Saving Elements Methods for Service Components of Images Cryptocompression Codograms




compression, cryptocompression, encryption, image, information protection, privacy


The article substantiates the requirements for the quality characteristics of the video information resource in the case of its use for information support of the functioning of critical infrastructure systems. At the same time, requirements are put forward regarding: timeliness of delivery and confidentiality of video information under conditions of a given level of its integrity and completeness. It is shown that, in part, such requirements are due to the intellectualization of individual stages of the process of analysis and decision-making in critical infrastructure systems. System justification for the existence of problematic issues is provided, which include: imbalance between the levels of productivity of information communication systems and the bit intensity of video information streams. Compression coding technologies are used to reduce this imbalance. They allow you to reduce the bit volume of video data. However, they do not provide the required level of information privacy by themselves. This proves the relevance of the scientific and applied problem regarding the need to ensure the required level of promptness of delivery of confidential information using wireless information communication technologies in critical infrastructure systems.

The article shows that the direction of solving the existing problem is the creation and application of coding technologies that allow ensuring the confidentiality of video information in the process of reducing redundancy. At the same time, one of the representatives of this class of methods are those built on the basis of cryptocompression coding technologies. A service information system is being formed for such technologies. On the one hand, this creates conditions for ensuring the confidentiality of video information in the process of reducing its bit volume. On the other hand, there is a destructive effect on restraining the growth of the compression ratio. Hence, the purpose of the article is to develop a method of storing the elements of service components of cryptographic codegrams.

Three methods of storing service components of cryptographic codegrams of images are proposed. The first consists in storing service data in the form of minimum and maximum values. The second method involves reducing the dynamic range of maximum values. The third method involves storing intermediate elements or elements of service components in the form of a half-sum and a half-difference. To implement the third method of storing elements of service data, a method has been developed that organizes a forced reduction in the quality of output images. The reduction in quality is organized by introducing an error into the source images to form even and odd values of the service data elements within the entire plane of the image, its individual blocks or for each line of the block separately. The peak signal-to-noise ratio (PSNR) of the reconstructed images is at least 56.5 dB, and the value of the correlation coefficient is 0.99997. A positive effect of the implementation of the developed method is an increase in the confidentiality of codegrams due to the violation of the relationship between service data elements, an increase in the compression ratio to 2-3% and a decrease in the volume of service components of codegrams by 6.25%.



Schneier B. (2015). Applied Cryptography: Protocols, Algorithms, and Source Code in C. Wiley, 784 p.

Ramakrishnan S. (2018). Cryptographic and Information Security Approaches for Images and Videos. CRC Press, Taylor & Francis Group, 962 p. doi:10.1201/9780429435461.

Announcing the ADVANCED ENCRYPTION STANDARD (AES). Federal Information Processing Standards Publication 197 (2001). Defense Technical Information Center.

Information technology – JPEG 2000 image coding system: Secure JPEG 2000. International Standard ISO/IEC 15444-8; ITU-T Recommendation T.807. (2007). 108 p.

Dufaux F., Ebrahimi T. (2006). Toward a Secure JPEG. Applications of Digital Image Processing XXIX, Vol. 6312. DOI: 10.1117/12.686963.

Miano J. (1999). Compressed image file formats: JPEG, PNG, GIF, XBM, BMP. ACM Press/Addison-Wesley Publishing Co., 264 p.

Joint Photographic Experts Group (JPEG). Information technology – digital compression and coding of continuous-tone still images: Requirements and guidelines. ISO/IEC 10918-1:1994, ITU/CCITT Recommendation T.81. (1992–2017). 182 p.

Sharma R., Bollavarapu S. (2015). Data Security using Compression and Cryptography Techniques. International Journal of Computer Application, Vol. 117, № 14, pp. 15–18. DOI: 10.5120/20621-3342.

Cisco Visual Networking Index: Global Mobile Data Traffic Forecast Update, 2017–2022. (2019). Cisco. 33 p.

Cisco Annual Internet Report (2018–2023). (2020). Cisco. 35 p.

Yuan L., Korshunov P., Ebrahimi T. (2015). Secure JPEG Scrambling enabling Privacy in Photo Sharing. 2015 11th IEEE International Conference and Workshops on Automatic Face and Gesture Recognition (FG), pp. 1-6. DOI: 10.1109/FG.2015.7285022.

Wong, K.-W. (2009). Image Encryption Using Chaotic Maps. In: Kocarev, L., Galias, Z., Lian, S. (eds) Intelligent Computing Based on Chaos. Studies in Computational Intelligence, Vol. 184. Springer. doi: 10.1007/978-3-540-95972-4_16.

Phatak A. G. (2016). A Non-format Compliant Scalable RSA-based JPEG Encryption Algorithm. International Journal of Image, Graphics and Signal Processing, Vol. 8, № 6, pp. 64–71. DOI: 10.5815/ijigsp.2016.06.08.

Yang Y., Zhu B., Li S., Yu N. (2007). Efficient and Syntax-Compliant JPEG 2000 Encryption Preserving Original Fine Granularity of Scalability. EURASIP Journal on Information Security, Vol. 2007, Iss. 1, 13 p. DOI:10.1186/1687-417X-2007-056365.

