Compression Coding Method Using Internal Restructuring of Information Space

Authors

  • Dmitro Karlov
  • Ivan Tupitsya
  • Maxim Parkhomenko
  • Oleksandr Musienko
  • Albert Lekakh

DOI:

https://doi.org/10.47839/ijc.21.3.2692

Keywords:

video information resource, restructuring, quantitative sign, coding, reliability, communication channel

Abstract

The subject of the study in this article is data transmission processes of the video information resource in the information communication systems of the air segment under the conditions of errors in the data transmission channel. The purpose of the article is the development of the method of compression coding in order to ensure an increase in the level of reliability of video information resources under the conditions of errors in communication channels. The following tasks are identified: to develop a method of compression coding using structural decomposition of statistical space; analyze the effectiveness of the developed method from the standpoint of ensuring the required level of reliability. The following results are obtained: the developed method of encoding video information allows increasing the level of reliability in the conditions of the transmission of video information resources in the information communication systems of the air segment due to the localization of the action of errors.

References

W. K. Pratt, W. H. Chen, L. R. Welch, “Slant transforms image coding,” Proceedings of the Computer Processing in Communications, 1969, рр. 63 84.

G. K. Wallace, “The jpeg still picture compression standard,” Communication in ACM, vol. 34, no. 4, рр. 31-34, 1991. https://doi.org/10.1145/103085.103089.

G. K. Wallace, “Overview of the JPEG (ISO/CCITT) still image compression: image processing algorithms and techniques,” Proceedings of the of SPIE-IS&T Electronic Imaging (SPIE), 1990, vol. 1244, рp. 220-233. https://doi.org/10.1117/12.19537.

S. Wang, X. Zhang, X. Liu, J. Zhang, S. Ma, W. Gao, “Utility driven adaptive preprocessing for screen content video compression,” IEEE Transactions on Multimedia, vol. 19, no. 3, pp. 660-667, 2017. https://doi.org/10.1109/TMM.2016.2625276.

R. C. Gonzales, R. E. Woods, Digital Image Processing, Prentice Inc. Upper Saddle River, 2002. 779 p.

W. Dong, J. Wang, “JPEG compression forensics against resizing,” Proceedings of the IEEE Trustcom/ BigDataSE/IвSPA, Tianjin, China, 2016, pp. 1001-1007. https://doi.org/10.1109/TrustCom.2016.0168.

T. Richter, “Error bounds for HDR image coding with JPEG XT,” Proceedings of the 2017 IEEE Data Compression Conference (DCC), 2017, pp. 122-130. https://doi.org/10.1109/DCC.2017.7.

W. Xiao, N. Wan, A. Hong and X. Chen, “A fast JPEG image compression algorithm based on DCT,” IEEE International Conference on Smart Cloud (SmartCloud), 2020, pp. 106-110. https://doi.org/10.1109/SmartCloud49737.2020.00028.

O. Rippel, “Learned video compression,” Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV), 2019, pp. 3453-3462. https://doi.org/10.1109/ICCV.2019.00355.

X. Wang, J. Xiao, R. Hu, Z. Wang, “Cruise UAV video compression based on long-term wide-range background,” Data Compression Conference (DCC), 2017, pp. 466-467. https://doi.org/10.1109/DCC.2017.71.

A. Djelouah, J. Campos, S. Schaub-Meyer, C. Schroers, “Neural inter-frame compression for video coding,” Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV), 2019, pp. 6420-6428. https://doi.org/10.1109/ICCV.2019.00652.

C. Narmatha, P. Manimegalai, S. Manimurugan, “A LS-compression scheme for grayscale images using pixel based technique,” Proceedings of the International Conference on Innovations in Green Energy and Healthcare Technologies (IGEHT), 2017, pp. 1-5, https://doi.org/10.1109/IGEHT.2017.8093980.

M. A. Alam, “Faster image compression technique based on LZW algorithm using GPU parallel processing,” Proceedings of the joint 7th International Conference on Informatics, Electronics & Vision (ICIEV) and 2nd International Conference on Imaging, Vision & Pattern Recognition (icIVPR), 2018, pp. 272-275, https://doi.org/10.1109/ICIEV.2018.8640956.

T. Shinde, “Efficient image set compression,” Proceedings of the IEEE International Conference on Image Processing (ICIP), 2019, pp. 3016-3017, https://doi.org/10.1109/ICIP.2019.8803230.

J. Lin, D. Liu, H. Li, F. Wu, “M-LVC: Multiple frames prediction for learned video compression,” Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2020, pp. 3543-3551. https://doi.org/10.1109/CVPR42600.2020.00360.

F. Artuğer, F. Özkaynak, “Fractal image compression method for lossy data compression,” Proceedings of the International Conference on Artificial Intelligence and Data Processing (IDAP), 2018, pp. 1-6. https://doi.org/10.1109/IDAP.2018.8620735.

R. Swaminathan, A. Madhukumar, “Classification of error correcting codes and estimation of interleaver parameters in a noisy transmission environment,” IEEE Transactions on Broadcasting, vol. 63, no. 3, pp. 463-478, 2017. https://doi.org/10.1109/TBC.2017.2704436.

