METHOD CODING OF CLUSTERED TRANSFORMANTS IN DIFFERENTIAL-NORMALIZED SPACE

2024;
: 19-28
1
V. N. Karazin Kharkiv National University
2
V. N. Karazin Kharkiv National University
3
Kharkiv National University of Radio Electronics
4
Heroiv Krut Military Institute of Telecommunications and Informatization

The article shows that one of the main purposes of projects for the development of informatization of the state is the proper provision of the necessary information to the centers of analysis and decision-making. It is important to comply with the requirements for the timeliness, reliability and security of information delivery processes. This contributes to the development of means of remote collection of information and its transmission using various technological solutions. Unmanned aerial vehicles (UNV) are in the greatest demand. However, the article shows that in practice there are factors that limit the capabilities of telecommunications equipment. Then the timeliness and reliability of information transmission will be realized only for low-level image formats. On the other hand, the procedure of information analysis, including the use of intelligent analysis, puts forward factors for the implementation of higher-level image formats on the UNV. It is clear that a contradiction arises. This contradiction concerns the inconsistency between the permissible and required levels of image formats for unmanned vehicles. Localization of such collisions is possible by reducing the information load on the basis of taking into account certain features in the description of image fragments. In spectral space, such features of fragments have the following manifestation: the presence of sequences of spectral components with a not significant deviation of the span interval. The presence of such features is a prerequisite for the construction of compression methods in the spectral-parametric description of transformants (SPDT). Therefore, the aim of the article is to develop methods for compressing images based on their spectral-parametric description, taking into account higher-order dependencies. The necessity for the formation of homogeneity spaces for the group of transformants of the general video stream for the implementation of the possibility of accounting for inter-transformant dependencies in the SPD of arrays of spectral elements is substantiated. A model for constructing homogeneity spaces (clusters) from the transformant group based on the power of the SP by the number of spectral SP has been developed. This creates the conditions for the implementation of the compression procedure with the additional removal of the amount of inter-transformant redundancy in the SPD-transformant.

[1]   Алімпієв А.М. Теоретичні основи створення технологій протидії прихованим інформаційним атакам в сучасній гібридній війні / А.М. Алімпієв, В.В. Бараннік, Т.В. Белікова, С.О. Сідченко // Системи обробки інформації. – Харків: ХНУПС, 2017. – Вип. 4(150). – С. 113-121.

[2]   Barannik V., Stepanko O., Nikodem J., Jancarczyk D.,Babenko Yu., Zawislak S. A Model for Representing Significant Segments of a Video Image Based on Locally Positional Coding on a Structural Basis. Smart and Wireless Systems within the Conferences on Intelligent Data Acquisition and Advanced Computing Systems (IEEE IDAACS-SWS 2020): proceedings of IEEE 5nd International Symposium, 2020. P. 1–5. DOI: 10.1109/IDAACS-SWS50031.2020.9297068.

[3]   Wenyang Liu, Yi Wang, Kim-Hui Yap, Lap-Pui Chau. Bitstream-Corrupted JPEG Images Are Restorable: Two-Stage Compensation and Alignment Framework for Image Restoration. Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2023, PP. 9979-9988.

[4]   V. Barannik, N. Kharchenko, O. Kulitsa, V. Tverdokhleb, , "The issue of timely delivery of video traffic with controlled loss of quality", in 13th International Conference on Modern Problems of Radio Engineering, Telecommunications and Computer Science (TCSET), 2016, pp. 902-904. DOI: 10.1109/TCSET.2016.7452220.

[5]   Barannik, D., Kulitsa, O., Barannik, V.V., Tarasenko, D., Podlesny, S., The video stream encoding method in infocommunication systems. IEEE 14th International Conference on Advanced Trends in Radioelecrtronics, Telecommunications and Computer Engineering (IEEE TCSET 2018), 2018, pp. 538-541. DOI: 10.1109/TCSET.2018.8336259.

[6]   Boyi Li, Wenqi Ren, Dengpan Fu, Dacheng Tao, Dan Feng, Wenjun Zeng, and Zhangyang Wang. Benchmarking singleimage dehazing and beyond. IEEE Transactions on Image Processing. - vol 28(1). – PP. 492–505, 2018.

