Image Compression Using Permanent Neural Networks for Predicting Compact Discrete Cosine Transform Coefficients
DOI:
https://doi.org/10.46604/ijeti.2021.6925Keywords:
image compression, discrete cosine transform, neural networksAbstract
This study proposes a new image compression technique that produces a high compression ratio yet consumes low execution times. Since many of the current image compression algorithms consume high execution times, this technique speeds up the execution time of image compression. The technique is based on permanent neural networks to predict the discrete cosine transform partial coefficients. This can eliminate the need to generate the discrete cosine transformation every time an image is compressed. A compression ratio of 94% is achieved while the average decompressed image peak signal to noise ratio and structure similarity image measure are 22.25 and 0.65 respectively. The compression time can be neglected when compared to other reported techniques because the only needed process in the compression stage is to use the generated neural network model to predict the few discrete cosine transform coefficients.
References
C. G. Bampis, Z. Li, I. Katsavounidis, T. Y. Huang, C. Ekanadham, and A. C. Bovik, “Towards Perceptually Optimized End-To-End Adaptive Video Streaming,” Arxiv E-prints, arXiv:1808.03898v1, 2018.
M. Tawalbeha, A. Eardley, and L. Tawalbeh, “Studying the Energy Consumption in Mobile Devices,” Procedia Computer Science, vol. 94, pp. 183-189, 2016.
“Apple X ®,” https://www.apple.com/iphone-xr/specs/, April 12, 2020.
R. C. Gonzalez and R. E. Woods, Digital Image Processing, 3rd ed. Upper Saddle River: Pearson Education, Inc., 2008.
K. Pathak, R. V. Arjunan, and V. Acharya, “An Innovative Lossless Image and Video Compression Using Revised S Transformation,” Journal of Advanced Research in Dynamical and Control System, vol. 11, no. 4, pp. 14-24, 2019.
S. UmaMaheswari and V. SrinivasaRaghavan, “Lossless Medical Image Compression Algorithm Using Tetrolet Transformation,” Journal of Ambient Intelligence and Humanized Computing, in press.
A. M. Raid, W. M. Khedr, M. A. El-Dosuky, and W. Ahmed, “JPEG Image Compression Using Discrete Cosine Transform-A Survey,” International Journal of Computer Science & Engineering Survey, vol. 5, no. 2, pp. 39-47, April 2014.
N. Ahmed, “How I Came Up with the Discrete Cosine Transform,” Digital Signal Processing, vol. 1, no. 1, pp. 4-5, January 1991.
J. G. Daugman, “Complete Discrete 2-D Gabor Transforms by Neural Networks for Image Analysis and Compression,” IEEE Transactions on Acoustics, Speech, and Signal Processing, vol. 36, no. 7, pp. 1169-1179, July 1988.
C. Amerijckx, M. Verleysen, P. Thissen, and J. D. Legat, “Image Compression by Self-Organized Kohonen Maps,” IEEE Transactions on Neural Networks, vol. 9, no. 3, pp. 503-507, May 1998.
D. P. Dutta, S. D. Choudhury, M. A. Hussain, and S. Majumder, “Digital Image Compression Using Neural Networks,” International Conference on Advances in Computing, Control, and Telecommunication Technologies, December 2009, pp. 116-120.
J. Robinson and V. Kecman, “Combining Support Vector Machine Learning with the Discrete Cosine Transform in Image Compression,” IEEE Transactions on Neural Networks, vol. 14, no. 4, pp. 950-958, July 2003.
R. D. Dony and S. Haykin, “Neural Network Approaches to Image Compression,” Proceedings of the IEEE, vol. 83, no. 2, pp. 288-303, February 1995.
V. R. P. Vaddella and K. Rama, “Artificial Neural Networks for Compression of Digital Images: A Review,” International Journal of Reviews in Computing, vol. 3, pp. 75-82, June 2010.
S. A. Alshehri, “Video Compression Using Frame Redundancy Elimination and Discrete Cosine Transform Coefficient Reduction,” Multimedia Tools and Applications, vol. 80, no. 1, pp. 367-381, January 2021.
K. Dimililer, “Backpropagation Neural Network Implementation for Medical Image Compression,” Journal of Applied Mathematics, vol. 2013, 453098, December 2013.
B. Patel and S. Agrawal, “Image Compression Techniques Using Artificial Neural Network,” International Journal of Advanced Research in Computer Engineering and Technology, vol. 2, no. 10, pp. 2725-2729, October 2013.
V. Mehare and S. Shibu, “A Neural Network Approach to Improve the Lossless Image Compression Ratio,” Peoples Journal of Science & Technology, vol. 2, no. 1, pp. 53-58, January-June 2012.
P. Karthikeyan and N. Sreekumar, “A Study on Image Compression with Neural Networks Using Modified Levenberg-Marquardt Method,” Global Journal of Computer Science and Technology, vol. 11, no. 3, pp. 1-5, March 2011.
S. Alshehri, “Neural Network Technique for Image Compression,” IET Image Processing, vol. 10, no. 3, pp. 222-226, March 2016.
S. Alshehri, “English Characters OCR Pertinent for Mobile Devices,” International Journal of Computing and Digital Systems, vol. 10, no. 1, pp. 135-141, 2021.
S. Ma, X. Zhang, C. Jia, Z. Zhao, S. Wang, and S. Wang, “Image and Video Compression with Neural Networks: A Review,” IEEE Transactions on Circuits and Systems for Video Technology, vol. 30, no. 6, pp. 1683-1698, June 2020.
G. Schaefer and M. Stich, “UCID: An Uncompressed Color Image Database,” Storage and Retrieval Methods and Applications for Multimedia, vol. 5307, pp. 472-480, December 2003.
C. M. Bishop, Neural Networks for Pattern Recognition, New York: Oxford University Press, Inc., 1997.
A. Horé and D. Ziou, “Image Quality Metrics: PSNR vs. SSIM,” 20th International Conference on Pattern Recognition, August 2010, pp. 2366-2369.
C. Rawat and S. Meher, “A Hybrid Image Compression Scheme Using DCT and Fractal Image Compression,” International Arab Journal of Information Technology, vol. 10, no. 6, pp. 553-562, November 2013.
S. Thayammal and D. Selvathi, “Edge Preserved Image Compression Using Extended Shearlet Transform,” Journal of Computer Science, vol. 11, no. 1, pp. 82-88, 2015.
A. Said and W. A. 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, June 1996.
M. Santhi and R. W. Banu, “Enhancing the Color Set Partitioning in Hierarchical Tree (SPIHT) Algorithm Using Correlation Theory,” Journal of Computer Science, vol. 7, no. 8, pp. 1204-1211, 2011.
Published
How to Cite
Issue
Section
License
Copyright Notice
Submission of a manuscript implies: that the work described has not been published before that it is not under consideration for publication elsewhere; that if and when the manuscript is accepted for publication. Authors can retain copyright in their articles with no restrictions. Also, author can post the final, peer-reviewed manuscript version (postprint) to any repository or website.
Since Jan. 01, 2019, IJETI will publish new articles with Creative Commons Attribution Non-Commercial License, under Creative Commons Attribution Non-Commercial 4.0 International (CC BY-NC 4.0) License.
The Creative Commons Attribution Non-Commercial (CC-BY-NC) License permits use, distribution and reproduction in any medium, provided the original work is properly cited and is not used for commercial purposes.
.jpg)
