METHOD FOR DETERMINING THE INFORMATIVENESS OF SEGMENTS FOR INTELLI-GENT VIDEO CONTENT PROCESSING SYSTEMS
DOI:
https://doi.org/10.18372/2310-5461.70.21197Keywords:
video frame segments, informativeness, video data coding, metrics, information and communication systemsAbstract
The article shows that currently a number of effective modern video data encoding technologies have been created, which are based on standardized video frame compression formats and dynamic streams. These include: H265/HEVC and H266/VVC. Accordingly, a number of directions are outlined for taking into account the features (complexity) of the content for these technologies. The first type of approaches is based on the principle of reducing the bitrate while complying with the requirements for the level of root-mean-square indicators, which determine the level of integrity. The disadvantages here are: increasing the complexity of processing due to the multi-iterative process of constructing a syntax tree; the possibility of making only a rough estimate of the bitrate level before the compression process (entropy coding) begins. The second direction is based on approaches based on the use of artificial intelligence models. At the same time, this approach has certain limitations. This concerns the increase in the complexity of processing and time delays in the process of learning and forming a latent space. In addition, errors occur in the process of identifying objects. One of the effective directions for increasing the efficiency of semantic selection of video segments in the compression process is the development of combined approaches. In this case, the basic one is the identification (classification) of segments by the level of semantic complexity on the basis of a set of features that are detected at the level of syntactic perception of video information. The main task here is the selection of such features that will simultaneously have an associative dependence with the level of semantic complexity of video segments and the permissible energy efficiency of the computational process. Methodological foundations have been developed for the threshold-metric classification of standardized segments by size into two basic classes based on the scaling of the results of division into classes of its local segments. The basic ones here are: squaring the segment by local segments with the formation of an information model for each of them by a set of structural and statistical parameters; Scaling to establish the level of complexity of a video image segment from the perspective of the potential of its features with respect to the presence of redundancy is carried out on the basis of a decision rule based on the analysis of frequency information of the results of dividing local segments into two basic classes; determination of the complexity class of local segments based on relating the metric to one of two permissible threshold intervals with a defined limit.
References
Alakuijala, J., Boukortt, S., at al. Benchmarking JPEG XL image compression. Optics, Photonics and Digital Technologies for Imaging Applications VI, SPIE, 2020, vol. 11353, pp. 187–206. https://doi.org/ 10.1117/12.2556264.
Barannik, V., Barannik, D., Babenko, M., Prokopenko, R., Akimov, O., & Petrukha, N. (2026). Devising a method for complex stegano-graphic embedding of information in the structural-psychovisual space. Eastern-European Journal of Enterprise Technologies, 1(9 (139),
–30. https://doi.org/10.15587/1729-4061.2026. 351181.
Barannik, V., Yudin, O., Boiko, Y., Ziubina, R., Vyshnevska, N. (2019). Video Data Compression Methods in the Decision Support Systems. In:
Hu, Z., Petoukhov, S., Dychka, I., He, M. (eds) Advances in Computer Science for Engineering and Education. ICCSEEA 2018. Advances in Intelligent Systems and Computing, vol 754. Springer, Cham. https://doi.org/10.1007/978-3-319-91008-6_30.
Barannik, V., Sidchenko, S., Barannik, D., Barannik, V., Datsun, A. (2021). Devising a conceptual method for generating cryptocompres-sion codograms of images without loss of information quality. Eastern-European Journal of Enterprise Technologies, 4 (2 (112)), 6–16. https://doi.org/10.15587/1729-4061.2021.237359.
Бараннік В. В. Основи теорії структурно-комбі-наторного стеганографічного кодування: моно-графія / В. В. Бараннік, Д. В. Бараннік, Бекиров А. Е. Х.: «Лідер», 2017. 256 с.
Fang Y. Distributed Arithmetic Coding for Uniform Sources. IEEE Transactions on Information Theory. 2023. Vol. 69. no. 1. P. 47–74. https://doi.org/10.1109/ TIT.2022.3221289 .
