Method of Additional Reduction of Structural Excession of Code Representation Video Data
DOI:
https://doi.org/10.31649/1997-9266-2022-162-3-67-76Keywords:
transformation, restructuring, clustering, signs of the number of series of units, coding, video information resource dataAbstract
To date, significant development of information technology is aimed at improving existing algorithms and technologies for encoding video information resources. This is due to the constant increase in the amount of data transmitted in data channels, under the existing bandwidth constraints. In turn, the active use of wireless technologies for data transmission is accompanied by increasing demands on video information resources — a compact presentation of encrypted data while maintaining their integrity. To this end, a method of encoding video information resource data using the restructuring of the information space of encoded data is being developed. Restructuring of the information space means clustering of message elements. The tool for clustering is a quantitative feature — a sign of the number of series of units in the internal binary structure of the message elements. The essence of clustering is that elements with the same values of the number of series of units form clusters. Peculiarities of transformations of the laws of distribution of elements in the message due to the use of internal restructuring of data on a quantitative basis are investigated. The essence of the developed method of cluster statistical coding of video information resource data is that the coding of message elements occurs in the statistical space of sets formed in the clustering process. A distinctive feature of the method is to preserve the integrity of the encoded data in terms of providing additional reduction of the structural redundancy of the code representation of video data.
References
S. Wang, S. Kim, Z. Yin, and T. He, “Encode when necessary: Correlated network coding under unreliable wireless links,” ACM Transactions on Sensor Networks, vol. 13, no. 1, pp. 24-29, 2017. https://doi.org/10.1145/ 3023953.
C. Chen, and Y. Zhuo, “A research on anti-jamming method based on compressive sensing for OFDM analogous system,” in IEEE 17th International Conference on Communication Technology (ICCT), 2017, pp. 655-659, https://doi.org/10.1109/ICCT.2017.8359718.
B. Zhurakovskyi, J. Boiko, V. Druzhynin, I. Zeniv, and O. Eromenko, “Increasing the efficiency of information transmission in communication channels,” Indonesian Journal of Electrical Engineering and Computer Science, vol. 19 (3), pp. 1306-1315, 2020. https://doi.org/ 10.11591/ijeecs.v19.i3.
R. Swaminathan, and A. Madhukumar, “Classification of Error Correcting Codes and Estimation of Interleaver Parameters in a Noisy Transmission Environment,” IEEE Transactions on Broadcasting, vol. 63, no. 3, 2017, pp. 463-478. https://doi.org/10.1109/TBC.2017.2704436.
T. Richter,” Error Bounds for HDR Image Coding with JPEG XT,” Data Compression Conference (DCC), 2017, pp. 122-130. https://doi.org/ 10.1109/DCC.2017.7.
S. Wang, X. Zhang, X. Liu, J. Zhang, Ma, S., and W. Gao, “Utility Driven Adaptive Preprocessing for Screen Content Video Compression,” IEEE Transactions on Multimedia, vol. 19, no. 3, pp. 660-667, 2017.
A. Djelouah, J. Campos, S. Schaub-Meyer, and C. Schroers, “Neural Inter-Frame Compression for Video Coding,” IEEE/CVF International Conference on Computer Vision (ICCV), 2019, pp. 6420-6428. https://doi.org/10.1109/ICCV.2019.00652.
X. Zhu, L. Liu, and Na Ai P. Jin, “Morphological component decomposition combined with compressed sensing for image compression, “IEEE International Conference on Information and Automation (ICIA), https://doi.org/10.1109/ICInfA.2016.7832096.
O. Rippel, “Learned Video Compression,” IEEE/CVF International Conference on Computer Vision (ICCV), 2019, pp. 3453-3462. https://doi.org/10.1109/ICCV. 2019.00355.
Z. Wang, R. Liao, Y. Ye, “Joint Learned and Traditional Video Compression for P Frame,” IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW), 2020, pp. 560-564. https://doi.org/10.1109/CVPRW50498.2020.00075.
