METHOD OF SEGMENT CLASSIFICATION IN VIDEO STREAM INTENSITY MANAGEMENT SYSTEMS USING COMPRESSION TECHNOLOGIES
DOI:
https://doi.org/10.18372/2310-5461.71.21449Keywords:
video block, intensity, informativeness, compression, quad-tree, information and communication networksAbstract
The article shows the relevance of issues related to the protection and improvement of the promptness of delivering video information streams. This applies especially to the area of applications in the interests of critical infrastructure. A critical factor here is the limited and uneven change in the data transmission rate in information and communication networks. This leads to an imbalance with the level of information load. One of the ways to overcome this imbalance is the use of intensity control technologies in the process of video stream compression. In this case, the compression of video data is provided under conditions of adaptation to the current data transmission rate in the network. In modern video stream compression technologies, which include HEVC and VVC, the control of information intensity (bitrate) is carried out depending on the level of the MSE indicator (mean square error between the original and reconstructed video frames of the stream). Such actions are performed at the stage of quad-tree construction and selection of the quantization step of the transformed residual blocks. However, existing approaches to controlling the video stream compression mode have certain disadvantages. An option to localize and reduce the impact of such disadvantages on the efficiency of video stream compression technology consists of pre-classifying video blocks by their informativeness level. This will simultaneously allow: identifying video frame fragments for which a specific quad-tree structure will be recommended; establishing recommendations regarding the limits of changing the quantization step with an acceptable impact on the level of video data integrity. Therefore, the purpose of the article's research concerns the development of a method for classifying video blocks according to their level of impact on the "bitrate - mean square error" system of indicators in video information stream compression technologies. The article presents the development of a method for determining a metric for associative evaluation and classification of local segments by their capabilities, taking into account a set of structural and statistical properties regarding the elimination of redundancy amount and information hiding. The metric is built taking into account: the presence of structural, statistical, and psychovisual dependencies in the luminance space; the presence of a distributed background at the corners of the segment; the proportionality of the impact of statistical and structural dependencies on the value of the classification metric.
References
1. Barannik, V., Barannik, D., Babenko, M., Proko-penko, R., Akimov, O., & Petrukha, N. (2026). Devising a method for complex steganographic embedding of information in the structural-psychovisual space. Eastern-European Journal of Enterprise Technologies, 1(9 (139), 19–30. https://doi.org/10.15587/1729-4061.2026.351181.
2. Alakuijala, J., Boukortt, S., Ebrahimi, T., Kliuch-nikov, E., Sneyers, J., Upenik, E., Vandevenne, L.,, Versari, L., & Wassenberg, J. Benchmarking JPEG XL image compression. Optics, Photonics and Digital Technologies for Imaging Applica-tions VI, SPIE, 2020, vol. 11353, pp. 187–206. DOI: 10.1117/12.2556264.
3. A. Alimpiev, V. Barannik, S. Podlesny, O. Suprun and A. Bekirov, "The video information resources integrity concept by using binomial slots," 2017 XIIIth International Conference on Perspective Technologies and Methods in MEMS Design (MEMSTECH), Lviv, Ukraine, 2017, pp. 193–196, doi: 10.1109/MEMSTECH.2017.7937564.
4. Fang Y. Distributed Arithmetic Coding for Uniform Sources / Y. Fang // IEEE Transactions on Information Theory. 2023. Vol. 69. no. 1.
P. 47–74. DOI: https://doi.org/10.1109/TIT.2022. 3221289.
5. Barannik V., Sidchenko S., Barannik D., Barannik V., Datsun, A. (2021). Devising a conceptual method for generating cryptocompression codograms of images without loss of information quality. Eastern-European Journal of Enterprise Techno-logies, 4 (2 (112)), 6–16. doi: https://doi.org/-10.15587/-1729-4061.2021.237359.
6. Bross B., Chen J., Kounnas C., Ohm J., Sullivan G. J., Wang Y.-K., Xu J., & Ye, Y. General video coding technology in responses to the joint call for proposals on video compression with capability beyond HEVC. IEEE Transactions on Circuits and Systems for Video Technology. 2020. vol. 30.
no. 5. Р. 1226–1240. DOI: 10.1109/TCSVT.2019. 2949619.