Alimpiev A., Barannik V., Podlesny S., Suprun O. and Bekirov A. "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, https://doi.org/10.1109/MEMSTECH.2017.7937564
Barannik V. V., Hahanova A. V. and Krivonos V. N. "Coding tangible component of transforms to provide accessibility and integrity of video data," East-West Design & Test Symposium (EWDTS 2013), 2013, pp. 1–5, https://doi.org/ 10.1109/EWDTS.2013.6673179
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). https://doi.org/10.48550/arXiv.2510. 13867
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. https://doi.org/ 10.1109/TCSVT.2019.2949619
V. Barannik and A. Shiryaev, "Quadrature compression of images in polyadic space," Proceedings of International Conference on Modern Problem of Radio Engineering, Tele-communications and Computer Science, 2012, pp. 422–422. INSPEC Accession Number: 12713484.
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 .
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. https://doi.org/10.1109/ TCSVT. 2021.3101953
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. https://doi.org/10.22266/ijies 2024.0229.38 .
Hussain J. A. at all. Image compression techniques: A survey in lossless and lossy algorithms. Neurocomputing. 2018. vol. 300. Р. 44–69, https://doi.org/10.1016/j.neucom.2018.02.094
Krasnorutsky A. et al.: "The Methods of Inte-llectual 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, https://doi.org/10.1109/ ATIT58178.2022.10024208
Joint Photographic Experts Group. JPEG AI Common Training and Test Conditions (CTTC). – [S.l.], 2022. – (ISO/IEC JTC1/SC29/WG1 (JPEG) Document; No. WG1N100106).
Barannik V., Shulgin S., Barannik N. and Barannik V. "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, https://doi.org/10.1109/ATIT 58178.2022.10024236
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 Transfor-mation Age. TCSET 2022. Lecture Notes in Electrical Engineering, vol 965. Springer, Cham. https://doi.org/10.1007/978-3-031-24963-1_25.
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. https://doi.org/10.1117/ 12.3031558
Kountouris M., & Pappas N. Semantics-empo-wered communication for networked intelligent systems. IEEE Communications Magazine. 2021. vol. 59. no. 6. Р. 96–102. https://doi.org/10.1109/ MCOM.001.2000604
Баранник В. В. та ін. Метод стиснення зобра-жень на основі нерівновісного позиційного кодування бітових площин. Радіоелектронні і комп’ютерні системи. 2009. № 1. С. 84–92. URL: http://nbuv.gov.ua/UJRN/recs_2009_1_13
Livada Č., Horvat T., & 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. https://doi.org/10.3390/app 13053152
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, https://doi.org/10.1109/ATIT58178.2022.10024240
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, https://doi.org/10.1109/ ATIT58178.2022.10024193
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
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. https://doi.org/ 10.3390/s22145331
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. https://doi.org/10.32620/reks. 2022.4.10.
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. https://doi.org/10.1109/ TCSVT.2021.3072430
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. https://doi.org/10.3390/rs12101590
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. https://doi.org/10.3390/e24070951
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. https://doi.org/10.1016/ j.sigpro.2007. 11.024
Бараннік В. В. Рельефне представлення зобра-жень пірамідальним кодуванням. Інформаційно- керуючи системи на залізничному транспорті. 2001. № 1. С. 17–25.
Barannik, V., Lytvinenko, M., Okladnoy, D., Suprun, O. 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 Infor-mation and Communication Technologies, AICT 2017 – Proceedings, art. no. 8020095, pp. 183–187. https://doi.org/10.1109/AIACT.2017.8020095
Silva D. A. A recurrent YOLOv8-based frame-work for event-based object detection / D. A. Silva, K. Smagulova, A. Elsheikh [et al.] Frontiers in Neuroscience. 2024. Vol. 18. Art. no. 1477979. https://doi.org/10.3389/fnins.2024.1477979.
Downloads
Published
How to Cite
Issue
Section
License
Copyright (c) 2026 В Бараннік
This work is licensed under a Creative Commons Attribution 4.0 International License.
The scientific journal adheres to the principles of Open Access and provides free, immediate, and permanent access to all published materials without financial, technical, or legal barriers for readers.
All articles are published in Open Access under the Creative Commons Attribution 4.0 International (CC BY 4.0) license.
Copyright
Authors who publish their works in the journal:
-
retain the copyright to their publications;
-
grant the journal the right of first publication of the article;
-
agree to the distribution of their materials under the CC BY 4.0 license;
-
have the right to reuse, archive, and distribute their works (including in institutional and subject repositories), provided that proper reference is made to the original publication in the journal.
