The article demonstrates that when unmanned platforms are used to collect video information, a disparity arises between the performance of wireless infocommunication systems and the intensity level of the information streams that must be processed and transmitted. In general, to maintain the required quality of video data under such a disparity, the following measures are necessary: increasing the performance of infocommunication systems (ICS); improving the efficiency of video data compression systems; and ensuring robustness against interference. Currently, a number of video compression methods have been developed. Functionally, they can be divided into two classes depending on the use of parameter control technologies within models that detect and reduce psychovisual redundancy. It is argued that the critical limitation of the first class of methods is the increase in information loss when practical compression levels must be achieved for information-rich areas of video data. The impact of this limitation on compression efficiency can be mitigated by developing methods from the second class. This class includes methods that eliminate redundancy of a structural-positional or statistical-positional nature. One of the main representatives of structural-positional encoding methods is the variable-weight positional encoding technology, also known as Nonequilibrium Positional (NQP) encoding. These methods are capable of adapting to the content of video segments based on their structural and positional characteristics. In this case, regardless of the encoding direction, there exists a dependency between the weight of higher-order NQP-number elements and the bases of lower-order elements. This leads to an increase in the weight of elements during encoding. A critical threshold arises if the encoding direction is chosen inappropriately, considering the structural characteristics of the video segments within the range of permissible values. Therefore, it is proposed to develop a more adaptive version based on a strategy of using bi-directional lexicography during NQP encoding. The article outlines the main stages for creating such bi-directional lexicography based on adaptive selection of the indexing direction of element values within the operational range of the NQP basis.
[1] T. Naga Lakshmi and S. Jyothi, "Performing Image Compression and Decompression Using Matrix Substitution Technique," in Advances in Computational and Bio-Engineering. CBE 2019. Learning and Analytics in Intelligent Systems, vol. 15, S. Jyothi, D. Mamatha, S. Satapathy, K. Raju and M. Favorskaya, Eds. Cham: Springer, 2020. doi: 10.1007/978-3-030-46939-9_3.
[2] Бараннік , Д. В., Гуржій , П. М., Бараннік , В. В., Сідченко , С. О., Чорномаз , І. К., and Григор’ян , М. Б. (2023) “Saving Elements Methods for Service Components of Images Cryptocompression Codograms”, Visnyk NTUU KPI Seriia - Radiotekhnika Radioaparatobuduvannia, (92), pp. 28-40. doi: 10.20535/RADAP.2023.92.28-40.
[3] A. Qasim, R. Din and F. Q. A. Alyousuf, "Review on techniques and file formats of image compression," Bulletin of Electrical Engineering and Informatics, vol. 9, no. 2, pp. 602–610, 2020. doi: 10.11591/eei.v9i2.2085.
[4] 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.
[5] Barannik V. et al. 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.
[6] A. Putra, S. Supriadi, A. Wibawa, A. Pranolo and A. Gaffar, "Modification of a gray-level dynamic range based on a number of binary bit representation for image compression," Science in Information Technology Letters, vol. 1, no. 1, pp. 9–16, 2020. doi: 10.31763/sitech.v1i1.17.
[7] Barannik V. et al. 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.
[8] Власов А.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.
[9] Tarasenko, D., Kulitsa, O., Barannik, D., Barannik, V.V., 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.
[10] 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.
[11] Ibrahim D. R., The J S., Abdullah R. (2021). An overview of visual cryptography techniques. Multimed Tools Appl 80, 31927–31952. doi: https://doi.org/10.1007/s11042-021-11229-9
[12] 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.
[13] Chen, C.-C., Lin, C.-S., Chen, J.-Z. (2022). Boolean-Based (k, n, m) Multi-Secret Image Sharing. Axioms, 11, 197. doi: https://doi.org/10.3390/axioms11050197
[14] Barannik V. et al. "Significant Microsegment Transformants Encoding Method to Increase the Availability of Video Information Resource," 2020 IEEE 2nd International Conference on Advanced Trends in Information Theory (ATIT), Kyiv, Ukraine, 2020, pp. 52-56, doi: 10.1109/ATIT50783.2020.9349256.
