Video Data Video Image Reconstruction Method for Increasing Delivery Efficiency in Air Segment Infocommunication Systems

Authors

  • I. M. Tupytsia Ivan Kozhedub Kharkiv National Air Force University
  • S. O. Kibitkin Ivan Kozhedub Kharkiv National Air Force University
  • V. M. Sukhoteplyi Ivan Kozhedub Kharkiv National Air Force University
  • D. M. Nepokrytov Ivan Kozhedub Kharkiv National Air Force University
  • D. V. Konov Ivan Kozhedub Kharkiv National Air Force University

DOI:

https://doi.org/10.31649/1997-9266-2022-163-4-72-82

Keywords:

transformation, restructuring, clustering, code construction, video images

Abstract

Today, the main tool of effective management at both the local, regional, and state levels is the availability of appropriate information support. This especially applies to the security and defense sector, where the availability of the necessary information support is the main requirement for the effective response of departmental bodies (relevant law enforcement agencies) to crisis situations arising both in society and the state as a whole. In connection with this, the role of video information support as a means for prompt decision-making is significantly increased. This is due to the fact that the key principles of the implementation of video information provision are timeliness (operational) and reliability. For this purpose, stationary and mobile photo and video surveillance systems are quite actively used. The use of the latter is closely related to the aviation segment — unmanned aerial vehicles and complexes, the role of which is increased by the presence of such properties as scale and mobility. However, at the same time, the following problematic factors arise related to the use of wireless communication technologies for data delivery to the final addressee: an imbalance between the ever-increasing volumes of data and the bandwidth of data transmission channels; the influence of obstacles arising in the process of video data delivery on the level of reliability of the reconstructed video image. It should be noted that the use of existing methods of interference-resistant coding to solve the above-mentioned problems leads to a significant increase in the volume of video data, which is critical in the conditions of using wireless communication technologies — video is an information resource transmitted with significant time delays. For this purpose, a method of video data reconstruction is being developed based on the use of identifiers (markers) of uneven code structures assigned to the elements of clusters formed as a result of the restructuring of the information space by structural feature. A distinctive feature of the developed method is the independent decompositional statistical decoding of individual code subsets according to structural features. This ensures, due to the use of additional service information, the localization of errors in the process of data reconstruction of the video information resource. A distinctive feature of the developed method is the independent decompositional statistical decoding of individual code subsets according to structural features. This ensures, due to the use of additional service information, the localization of errors in the process of data reconstruction of the video information resource. A distinctive feature of the developed method is the independent decompositional statistical decoding of individual code subsets according to structural features. This ensures, due to the use of additional service information, the localization of errors in the process of data reconstruction of the video information resource.

Author Biographies

I. M. Tupytsia, Ivan Kozhedub Kharkiv National Air Force University

Lecturer of the Chair of Combat Application and Operation of Automated Control Systems

S. O. Kibitkin, Ivan Kozhedub Kharkiv National Air Force University

Cand. Sc. (Eng.), Lecturer of the Chair of Aviation Equipment and Air Intelligence Complexes

V. M. Sukhoteplyi, Ivan Kozhedub Kharkiv National Air Force University

Senior Lecturer of the Chair of Radio electronic Systems of Control Points

D. M. Nepokrytov, Ivan Kozhedub Kharkiv National Air Force University

Associate Professor of the Chair of Radio Electronic Systems of Control Points of Air Force

D. V. Konov, Ivan Kozhedub Kharkiv National Air Force University

Researcher of the Scientific Research Laboratory of the Department of Automated Control Systems and Ground Support of Aviation Flights

References

Кабінет Міністрів України, Постанова № 695 «Про затвердження Державної стратегії регіонального розвитку на 2021–2027 роки», 2020, серп. 5. [Електронний ресурс]. Режим доступу: https://zakon.rada.gov.ua/laws/show/695-2020-%D0%BF#Text .

Верховна Рада України, Закон № 1882-IX «Про критичну інфраструктуру», 2021, лист. 16. [Електронний ресурс]. Режим доступу: https://zakon.rada.gov.ua/laws/show/1882-20#Text .