7. Esenlik S. An Overview of the JPEG AI Learning-Based Image Coding Standard [Електронний ресурс] / S. Esenlik, Y. Wu, Z. Zhang [et al.]. – 2025. – (Preprint / arXiv:2510.13867). – URL: https://arxiv.org/abs/2510.13867 (дата звернення: 23.12.2025). DOI: https://doi.org/10.48550/arXiv. 2510.13867.
8. Barannik V. V., Krivonos V. N., Hahanova A. V. "Coding tangible component of transforms to provide accessibility and integrity of video data," East-West Design & Test Symposium (EWDTS 2013), 2013, pp. 1–5, doi: 10.1109/EWDTS.2013. 6673179.
9. Bross B. Overview of the Versatile Video Coding (VVC) Standard and its Applications / B. Bross [et al.] // IEEE Transactions on Circuits and Systems for Video Technology. 2021. Vol. 31, no. 10.
P. 3736–3764. DOI: 10.1109/TCSVT.2021.3101953.
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. V. Barannik and A. Shiryaev, "Quadrature compression of images in polyadic space," Proceedings of International Conference on Modern Problem of Radio Engineering, Telecommunica-tions and Computer Science, 2012, pp. 422–422. INSPEC Accession Number: 12713484.
12. Hendrawan A. A novel YOLO-aria approach for real-time vehicle detection and classification in urban traffic / A. Hendrawan, R. Gernowo,
O. D. Nurhayati1, C. Dewi // International Journal of Innovative Engineering and Sciences. 2024. Vol. 9, no. 2. P. 38–44. DOI: https://doi.org/ 10.22266/ijies2024.0229.38.
13. Krasnorutsky A. et al.: "The Methods of Intellec-tual Processing of Video Frames in Coding Systems in Progress Aeromonitor to Increase Efficiency and Semantic Integrity," 2022 IEEE 4th International Conference on Advanced Trends in Information Theory (ATIT), Kyiv, Ukraine, 2022, pp. 53–56, doi: 10.1109/ATIT58178.2022. 10024208.
14. Hussain J.A., Al-Fayadh A. and Radi N. Image compression techniques: A survey in lossless and lossy algorithms. Neurocomputing. 2018. vol. 300. Р. 44–69, doi: https://doi.org/10.1016/ j.neucom.2018.02.094
15. Joint Photographic Experts Group. JPEG AI Common Training and Test Conditions (CTTC). – [S.l.], 2022. – (ISO/IEC JTC1/SC29/WG1 (JPEG) Document ; No. WG1N100106).
16. Kountouris M., & Pappas N. Semantics-empowered communication for networked intelligent systems. IEEE Communications Magazine. 2021. vol. 59. no. 6. Р. 96–102. DOI: 10.1109/MCOM.001. 2000604.
17. 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 Enginee-ring, vol 965. Springer, Cham. https://doi.org/10.1007/ 978-3-031-24963-1_25.
18. Kim J. AI-based content-aware encoding at scale utilizing hardware resources in video ASICs for data center / J. Kim, A. Fomina, E. Baek [et al.] // Applications of Digital Image Processing XLVII: proceedings of SPIE. – 2024. – Vol. 13165. – Art. no. 1316503. – ISSN 0277-786X. DOI: https://doi.org/ 10.1117/12.3031558.
19. V. Barannik, S. Shulgin, N. Barannik and
V. Barannik, "Method of Coding Subbands of Non-Homogeneous Spectrum of Video Segments in Uneven Diagonal Space," 2022 IEEE 4th International Conference on Advanced Trends in Information Theory (ATIT), Kyiv, Ukraine, 2022, pp. 72–75, doi: 10.1109/ATIT58178.2022.10024236.
20. Krasnorutsky A. et al. "Method of Structural-Statistical Coding of Video Segments in Spectral-Cluster Space," 2022 IEEE 4th International Conference on Advanced Trends in Information Theory (ATIT), Kyiv, Ukraine, 2022, pp. 32–37, doi: 10.1109/ATIT58178.2022.10024240.
