A METHOD FOR FORMING A TRUNCATED POSITIONAL CODE SYSTEM FOR TRANSFORMED VIDEO IMAGES

[1] Auer S. et al.: Bitstream-based JPEG Encryption in Real-time. International Journal of Digital Crime and Forensics 5(3), 2013, 1–14 [https://doi.org/10.4018/jdcf.2013070101].
DOI: https://doi.org/10.4018/jdcf.2013070101   Google Scholar

[2] Barannik D., Barannik V.: Steganographic Coding Technology for Hiding Information in Infocommunication Systems of Critical Infrastructure. IEEE 4th International Conference on Advanced Trends in Information Theory (ATIT), 2022, 88–91 [https://doi.org/10.1109/ATIT58178.2022.10024185].
DOI: https://doi.org/10.1109/ATIT58178.2022.10024185   Google Scholar

[3] Barannik V. et al.: A method to control bit rate while compressing predicted frames. Proceedings of 13th International Conference: The Experience of Designing and Application of CAD Systems in Microelectronics (CADSM), 2015, 36–38 [https://doi.org/10.1109/CADSM.2015.7230789].
DOI: https://doi.org/10.1109/CADSM.2015.7230789   Google Scholar

[4] Barannik V. et al.: Detections of sustainable areas for steganographic embedding. IEEE East-West Design & Test Symposium (EWDTS), 2017, 1–4 [https://doi.org/10.1109/EWDTS.2017.8110028].
DOI: https://doi.org/10.1109/EWDTS.2017.8110028   Google Scholar

[5] Barannik V. et al.: Method of indirect information hiding in the process of video compression. Radioelectronic and Computer Systems 4, 2021, 119–131 [https://doi.org/10.32620/reks.2021.4].
DOI: https://doi.org/10.32620/reks.2021.4   Google Scholar

[6] Barannik V., Barannik N.: Indirect information hiding technology on a multiadic basis. Informatyka, Automatyka, Pomiary w Gospodarce i Ochronie Środowiska – IAPGOS 11(3), 2021, 14 – 17 [https://doi.org/10.35784/iapgos.2812].
DOI: https://doi.org/10.35784/iapgos.2713   Google Scholar

[7] Bernardo M. V. et al.: JPEG XL Objective Evaluation Results, document JPEG (ISO/IEC JTC 1/SC 29/WG 1). 86th Meeting, M86070, Sydney, NSW, Australia, 2020.
  Google Scholar

[8] Chunyi Li et al.: MISC: Ultra-low Bitrate Image Semantic Compression Driven by Large Multimodal Model. Journal of Latex Class Files 1(1), 2024, 1–13 [https://doi.org/arxiv-2402.16749].
  Google Scholar

[9] Descampe A. et al.: JPEG XS – A New Standard for Visually Lossless Low-Latency Lightweight Image Coding. Proceedings of the IEEE, 109(9), 2021, 1559–1577 [https://doi.org/10.1109/jproc.2021.3080916].
DOI: https://doi.org/10.1109/JPROC.2021.3080916   Google Scholar

[10] Duda J. et al.: The use of asymmetric numeral systems as an accurate replacement for Huffman coding. Picture Coding Symposium (PCS), 2015, 65–69.
DOI: https://doi.org/10.1109/PCS.2015.7170048   Google Scholar

[11] Duda J.: Asymmetric numeral systems: entropy coding combining speed of Huffman coding with compression rate of arithmetic coding, 2014.
  Google Scholar

[12] Gary J. et al.: Overview of the High Efficiency Video Coding (HEVC) Standard. IEEE Transactions on Circuits and Systems for Video Technology, 2012.
  Google Scholar

[13] Hasler D., Suesstrunk S. E.: Measuring colorfulness in natural images. Proc. SPIE 5007, 2003, 87–95.
DOI: https://doi.org/10.1117/12.477378   Google Scholar