Кабінет Міністрів України. Постанова № 821 «Про затвердження Порядку проведення моніторингу рівня безпеки об’єктів критичної інфраструктури». 2021, січ. 13. [Електронний ресурс]. Режим доступу: https://zakon.rada.gov.ua/laws/show/821-2022-%D0%BF#Text .

Міністерство Внутрішніх Справ України, Наказ № 12 «Про Про затвердження Інструкції із застосування військовослужбовцями Національної гвардії України технічних приладів і технічних засобів, що мають функції фото- і кінозйомки, відеозапису, засобів фото- і кінозйомки, відеозапису», 2022, лип. 22. [Електронний ресурс]. Режим доступу: https://zakon.rada.gov.ua/laws/show/z0294-21#Text .

Prozorro [Електронний ресурс]. Режим доступу: https://prozorro.gov.ua/tender/UA-2022-07-15-007145-a .

Prozorro [Електронний ресурс]. Режим доступу: https://prozorro.gov.ua/tender/UA-2022-06-28-002174-a .

Prozorro [Електронний ресурс]. Режим доступу: https://prozorro.gov.ua/tender/UA-2021-12-15-020377-c .

Prozorro [Електронний ресурс]. Режим доступу: https://prozorro.gov.ua/tender/UA-2021-12-17-004587-b .

Prozorro [Електронний ресурс]. Режим доступу: https://prozorro.gov.ua/tender/UA-2022-02-18-001165-c .

R. Swaminathan, and 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, 2017, pp. 463-478. https://doi.org/10.1109/TBC.2017.2704436 .

B. -F. Wu, Y. -C. Wu, L. -W. Chiu, and H. -P. Liu, “Soft Label With Channel Encoding for Dependent Facial Image Classification,” in IEEE Access, vol. 10, pp. 10661-10672, 2022, https://doi.org/10.1109/ACCESS.2022.3145195 .

Y. Jiang, “Analysis of Bit Error Rate Between BCH Code and Convolutional Code in Picture Transmission,” in 2022 3rd International Conference on Electronic Communication and Artificial Intelligence (IWECAI), Zhuhai, 2022, pp. 77-80, https://doi.org/10.1109/IWECAI55315.2022.00023 .

S. D. Potey, and P. M. Dhande, “Error Detection and Correction Capability for BCH Encoder using VHDL,” in IEEE 5th International Conference for Convergence in Technology (I2CT), Bombay, 2019, pp. 1-4.

https://doi.org/10.1109/I2CT45611.2019.9033847 .

P. Garlapati, B. Yamuna, and K. Balasubramanian, “A Low Power Hard Decision Decoder for BCH Codes,” in 2021 International Conference on Advances in Computing and Communications (ICACC), Kochi, Kakkanad, 2021, pp. 1-6, https://doi.org/10.1109/ICACC-202152719.2021.9708303 .

T. Richter, “Error Bounds for HDR Image Coding with JPEG XT,” in Data Compression Conference (DCC), 2017, pp. 122-130. https://doi.org/10.1109/DCC.2017.7 .

Y. Chen, F. Wu, C. Li, and 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 .

X. Peng, J. Jiang, L. Tan, and J. Hou, “2-D Bi-Level Block Coding for Color Image Compression and Transmission With Bit-Error Awareness,” in IEEE Access, vol. 8, pp. 110093-110102, 2020. https://doi.org/10.1109/ACCESS.2020.3001073.

P. Lamsrichan, V. Manthamkarn, and U. Tuntoolavest, “Performance Evaluation of the Block Truncation Image Coding with BCH Codes under Noisy Channels,” in 19th International Conference on Electrical Engineering/Electronics, Computer, Telecommunications and Information Technology (ECTI-CON), Prachuap Khiri Khan, 2022, pp. 1-4.

https://doi.org/10.1109/ECTI-CON54298.2022.9795634 .