21. Horvat T., Livada Č., & Baumgartner A. Novel Block Sorting and Symbol Prediction Algorithm for PDE-Based Lossless Image Compression: A Comparative Study with JPEG and JPEG 2000. Applied sciences. 2023. vol. 13. iss. 5. article no. 3152, pp. 2–34. DOI: 10.3390/app13053152.
22. Баранник В. В. Метод сжатия изображений на основе неравновесного позиционного кодиро-вания битовых плоскостей / В. В. Баранник, Н. К. Гулак, Н. А. Королева // Радіоелектронні і Радіоелектронні і комп’ютерні системи. 2009. – № 1. С. 84–92. Режим доступу: http://nbuv.gov.ua/UJRN/recs_ 2009_1_13
23. Ma Z. Deep Lossless Compression Algorithm Based on Arithmetic Coding for Power Data /
Z. Ma, H. Zhu, Z. He, Y. Lu, F. Song // Sensors. 2022. Vol. 22. Art. no. 5331. DOI: https://doi.org/ 10.3390/s22145331.
24. Barannik V. et al.: The method of masking over-head compaction in video compression systems, Radioelectronic and Computer Systems, 2(2021), 2021, 51–63. https://doi.org/10.32620/reks.2021. 2.05.
25. Babenko Y. et al. "Method Taking into Account Level of Structural and Statistical Saturation of Video Segments in the Coding Process," 2022 IEEE 4th International Conference on Advanced Trends in Information Theory (ATIT), Kyiv, Ukraine, 2022, pp. 66–71, doi: 10.1109/ATIT58178. 2022.10024193.
26. Barannik V., Krasnorutsky A., Kolesnyk V., Barannik V., Pchelnikov S., Zeleny P. Method of compression and ensuring the fidelity of video images in infocommunication networks. Radioelectronic and computer systems, 2022, vol. 4, pp. 129–142. DOI: https://doi.org/10.32620/ reks.2022.4.10.
27. Rojas-Hernández, R., Díaz-de-León-Santiago, J. L., Barceló-Alonso, G., Bautista-López, J., Trujillo-Mora, V., & Salgado-Ramírez, J. C. Lossless medical image compression by using difference transform. Entropy. 2022. vol. 24. no. 7. article no. 951. DOI: 10.3390/e24070951.
28. Баранник В. В. Рельефное представление изоб-ражений пирамидальным кодированием. Інформаційно-керуючи системи на залізнич-ному транспорті. 2001. № 1. С. 17–25.
29. Pfaff J., Nguyen T. D. T., Schwarz H., Marpe D., & Wiegand T. Intra prediction and mode coding in VVC. IEEE Transactions on Circuits and Systems for Video Technology. 2021. vol. 31. no. 10. Р. 3834–3847. DOI: 10.1109/TCSVT.2021. 3072430.
30. Shao X., & Johnson S. G. Type-IV DCT, DST, and MDCT algorithms with reduced numbers of arithmetic operations. Signal Processing. 2008. vol. 88(6). Р. 1313–1326. DOI: 10.1016/j.sigpro. 2007. 11.024.
31. Radosavljević M., Brkljač B., Lugonja P., Crnojević V., Trpovski Ž., Xiong Z., Vukobratović D. Lossy Compression of Multi-spectral Satellite Images with Application to Crop Thematic Mapping: A HEVC Comparative Study. Remote Sensing. 2020. vol. 12. 1590. doi: https://doi.org/10.3390/rs12101590.
32. Barannik, V., et al. Description of the OFDM symbol with the help of mathematical laws. Analysis of technologies that were used in this case (2017) 2nd International Conference on Advanced Information and Communication Technologies, AICT 2017 – Proceedings, art.
no. 8020095, pp. 183–187. doi: 10.1109/AIACT.2017. 8020095.
33. Бараннік, В., & Перцев, П. (2026). Метод виз-начення інформативності сегментів для систем інтелектуальної обробки відеокон-тенту. Наукоємні технології, 70(2), 212–219. https://doi.org/ 10.18372/2310-5461.70.21197
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