imaging and Signal Processing: Evolution in Remote
Sensing (WHISPERS), pp. 1–5, 2019. [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.