N. Patsei, and K. Tsybulka, “Multi-class Object Classification Model Based on Error-Correcting Output Codes,” in IEEE Open Conference of Electrical, Electronic and Information Sciences (eStream), Vilnius, 2021, pp. 1-5, https://doi.org/10.1109/eStream53087.2021.9431443 .

P. Puteaux, and W. Puech, “Localization and Correction of Corrupted Pixel Blocks in Noisy Encrypted Images,” in 2020 Tenth International Conference on Image Processing Theory, Tools and Applications (IPTA), Paris, 2020, pp. 1-6. https://doi.org/10.1109/IPTA50016.2020.9286451 .

S. Khmelevskiy, I. Tupitsya, Q. A. Mahdi, О. Musienko, M. Parkhomenko, and Y. Borovensky, “Development of the external restructuring method to increase the efficiency of information resource data encoding,” Information Processing Systems, 3(166), pp. 52-61, 2021. https://doi.org/10.30748/soi.2021.166.06 .

G. B. Iwasokun, “Lossless JPEG-Huffman model for digital image compression,” Adv. Image Video Process, vol. 7, no. 1, pp. 1-12, Feb. 2019. https://doi.org/10.14738/aivp.71.5837 .

A. A. Jeny, M. B. Islam, M. S. Junayed, and D. Das, “Improving Image Compression with Adjacent Attention and Refinement Block,” in IEEE Access, 2022. https://doi.org/10.1109/ACCESS.2022.3195295 .

S. A. Deepthi, E. S. Rao, and M. N. G. Prasad, “Image compression techniques in wireless sensor networks,” in Proc. IEEE Int. Conf. Smart Technol. Manage. Comput., Commun., Controls, Energy Mater. (ICSTM), Aug. 2017, pp. 286-289. https://doi.org/10.1109/ICSTM.2017.8089170 .

S. Khmelevsky et al., Method for quantitative criterion based transformation of the video information alphabet. Radioelectronic and Computer Systems, no. 2, pp. 200-216, 2022. https://doi.org/10.32620/reks.2022.2.16 .

Y. S. Manzhos, and Y. V. Sokolova, “A Method of IoT Information Compression,” International Journal of Computing, no. 21(1), pp. 100-110, 2022. https://doi.org/10.47839/ijc.21.1.2523 .

H. Huan, Z. Yuxuan, Y. Weijun, Y. Sihai, and L. Jing, “The Research on Image Processing Based on Wavelet Analysis,” in IEEE 10th Joint International Information Technology and Artificial Intelligence Conference (ITAIC), Chongqing, 2022, pp. 1162-1165. https://doi.org/10.1109/ITAIC54216.2022.9836655 .

T. Shinde, “Efficient Image Set Compression,” in IEEE International Conference on Image Processing (ICIP), 2019, pp. 3016-3017. https://doi.org/10.1109/ICIP. 2019.8803230 .

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

W. Xiao, N. A. Wan, Hong, and X. Chen, “A Fast JPEG Image Compression Algorithm Based on DCT,” in IEEE International Conference on Smart Cloud (SmartCloud), 2020, pp. 106-110. https://doi.org/10.1109/ SmartCloud49737. 2020.00028

S. Khmelevsky, I. Tupitsya, M. Parkhomenko, and Y. Borovensky, “Model of Transformation of the Alphabet of the Encoded Data as a Tool to Provide the Necessary Level of Video Image Quality in Aeromonitoring Systems,” IT&I Workshops, 2021, pp. 311-319. [Electronic resource]. Available: http://ceur-ws.org/Vol-3179/Short_4.pdf .

B. A. Lungisani, C. K. Lebekwe, A. M. Zungeru and A. Yahya, “Image Compression Techniques in Wireless Sensor Networks: A Survey and Comparison,” in IEEE Access, vol. 10, pp. 82511-82530, 2022. https://doi.org/10.1109/ACCESS.2022.3195891.

H. Shi, K. M. Hou, X. Diao, L. Xing, J.-J. Li, and C. De Vaulx, A wireless multimedia sensor network platform for environmental event detection dedicated to precision agriculture, 2018, arXiv: 1806.03237.