[14] Helin H. et al.: Optimized JPEG 2000 Compression for Efficient Storage of Histopathological Whole-Slide Images. Journal of pathology informatics 9, 2018 [https://doi.org/10.4103/jpi.jpi_69_17].
DOI: https://doi.org/10.4103/jpi.jpi_69_17   Google Scholar

[15] How JPEG XL Compares to Other Image Codecs https://cloudinary.com/blog/how_jpeg_xl_compares_to_other_image_codecs.
  Google Scholar

[16] Iwahashi M., Kiya H.: Non Separable Two Dimensional Discrete Wavelet Transform for Image Signals. Discrete Wavelet Transforms, 2013 [https://doi.org/10.5772/51199].
DOI: https://doi.org/10.5772/51199   Google Scholar

[17] JPEG XL: How It Started, How It’s Going https://cloudinary.com/blog/jpeg-xl-how-it-started-how-its-going.
  Google Scholar

[18] Li F., Lukin V.: Providing a Desired Compression Ratio for Better Portable Graphics Encoder of Color Images: Design and Analysis, Digitalization and Management Innovation. Proceedings of DMI, 2022, 633–640 [https://doi.org/10.3233/FAIA230063].
DOI: https://doi.org/10.3233/FAIA230063   Google Scholar

[19] Mallat S.: A Wavelet Tour of Signal Processing, Third Edition: The Sparse Way. 3rd edition. Academic Press, Inc., USA 2008.
  Google Scholar

[20] Ming L. et al.: Transformer-based Image Compression. Data Compression Conference (DCC), 2022, 469 [https://doi.org/10.1109/DCC52660.2022.00080].
DOI: https://doi.org/10.1109/DCC52660.2022.00080   Google Scholar

[21] Minner D. et. al.: Channel-wise Autoregressive Entropy Models for Learned Image Compression. IEEE International Conference on Image Processing (ICIP), 2020, 3339–3343 [https://doi.org/10.1109/icip40778.2020.9190935].
DOI: https://doi.org/10.1109/ICIP40778.2020.9190935   Google Scholar

[22] Mishra D. et al.: Wavelet-based Deep Auto Encoder-Decoder (WDAED) based Image Compression. IEEE Transactions on Circuits and Systems for Video Technology, 2020, 1452–1462 [https://doi.org/10.1109/TCSVT.2020.3010627].
DOI: https://doi.org/10.1109/TCSVT.2020.3010627   Google Scholar

[23] Pratt W.: Introduction to Digital Image Processing. 1th Edition. CRC Press, 2013.
DOI: https://doi.org/10.1201/b15731   Google Scholar

[24] Ramadan M. R., Suhair A. D.: Digital image compression by using intelligence swarm algorithms. International Journal of Mathematics and Computer Science, 17, 2022, 785–794.
  Google Scholar

[25] Scott E.: Umbaugh. Digital Image Processing and Analysis: Applications with MATLAB and CVIPtools. 4th Edition. CRC Press, 2017.
  Google Scholar

[26] Seow J. W. et al.: A comprehensive overview of Deepfake: Generation, detection, datasets, and opportunities. Neurocomputing 513(7), 2022, 351–371 [https://doi.org/10.1016/j.neucom.2022.09.135].
DOI: https://doi.org/10.1016/j.neucom.2022.09.135   Google Scholar

[27] Tang Y. et al.: JPEG-XR-GCP: Promoting JPEG-XR Compression by Gradient-Based Coefficient Prediction. 12th International Conference on Advanced Computational Intelligence (ICACI), 2020, 51–58 [https://doi.org/10.1109/ICACI49185.2020.9177623].
DOI: https://doi.org/10.1109/ICACI49185.2020.9177623   Google Scholar

[28] Xu J. et al.: Directional Lapped Transforms for Image Coding. IEEE Transactions on Image Processing 19(1), 2010, 85–97.
DOI: https://doi.org/10.1109/TIP.2009.2032344   Google Scholar