X. Zhu, L. Liu, P. Jin, N. Ai, “Morphological component decomposition combined with compressed sensing for image compression,” Proceedings of the IEEE International Conference on Information and Automation (ICIA), https://doi.org/10.1109/ICInfA.2016.7832096.

S. Wang, S. M. Kim, Z. Yin, & T. He, “Encode when necessary: Correlated network coding under unreliable wireless links,” ACM Transactions on Sensor Networks, vol. 13, issue 1, article 7, pp. 1-22, 2017. https://doi.org/10.1145/3023953.

Y. Yehezkeally, M. Schwartz, “Limited-magnitude error-correcting gray codes for rank modulation,” IEEE Transactions on Information Theory, vol. 63, no. 9, pp. 5774-5792, 2017. https://doi.org/10.1109/TIT.2017.2719710.

S. Han, H. Mao, W. Dally, “Deep compression: Compressing deep neural networks with pruning, trained quantization and Huffman coding,” 2015. arXiv: 1510.00149.

Y. Chen, F. Wu, C. Li, P. Varshney, “An efficient construction strategy for near-optimal variable-length error-correcting codes,” IEEE Communications Letters, vol. 23, no. 3, pp. 398-401, 2019. https://doi.org/10.1109/LCOMM.2019.2891623.

A. Phatak, “A non-format compliant scalable RSA-based JPEG encryption algorithm,” International Journal of Image. Graphics and Signal Processing, vol. 8, no. 6, pp. 64-71, 2016. https://doi.org/10.5815/ijigsp.2016.06.08.

H. Wu, X. Sun, J. Yang, W. Zeng, F. Wu, “Lossless compression of JPEG coded photo collections,” IEEE Transactions on Image Processing, vol. 25, no. 6, pp. 2684-2696, 2016. https://doi.org/10.1109/TIP.2016.2551366.

C. Chen, Y. Zhuo, “A research on anti-jamming method based on compressive sensing for OFDM analogous system,” Proceedings of the IEEE 17th International Conference on Communication Technology (ICCT), 2017, pp. 655-659, https://doi.org/10.1109/ICCT.2017.8359718.

Y. S. Manzhos, & Y. V. Sokolova, “A method of IoT information compression,” International Journal of Computing, vol. 21, issue 1, pp. 100-110, 2022. https://doi.org/10.47839/ijc.21.1.2523.

J. Lee, S. Cho, S.-K. Beack, “Context-adaptive entropy model for end-to-end optimized image compression,” 2018. arXiv: 1809.10452.

Z. Wang, R. Liao, Y. Ye, “Joint learned and traditional video compression for P frame,” Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW), 2020, pp. 560-564. https://doi.org/10.1109/CVPRW50498.2020.00075.

M. Akbari, J. Liang, J. Han, C. Tu, “Learned variable-rate image compression with residual divisive normalization,” Proceedings of the IEEE International Conference on Multimedia and Expo (ICME), 2020, pp. 1-6. https://doi.org/10.1109/ICME46284.2020.9102877.

S. Wang, S. Kim, Z. Yin, T. He, “Encode when necessary: Correlated network coding under unreliable wireless links,” ACM Transactions on Sensor Networks, vol. 13, no.1, pp. 24-29, 2017. https://doi.org/10.1145/3023953.

https://sipi.usc.edu/database/database.php?volume=misc&image=11#top.

V. Barannik, I. Tupitsya, O. Dodukh, V. Barannik, M. Parkhomenko, “The method of clustering information resource data on the sign of the number of series of units as a tool to improve the statistical coding efficiency,” Proceedings of the IEEE 15th International Conference on the Experience of Designing and Application of CAD Systems (CADSM), 2019, pp. 32-35. https://doi.org/10.1109/CADSM.2019.8779243.

V. Barannik, I. Tupitsya, I. Gurzhii, V. Barannik, S. Sidchenko, O. Kulitsa, “Two-hierarchical scheme of statistical coding of information resource data with quantitative clustering,” Proceedings of the IEEE International Conference on Advanced Trends in Information Theory (ATIT), 2019, pp. 89-92. https://doi.org/10.1109/ATIT49449.2019.9030451.

V. Barannik, I. Tupitsya, V. Barannik, S. Shulgin, A. Musienko, R. Kochan, O. Veselska, “the application of the internal restructuring method of the information resource data according to the sign of the number of series of units to improve the statistical coding efficiency,” Proceedings of the 10th IEEE International Conference on Intelligent Data Acquisition and Advanced Computing Systems: Technology and Applications (IDAACS’2019), 2019, pp. 65-69. https://doi.org/10.1109/IDAACS.2019.8924460.

O. Yudin, V. Artemov, A. Krasnorutsky, V. Barannik, I. Tupitsya and G. Pris, “Creating a mathematical model for estimating the impact of errors in the process of reconstruction of non-uniform code structures on the quality of recoverable video images,” Proceedings of the Advanced Trends in Information Theory (ATIT’2021), 2021, pp. 38-41. https://doi.org/10.1109/ATIT54053.2021.9678887.

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Published

2022-09-30

How to Cite

Karlov, D., Tupitsya, I., Parkhomenko, M., Musienko, O., & Lekakh, A. (2022). Compression Coding Method Using Internal Restructuring of Information Space. International Journal of Computing, 21(3), 360-368. https://doi.org/10.47839/ijc.21.3.2692

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