[7]   Yuan Liu, Songyang Zhang, Jiacheng Chen, Zhaohui Yu, Kai Chen, Dahua Lin. Improving Pixel-based MIM by Reducing Wasted Modeling Capability. 2023 IEEE/CVF International Conference on Computer Vision (ICCV). – 2023. – pp. 5338-5349. DOI Bookmark: 10.1109/ICCV51070.2023.00494.

[8]   Shamir A., Rivest R. L., Adleman L. M. A method for obtaining digital signatures and public-key cryptosystems. Communications of the ACM. 1978. Vol. 21. Iss. 2. P. 120–126. DOI: 10.1145/359340.359342.

[9]   Баранник В.В. Обоснование значимых угроз безопасности видеоинформационного ресурса систем видеоконференцсвязи профильных систем управления / В.В. Баранник, А.В. Власов, С.А. Сидченко // Информационно-управляющие системы на ЖД транспорте. – 2014. ‑ №3. ‑ С. 24 – 31.

[10]Belikova T. Decoding Method of Information-Psychological Destructions in the Phonetic Space of Information Resources. Advanced Trends in Information Theory (ATIT): proceedings of the 2nd IEEE International Conference, 2020. P. 87–91. URL: https://ieeexplore.ieee.org/document/9349300.

[11]Yingkai Huang, Zhuxian Liu, Qiwen Wu, Xiaolong Liu. Robust image steganography against JPEG compression based on DCT residual modulation. Signal Processing. – Vol. 219. - 2024. https://doi.org/10.1016/j.sigpro.2024.109431.

[12]X., Au O. C. , Zhou J., Liu Tang Y. Y. Designing an Efficient Image Encryption-Then-Compression System via Prediction Error Clustering and Random Permutation.  IEEE Transactions on Information Forensics and Security. 2014. Vol. 9, No. 1. P. 39–50. DOI: 10.1109/TIFS.2013.2291625.

[13]Barannik, V. et al. (2023). Processing Marker Arrays of Clustered Transformants for Image Segments. In: Klymash, M., Luntovskyy, A., Beshley, M., Melnyk, I., Schill, A. (eds) Emerging Networking in the Digital Transformation Age. TCSET 2022. Lecture Notes in Electrical Engineering, vol 965. Springer, Switzerland, Cham. https://doi.org/10.1007/978-3-031-24963-1_25.

[14]Guojun Fan, Zhibin Pan, Quan Zhou, Jing Dong, Xiaoran Zhang. Pixel type classification based reversible data hiding for hyperspectral images. Knowledge-Based Systems. – vol. 254. – 2022. https://doi.org/10.1016/j.knosys.2022.109606.

[15]Barannik, V., Barannik, N., Sidchenko, S., Khimenko, A. The method of masking overhead compaction in video compression systems, Radioelectronic and Computer Systems, 2021, no. 2, pp. 51–63. doi: https://doi.org/10.32620/reks.2021.2.05.

[16]Tong Qiao, Shuai Wang, Xiangyang Luo, Zhiqiang Zhu. Robust steganography resisting JPEG compression by improving selection of cover element. - Signal Processing. - vol.183. – 2021. https://doi.org/10.1016/j.sigpro.2021.108048.

[17]Xiuli Bi, Wuqing Yan, Bo Liu, Bin Xiao, Weisheng Li, Xinbo Gao. Self-Supervised Image Local Forgery Detection by JPEG Compression Trace. - The Thirty-Seventh AAAI Conference on Artificial Intelligence (AAAI-23). - vol. 37 (No. 1). – pp. 232 – 240. DOI: https://doi.org/10.1609/aaai.v37i1.25095.

[18]Genki Hamano, Shoko Imaizumi, Hitoshi Kiya. Effects of JPEG Compression on Vision Transformer Image Classification for Encryption-then-Compression Images. - Sensors vol.23. – pp.1-19. – 2023. https://doi.org/10.3390/s23073400.