Chen Ch., Wu W. (2014). A secure Boolean-based multi-secret image sharing scheme. Journal of Systems and Software, Vol. 92, pp. 107–114. DOI: 1016/j.jss.2014.01.001.

Tsai Ch.-L., Chen Ch.-J., Hsu W.-L. (2012). Multi-morphological image data hiding based on the application of Rubik’s cubic algorithm. IEEE International Carnahan Conference on Security Technology ICCST, pp. 135–139. DOI: 10.1109/CCST.2012.6393548.

Tverdokhlib V., Zakomorna K., Dvukhglavov D., Oleksin O., Zhuikov D., Kryvonos V. (2021). Technology Increasing Capacity Protected Channel Delivery Video Data Telecommunication Systems Critical Infrastructure. IEEE 3rd International Conference on Advanced Trends in Information Theory (ATIT), Kyiv, Ukraine, pp. 57-60. DOI: 10.1109/ATIT54053.2021.9678736.

Dick K., Russell L., Dosso Y., Kwamena F., Green J. (2019). Deep Learning for Critical Infrastructure Resilience. Journal of Infrastructure Systems, Vol. 25, Iss. 2, 11 p. DOI: 10.1061/(ASCE)IS.1943-555X.0000477.

Isern J., Barranco F., Deniz D., Lesonen J., Hannuksela J., Carrillo R. (2020). Reconfigurable cyber-physical system for critical infrastructure protection in smart cities via smart video-surveillance. Pattern Recognition Letters, Vol. 140, pp. 303-309. DOI: 10.1016/j.patrec.2020.11.004.

Gonzalez R., Woods R. (2018). Digital Image Processing. Pearson, 1168 p.

Salomon D. (2007). Data Compression: The Complete Reference. Springer, 1092 p.

Gore A, Gupta S. (2015). Full reference image quality metrics for JPEG compressed images. AEU – International Journal of Electronics and Communications, Vol. 69, Iss. 2, pp. 604–608. DOI: 10.1016/j.aeue.2014.09.002.

Cogranne R. (2018). Determining JPEG Image Standard Quality Factor from the Quantization Tables, 6 p. Cornell University.

Wu Yu., Agaian S., Noonan J. (2012). Sudoku Associated Two Dimensional Bijections for Image Scrambling. Cornell University.

Auer S., Bliem A., Engel D. et al. (2013). Bitstream-based JPEG Encryption in Real-time. International Journal of Digital Crime and Forensics, Vol. 5, Iss. 3, pp. 1-14. DOI: 10.4018/jdcf.2013070101.

Minemura K., Moayed Z., Wong K. et al. (2012). JPEG image scrambling without expansion in bitstream size. 2012 19th IEEE International Conference on Image Processing, pp. 261–264. DOI: 10.1109/ICIP.2012.6466845.

Barannik V., Sidchenko S., Barannik D. (2020). Technology for Protecting Video Information Resources in the Info-Communication Space. 2020 IEEE 2nd International Conference on Advanced Trends in Information Theory (ATIT), pp. 29–33. DOI: 10.1109/ATIT50783.2020.9349324.

Alimpiev A., Barannik V., Sidchenko S. (2017). The method of cryptocompression presentation of videoinformation resources in a generalized structurally positioned space. Telecommunications and Radio Engineering, Vol. 76, № 6, pp. 521–534. DOI: 10.1615/TelecomRadEng.v76.i6.60.

Barannik V., Sidchenko S., Barannik N., Barannik V. (2021). Development of the method for encoding service data in cryptocompression image representation systems. Eastern-European Journal of Enterprise Technologies, Vol. 3, № 9(111), pp. 103–115. DOI: 10.15587/1729-4061.2021.235521.

Barannik V., Sidchenko S., Barannik D., Shulgin S., 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 Technologies, Vol. 4, № 2(112), pp. 6–17. DOI: 10.15587/1729-4061.2021.237359.

Barannik V., Sidchenko S., Barannik N., Barannik D., Shulgin S. (2021). Methods for Decoding Informational Codes of Cryptocompression Codegrams to Improve Information Security. CEUR Workshop Proceedings (, Vol. 2923, pp. 143–152.

Barannik V., Sidchenko S., Barannik N., Khimenko A. (2021). The method of masking overhead compaction in video compression systems. Radioelectronic and Computer Systems, No. 2, pp. 51–63. DOI: 10.32620/reks.2021.2.05.

Barannik V., Sidchenko S., Barannik D., Barannik V., Hurzhii I., Babenko Y. (2021). Evaluating of the Resistance of Crypto-Compression Image Codograms to Errors in the Data Transmission Channel. 2021 IEEE 3rd International Conference on Advanced Trends in Information Theory (ATIT), Kyiv, pp. 52–56. DOI: 10.1109/ATIT54053.2021.9678774.

School of Electronic Engineering and Computer Science. ECS605U/ECS776P – Image Processing.

Wu Y., Noonan J. P., Agaian S. (2011). NPCR and UACI Randomness Tests for Image Encryption. Cyber Journals: Multidisciplinary Journals in Science and Technology, Journal of Selected Areas in Telecommunications (JSAT), April Edition, pp. 31–38.



How to Cite

Бараннік , В. В., Сідченко , С. О., Бараннік , Д. В., Чорномаз , І. К., Гуржій , П. М. 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.



Telecommunication, navigation, radar systems, radiooptics and electroacoustics