A. Djelouah, J. Campos, S. Schaub-Meyer, C. Schroers, “Neural Inter-Frame Compression for Video Coding,” in IEEE/CVF International Conference on Computer Vision (ICCV), 2019, pp. 6420-6428. https://doi.org/10.1109/ICCV.2019. 00652.

O. Rippel, “Learned Video Compression,” in IEEE/CVF International Conference on Computer Vision (ICCV), 2019, pp. 3453-3462. https://doi.org/10.1109/ICCV. 2019.00355 .

F. Artuğer, and F. Özkaynak, “Fractal Image Compression Method for Lossy Data Compression,” in International Conference on Artificial Intelligence and Data Processing (IDAP), 2018, pp. 1-6. https://doi.org/10.1109/IDAP. 2018.8620735 .

M. A. Alam, “Faster Image Compression Technique Based on LZW Algorithm Using GPU Parallel Processing,” in 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 .

V. Barannik et al, “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,” in 10th IEEE International Conference on Intelligent Data Acquisition and Advanced Computing Systems: Technology and Applications (IDAACS), 2019, pp. 65-69. https://doi.org/ 10.1109/IDAACS.2019.8924460.

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,” in 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 et al., “Two-Hierarchical Scheme of Statistical Coding of Information Resource Data with Quantitative Clustering,” in IEEE International Conference on Advanced Trends in Information Theory (ATIT), 2019, pp. 89-92. https://doi.org/10.1109/atit49449.2019.9030451 .

V. Barannik et al., “The analysis of the internal restructuring method efficiency used for a more compact representation of the encoded data,” in Advanced Trends in Information Theory (ATIT’2020): proceedings of the Intern. Conf., 2020, pp. 89-92. https://doi.org/10.1109/atit49449.2019.9030451 .

O. Yudin et al. “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,” in Advanced Trends in Information Theory (ATIT’2021): proceedings of the Intern. Conf., pp. 38-41. https://doi.org/10.1109/atit54053.2021.9678887 .

Ю. В. Стасєв, І. M. Тупиця, і М. В. Пархоменко, «Метод додаткового скорочення структурної надмірності кодового представлення відеоданих,» Вісник Вінницького політехнічного інституту, № 3, с. 67-76, 2022. https://doi.org/10.31649/1997-9266-2022-162-3-67-76.

USC Viterbi, School of Engineering. [Electronic resource]. Available: https://sipi.usc.edu/database/database.php?volume= isc&image=26#top .

USC Viterbi, School of Engineering. [Electronic resource]. Available: https://sipi.usc.edu/database/database.php?volume= misc&image=27#top .

USC Viterbi, School of Engineering. [Electronic resource]. Available: https://sipi.usc.edu/database/database.php?volume= misc&image=24#top .

Downloads

Abstract views: 189

Published

2022-09-02

How to Cite

[1]
I. M. Tupytsia, S. O. Kibitkin, V. M. Sukhoteplyi, D. M. Nepokrytov, and D. V. Konov, “Video Data Video Image Reconstruction Method for Increasing Delivery Efficiency in Air Segment Infocommunication Systems”, Вісник ВПІ, no. 4, pp. 72–82, Sep. 2022.

Issue

No. 4 (2022)

Section

Information technologies and computer sciences

Metrics

Downloads

Download data is not yet available.

License

Creative Commons License

This work is licensed under a Creative Commons Attribution 4.0 International License.

Authors who publish with this journal agree to the following terms:

  • Authors retain copyright and grant the journal right of first publication.
  • Authors are able to enter into separate, additional contractual arrangements for the non-exclusive distribution of the journal's published version of the work (e.g., post it to an institutional repository or publish it in a book), with an acknowledgment of its initial publication in this journal.
  • Authors are permitted and encouraged to post their work online (e.g., in institutional repositories or on their website) prior to and during the submission process, as it can lead to productive exchanges, as well as earlier and greater citation of published work (See The Effect of Open Access).