[15] Alsafyani M, Alhomayani F, Alsuwat H, Alsuwat E. Face Image Encryption Based on Feature with Optimization Using Secure Crypto General Adversarial Neural Network and Optical Chaotic Map. Sensors. 2023; 23(3):1415. https://doi.org/10.3390/s23031415
[16] V. Barannik, A. Krasnorutsky, Y. Ryabukha, R. Onyshchenko, S. Shulgin and O. Slobodyanyuk, "Marker Information Coding for Structural Clustering of Spectral Space," 2021 IEEE 3rd International Conference on Advanced Trends in Information Theory (ATIT), Kyiv, Ukraine, 2021, pp. 46-51, doi: 10.1109/ATIT54053.2021.9678538.
[17] Xu, Daihan et al. “Design of artificial intelligence image encryption algorithm based on hyperchaos.” Ain Shams Engineering Journal (2022): n. pag.
[18] Gadhiya N., Tailor S., Degadwala S. (2024). A Review on Different Level Data Encryption through a Compression Techniques. 2024 International Conference on Inventive Computation Technologies (ICICT), Lalitpur, Nepal, p. 1378-1381. doi: 10.1109/ICICT60155.2024.10544803.
[19] V. Barannik and A. Shiryaev, "Quadrature compression of images in polyadic space," Proceedings of International Conference on Modern Problem of Radio Engineering, Telecommunications and Computer Science, 2012, pp. 422-422. INSPEC Accession Number: 12713484.
[20] Matel I.-E. (2024) Novel Lossless Crypto-Compression Scheme for Medical Images. 2024 26th International Symposium on Symbolic and Numeric Algorithms for Scientific Computing (SYNASC), Timisoara, Romania, p. 378-385. doi: 10.1109/SYNASC65383.2024.00069.
[21] Yuanlin C., Tianxiu L., Caiwen C., Yi X. A novel image encryption method based on improved two-dimensional logistic mapping and DNA computing. Advances in Nonlinear Systems and Networks, 2024, Volume III, DOI: 10.3389/fphy.2024.1469418.
[22] Hamano G., Imaizumi S., Kiya H. (2023). Effects of JPEG Compression on Vision Transformer Image Classification for Encryption-then-Compression Images. Sensors, 23, 3400. doi: https://doi.org/10.3390/s23073400
[23] Alqahtani, F. (2024). Ai-powered image security: utilizing autoencoders for advanced medical image encryption. Computer Modeling in Engineering & Sciences, 141(2), 1709–1724. https://doi.org/10.32604/cmes.2024.054976
[24] Shoko Imaizumi, Genki Hamano, 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.
[25] Barannik V. et al. "Method of coding dynamic sequence of frame-spline structures of provided frames in info-communications," 2021 IEEE 4th International Conference on Advanced Information and Communication Technologies (AICT), Lviv, Ukraine, 2021, pp. 36-40, doi: 10.1109/AICT52120.2021.9628928.
[26] Fatima R., Baheeja K, May A. Survey Study Image Cryptography System. BIO Web Conf, Volume 97, 2024, DOI: 10.1051/bioconf/20249700044.
[27] Barannik V. et al. Method of indirect information hiding in the process of video compression. Radioelectronic and Computer Systems. 2021. №. 4. PP. 119–131. https://doi.org/10.32620/reks.2021.4.
[28] V. Barannik, O. Slobodyanyuk, A. Krasnorutsky, A.Korchenko, S.Pchelnikov “Aerial Photographs for Ensuring Cyber Security of Critical Infrastructure Objects” 2021 Proceedings of Selected Papers of the Workshop on Cybersecurity Providing in Information and Telecommunication Systems (CPITS 2021), Kyiv, Ukraine, pp. 182–191.
[29] Xiaowu L., Huiling P. Chaotic medical image encryption method using attention mechanism fusion ResNet model. Front Neurosci. 2023, DOI: 10.3389/fnins.2023.1226154.
[30] Bhat, R., & Nanjundegowda, R. (2024). CryptoGAN: a new frontier in generative adversarial network-driven image encryption. IAES International Journal of Artificial Intelligence (IJ-AI), 13(4), 4813-4821. doi:http://doi.org/10.11591/ijai.v13.i4.pp4813-4821.