Y. S. Manzhos, and Y. V. Sokolova, “A Method of IoT Information Compression,” International Journal of Computing, 21(1), pp. 100-110, 2022. https://doi.org/10.47839/ijc.21.1.2523.
X. Wang, J. Xiao, R. Hu, Z. Wang, “Cruise UAV Video Compression Based on Long-Term Wide-Range Background,” Data Compression Conference (DCC), 2017, pp. 466-467. https://doi.org/10.1109/DCC.2017.71.
T. Shinde, “Efficient Image Set Compression,” IEEE International Conference on Image Processing (ICIP), 2019, pp. 3016-3017, https://doi.org/10.1109/ICIP. 2019.8803230.
C. Narmatha, P. Manimegalai, and S. Manimurugan, “A LS-compression scheme for grayscale images using pixel based technique,” International Conference on Innovations in Green Energy and Healthcare Technologies (IGEHT), 2017, pp. 1-5, https://doi.org/10.1109/ IGEHT.2017.8093980.
S. Han, H. Mao, and W. Dally, Deep compression: Compressing deep neural networks with pruning, trained quantization and huffman coding, 2015. arXiv: 1510.00149.
J. Lee, S. Cho, and S.-K. Beack, Context-adaptive entropy model for end-to-end optimized image compression, 2018. arXiv: 1809.10452.
Y. Chen, F. Wu, C. Li, and P. Varshney, “An Efficient Construction Strategy for Near-Optimal Variable-Length Error-Correcting Codes,” IEEE Communications Letters, vol. 23, no. 3, pp. 398-401, 2019. https://doi.org/10.1109/LCOMM.2019.2891623.
Y. Yehezkeally, and M. Schwartz, “Limited-Magnitude Error-Correcting Gray Codes for Rank Modulation,” IEEE Transactions on Information Theory, vol. 63, no. 9, pp. 5774-5792, 2017. https://doi.org/10.1109/TIT.2017.2719710.
F. Artuğer, and F. Özkaynak, “Fractal Image Compression Method for Lossy Data Compression,” International Conference on Artificial Intelligence and Data Processing (IDAP), 2018, pp. 1-6. https://doi.org/10.1109/IDAP. 2018.8620735.
J. Lin, D. Liu, H. Li, and F. Wu, “M-LVC: Multiple Frames Prediction for Learned Video Compression,” IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2020, pp. 3543-3551. DOI: 10.1109/CVPR42600. 2020.00360.
W. Dong, and J. Wang, “JPEG Compression Forensics against Resizing,” IEEE Trustcom/ BigDataSE/IвSPA, Tianjin, China, 2016, pp. 1001-1007. https://doi.org/10.1109/TrustCom.2016.0168.
W. Xiao, N. Wan, A. Hong, and X. Chen, “A Fast JPEG Image Compression Algorithm Based on DCT,” IEEE International Conference on Smart Cloud (SmartCloud), 2020, pp. 106-110. https://doi.org/10.1109/ SmartCloud49737. 2020.00028.
A. Phatak, “A Non-format Compliant Scalable RSA-based JPEG Encryption Algorithm,” International Journal of Image. Graphics and Signal Processing, vol. 8, no. 6, pp 64-71, 2016. https://doi.org/10.5815/ijigsp. 2016.06.08.
H. Wu, X. Sun, J. Yang, W. Zeng, and F. Wu, “Lossless Compression of JPEG Coded Photo Collections,” IEEE Transactions on Image Processing, vol. 25, no. 6, pp. 2684-2696, 2016. https://doi.org/10.1109/ TIP.2016.2551366.
M. Akbari, J. Liang, J. Han, and C. Tu, “Learned Variable-Rate Image Compression With Residual Divisive Normalization,” IEEE International Conference on Multimedia and Expo (ICME), 2020, pp. 1-6. https://doi.org/10.1109/ICME46284.2020.9102877.