[19]Nagamori, H.; Kiya, H. Combined Use of Federated Learning and Image Encryption for Privacy-Preserving Image Classification with Vision Transformer. arXiv 2023, arXiv:2301.09255.

[20]Deshmukh, M. An (n, n)-Multi Secret Image Sharing Scheme Using Boolean XOR and Modular Arithmetic [Text] / M. Deshmukh, N. Nain, M. Ahmed // Advanced Information Networking and Applications : proc. IEEE 30 th Int. Conf. (AINA), 23-25 March 2016. – Crans-Montana, Switzerland, 2016. – P. 690–697. DOI: 10.1109/aina.2016.56.

[21]Цімура Ю. В., Юдін О. К., Мельников О. Є., Коляденко Ю. Ю., Гуржій П. М.. Модель оцінювання інформативності спектрально-параметричного опису трансформованих відео фрагментів // Наукоємні технології № 4(60). - 2023. – С 423 – 429. doi: 10.18372/2310-5461.60.18272.

[22]Barannik V., Barannik D. Barannik N., Indirect Steganographic Embedding Method Based On Modifications of The Basis of the Polyadic System. Modern Problems of Radio Engineering, Telecommunications and Computer Science (TCSET’2020): proceedings of 15 th IEEE International Conference, 2020. P. 699–702. DOI: 10.1109/TCSET49122.2020.235522.

[23]Kiya, H., Aprilpyone, M., Kinoshita, Y., Imaizumi, S., Shiota, S. An Overview of Compressible and Learnable Image Transformation with Secret Key and Its Applications. APSIPA Trans. Signal Inf. Process. 2022, 11, e11. http://dx.doi.org/10.1561/116.00000048.

[24]Wong K. W. Image encryption using chaotic maps. Intelligent Computing Based on Chaos. 2009. Vol. 184. P. 333–354. DOI: 10.1007/978-3-540-95972-4_16.

[25]Aprilpyone, M.; Kiya, H. Privacy-Preserving Image Classification Using an Isotropic Network. IEEE Multimed. - 2022. - 29. – pp. 23–33. doi: 10.1109/MMUL.2022.3168441.

[26]Цімура Ю.В., Юдін О.К., Коляденко Ю.Ю., Єрошенко В.П., Метод кодування фрагментів-контейнерів в спектрально-параметричному просторі // Наукоємні технології – 2024. – № 1(61). – С. 36 – 43. doi: 10.18372/2310-5461.61.18513.

[27]Цімура Ю., Костромицький А., Суханов О., Думич C. Метод кодування відеоданих в спектрально-параметричному просторі // Інфокомунікаційні технології та електронна інженерія. – 2024. – Випуск 4 (1). – С 61 – 69. doi: 10.23939/ictee2024.01.061.

[28]Bаrаnnіk V.V., Kаrреnkо S. Mеthоd оf thе 3-D іmаgе рrоcеssіng. Mоdеrn Рrоblеms оf Rаdіо Еngіnееrіng, Tеlеcоmmunіcаtіоns аnd Cоmрutеr Scіеncе (ІЕЕЕ TCSЕT 2008): proceedings of ІЕЕЕ Іntеrnаtіоnаl Cоnfеrеncе, 2008. P. 378-380.

[29]Barannik, V. et al. (2023). A Method of Scrambling for the System of Cryptocompression of Codograms Service Components. In: Klymash, M., Luntovskyy, A., Beshley, M., Melnyk, I., Schill, A. (eds) Emerging Networking in the Digital Transformation Age. TCSET 2022. Lecture Notes in Electrical Engineering, vol 965. Springer, Switzerland, Cham. https://doi.org/10.1007/978-3-031-24963-1_26

[30]Цімура Ю.В., Бабенко Ю. М., Бучик С.С., Пчельніков С.І., Ушань В.М. Метод кодування низькоінформативних сегментів відеоінформаційного ресурсу для підвищення їх доступності // Наукоємні технології. ‑ 2023. ‑№ 1. – С. 20 – 27. DOI: https://doi.org/10.18372/2310-5461.57.17441.