THE METHOD OF ADAPTIVE STATISTICAL CODING TAKING INTO ACCOUNT THE STRUCTURAL FEATURES OF VIDEO IMAGES
Volodymyr Barannik
vvbar.off@gmail.comV. N. Karazin Kharkiv National University (Ukraine)
https://orcid.org/0000-0002-2848-4524
Dmytro Havrylov
Іvan Kozhedub Kharkiv National Air Force University (Ukraine)
Serhii Pantas
Heroes of Kruty Military Institute of Telecommunications and Informatization (Ukraine)
Yurii Tsimura
Heroes of Kruty Military Institute of Telecommunications and Informatization (Ukraine)
Tatayna Belikova
Kharkiv National University of Radio Electronics (Ukraine)
Rimma Viedienieva
National University of Civil Defense of Ukraine (Ukraine)
Vasyl Kryshtal
National University of Civil Defense of Ukraine (Ukraine)
Abstract
The paper proposes a method of improved adaptive integral arithmetic coding. This method is advisable to use in the technology of multi-level processing of video data based on the JPEG method. The technology is based on the detection of key information at several stages of video data processing. To reduce the output volume, the RLE algorithm and integral arithmetic coding are adapted to the new structure of the input data. Thus, the method of linearization of two-dimensional transformants based on zig-zag scanning was further developed. The differences of the method consist in carrying out vector intertransformation zig-zag linearization taking into account the selection of spectral components defined as complementary. The linearized decomposition approach was developed for the first time transformants based on entry into control ranges. In connection with the presence of different types of transformants in the group, the threshold is adapted according to the criterion of the total uneven number of non-equilibrium complementary components. On the basis of taking into account the probability of occurrence of dictionary elements, integrated arithmetic coding (two-dictionary integrated arithmetic coding) has been improved. Determination of current code components according to the decomposed working interval depending on the power of the dictionaries of significant elements and the number of repetitions. This allows you to additionally take into account the statistical features of the components of the RLE-structured linearized transformants and reduce the length of the arithmetic code; for the first time, a transformant compression method was created based on the reduction of various types of redundancy in groups of transformants. Comparative experimental analysis with known methods indicated that the developed technology has a higher compression ratio with reduced processing time. This makes it possible to ensure the necessary level of access and reliability in the conditions of the growth of the original volume of data.
Keywords:
method of multilevel selective processing, RLE, arithmetic codingReferences
[1] Alakuijala J. et al.: JPEG XL next-generation image compression architecture and coding tools. Proc. SPIE 11137, 2019, 111370K.
Google Scholar
[2] Alakuijala J.: Image compression benchmark [https://drive.google.com/corp/ drive/folders/0B0w_eoSgaBLXY1JlYUVOMzM5VFk] (access 2024/09/08).
Google Scholar
[3] Barannik D. et. al.: Steganographic Coding Technology for Hiding Information in Infocommunication Systems of Critical Infrastructure. 4th International Conference on Advanced Trends in Information Theory (ATIT). 2022, 88–91 [https://doi.org/10.1109/ATIT58178.2022.10024185].
Google Scholar
[4] Barannik V. et. al.: Evaluation of Effectiveness of Masking Methods of Aerial Photographs. 3rd International Conference on Advanced Information and Communications Technologies (AICT). 2019, 415–418 [https://doi.org/10.1109/AIACT.2019.8847820].
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].
Google Scholar
[6] Bondžulić B. et. al.: Picture-wise just noticeable difference prediction model for JPEG image quality assessment. Vojnotehnički glasnik 70(1), 2022, 62–86 [https://doi.org/10.5937/vojtehg70-34739].
Google Scholar
[7] Cardone B.: Fuzzy Transform Image Compression in the YUV Space. Computation 11(10), 2023, 1–19
Google Scholar
[https://doi.org/10.3390/computation11100191].
Google Scholar
[8] Chen T. et. al.: End-to-End Learnt Image Compression via Non-Local Attention Optimization and Improved Context Modeling. IEEE Transactions on Image Processing, 2021, 3179–3191 [https://doi.org/10.1109/tip.2021.3058615].
Google Scholar
[9] Cho J., Kwon O.-J., Choi S.: Improvement of JPEG XL Lossy Image Coding Using Region Adaptive DCT Block Partitioning Structure. IEEE Access 9, 113213–113225 [https://doi.org/10.1109/ACCESS.2021.3102235].
Google Scholar
[10] Duda J.: Asymmetric Numeral System implementation by Andrew Polar [http://ezcodesample.com/abs/abs_article.html] (access 2023/05/14).
Google Scholar
[11] Duda J.: Asymmetric numeral systems. arXiv:0902.0271. [https://doi.org/10.48550/arXiv.0902.0271] (access 2024/09/08).