M. A. Alam, “Faster Image Compression Technique Based on LZW Algorithm Using GPU Parallel Processing,” Joint 7th International Conference on Informatics, Electronics & Vision (ICIEV) and 2nd International Conference on Imaging, Vision & Pattern Recognition (icIVPR), 2018, pp. 272-275, https://doi.org/10.1109/ICIEV.2018.8640956.
S. Khmelevskiy, I. Tupitsya, Q. A. Mahdi, О. Musienko, M. Parkhomenko, and Y. Borovensky, “Development of the external restructuring method to increase the efficiency of information resource data encodin,” Information Processing Systems, 3(166), pp. 52-61, 2021. https://doi.org/10.30748/soi.2021.166.06.
V. Barannik, S. Sidchenko, I. Tupitsya, and S. Stasev, “The application for internal restructuring the data in the entropy coding process to enhance the information resource security,” IEEE East-West Design and Test Symposium (EWDTS), 2016, pp. 1-4. https://doi.org/10.1109/EWDTS.2016.7807749.
Barannik, V., Tupitsya, I., Barannik, V., Shulgin, S., Musienko, A., Kochan, R., Veselska, O. The Application of the Internal Restructuring Method of the Information Resource Data According to the Sign of the Number of Series of Units to Improve the Statistical Coding Efficiency. 10th IEEE International Conference on Intelligent Data Acquisition and Advanced Computing Systems: Technology and Applications (IDAACS), 2019, pp.65-69. DOI: 10.1109/IDAACS.2019.8924460.
V. Barannik, I. Tupitsya, O. Kovalenko, Y. Sidchenko, V. Yroshenko, and O. Stepanko, “The analysis of the internal restructuring method efficiency used for a more compact representation of the encoded data,” Advanced Trends in Information Theory (ATIT’2020): proceedings of the Intern. Conf., 2020, pp. 89-92. https://doi.org/10.1109/ ATIT49449.2019.9030451.
V. Barannik, I. Tupitsya, O. Dodukh, V. Barannik, and M. Parkhomenko, “The Method of Clustering Information Resource Data on the Sign of the Number of Series of Units as a Tool to improve the Statistical Coding Efficiency. IEEE 15th International Conference on the Experience of Designing and Application of CAD Systems (CADSM), 2019, pp. 32-35. https://doi.org/10.1109/CADSM.2019.8779243.
O. Yudin, V. Artemov, A. Krasnorutsky, V. Barannik, I. Tupitsya and G. Pris, “Creating a mathematical model for estimating the impact of errors in the process of reconstruction of non-uniform code structures on the quality of recoverable video images,” Advanced Trends in Information Theory (ATIT’2021): proceedings of the Intern. Conf., pp. 38-41. https://doi.org/10.1109/ATIT54053.2021.9678887.
V. Barannik, I. Tupitsya, I. Gurzhii, V. Barannik, S. Sidchenko, O. Kulitsa, “ Two-Hierarchical Scheme of Statistical Coding of Information Resource Data with Quantitative Clustering,” IEEE International Conference on Advanced Trends in Information Theory (ATIT), 2019, pp. 89-92. https://doi.org/10.1109/ATIT49449.2019.9030451. Accessed: http://sipi.usc.edu/database/database.php?volume=misc&image=12#top
Downloads
-
PDF (Українська)
Downloads: 125
Published
How to Cite
Issue
Section
License
This work is licensed under a Creative Commons Attribution 4.0 International License.
Authors who publish with this journal agree to the following terms:
- Authors retain copyright and grant the journal right of first publication.
- Authors are able to enter into separate, additional contractual arrangements for the non-exclusive distribution of the journal's published version of the work (e.g., post it to an institutional repository or publish it in a book), with an acknowledgment of its initial publication in this journal.
- Authors are permitted and encouraged to post their work online (e.g., in institutional repositories or on their website) prior to and during the submission process, as it can lead to productive exchanges, as well as earlier and greater citation of published work (See The Effect of Open Access).