Google Scholar
[12] Gonzalez R. et. al.: Digital Image Processing. 4th Edition. Pearson Education, 2018.
Google Scholar
[13] ITU-T Recommendation H.265. High efficiency video coding. 2019 [https://www.itu.int/ rec/T-REC-H.265] (access 2024/09/08).
Google Scholar
[14] Jinming L. et. al.: Learned Image Compression with Mixed Transformer-CNN Architectures. IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2023, 14388–14397 [https://doi.org/10.1109/CVPR52729.2023.01383].
Google Scholar
[15] Kim I. et. al.: High efficiency video coding (HEVC) test model 12 (HM12) encoder description. JCTVC 14th meeting, 2013, JCTVC-N1002.
Google Scholar
[16] Landu R. S.: Image Compression Using AI: Brief Insights into Deep Learning Techniques and AI Frameworks. International Journal of Engineering, Science, Technology and Innovation (IJESTI) 2(1), 2022, 1–6.
Google Scholar
[17] Li C. et al.: Yolov6: A single-stage object detection framework for industrial applications. arXiv preprint arXiv:2209.02976, 2022.
Google Scholar
[18] Li H. et al.: Frequency-aware Transformer for Learned Image Compression. ICLR, 2024, 19, arXiv:2310.16387 (access 2024/09/08).
Google Scholar
[19] Liao S. et. al.: Rate-Quality Based Rate Control Model for Neural Video Compression. International Conference on Acoustics, Speech and Signal Processing (ICASSP), 2024, 4215–4219 [https://doi.org/10.1109/ICASSP48485.2024.10447777].
Google Scholar
[20] Lopes F. A. et. al.: FPGA implementation of the JPEG XR for onboard earth observation applications. Journal of Real-Time Image Processing 18(6), 2021, 1–12 [https://doi.org/10.1007/s11554-021-01078-y].
Google Scholar
[21] Park W. et. al.: Fast Computation of Integer DCT-V, DCT-VIII, and DST-VII for Video Coding. IEEE Transactions on Image Processing 28(12), 2019, 5839–5851.
Google Scholar
[22] Ponomarenko N. et. al.: Image database TID2013: Peculiarities, results and perspectives. Signal Processing: Image Communication 30, 2015, 57–77.
Google Scholar
[23] Rao K. et. al.: JPEG Series. 1st edition. River Publishers, 2021.
Google Scholar
[24] Ren S. et al.: Faster R-CNN: Towards real-time object detection with region proposal networks. NeurIPS, 2015, arXiv:1506.01497.
Google Scholar
[25] Russ J. C., Neal F. B.: The Image Processing Handbook. 7th Edition. CRC Press, 2018.
Google Scholar
[26] Sneyers J.: Improve the Web Experience With Progressive Image DecodingImprove the Web Experience With Progressive Image Decoding, 2021 [https://cloudinary.com/blog/improve_the_web_experience_with_progressive_image_decoding] (access 2024/09/08).
Google Scholar
[27] Umbaugh S. E.: Digital Image Processing and Analysis: Computer Vision and Image Analysis 4th Edition. Taylor & Francis Ltd 2023.
Google Scholar
[28] Wassenberg J. et. al.: DIS Text of ISO IEC 18181-1 (JPEG XL), document JPEG (ISO/IEC JTC 1/SC 29/WG 1). 86th Meeting, 2020.
Google Scholar
[29] Wassenberg J., Sneyers J.: JPEG XL White Paper, document JPEG(ISO/IEC JTC 1/SC 29/WG 1). 87th Meeting. Germany, Erlangen, 04.2020, N87021, 27–30.
Google Scholar
[30] Wiegand T. et. al.: Overview of the H.264/AVC Video Coding Standard. IEEE Transactions on Circuits and Systems for Video Technology 13(7), 2003, 560–576.
Google Scholar
[31] Zhang X.: Shufflenet: An extremely efficient convolutional neural network for mobile devices. CVPR, 2018, arXiv:1707.01083.
Google Scholar
Authors
Volodymyr Barannikvvbar.off@gmail.com
V. N. Karazin Kharkiv National University Ukraine
https://orcid.org/0000-0002-2848-4524
Authors
Dmytro HavrylovІvan Kozhedub Kharkiv National Air Force University Ukraine
Authors
Serhii PantasHeroes of Kruty Military Institute of Telecommunications and Informatization Ukraine
Authors
Yurii TsimuraHeroes of Kruty Military Institute of Telecommunications and Informatization Ukraine
Authors
Tatayna BelikovaKharkiv National University of Radio Electronics Ukraine
Authors
Rimma ViedienievaNational University of Civil Defense of Ukraine Ukraine
Authors
Vasyl KryshtalNational University of Civil Defense of Ukraine Ukraine
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