Adaptive algorithm of image encoding and decoding in transport video surveillance systems

Cover Page

Cite item

Full Text

Abstract

It is well known that the image processing standards (JPEG) and video processing standards (MPEG), based on the use of spectral cosine transformation, do not meet the growing requirements for high quality video transmission obtained from surveillance cameras in transport. The reason for this is the large amount of visual data and the fact that the optimization of the video codec should be done (at least) on three parameters: bit rate, distortion and computational complexity. Therefore, the task arises to develop new image compression algorithms that are best aligned with existing standards. One of the algorithms for improving the process of coding and decoding is considered in this paper. It differs from the existing ones in that the sizes of blocks subjected to cosine transformation can be arbitrary and are selected taking into account the specifics of transport subjects.

About the authors

Sh. S. Fahmi

St. Petersburg state electrotechnical University. V. I. Ulyanov (Lenin) “LETI”; Solomenko Institute of Transport Problems of the Russian academy of sciences

Author for correspondence.
Email: Shakeebf@mail.ru

Professor

Russian Federation, St. Petersburg; St. Petersburg

N. G. Ryjov

St. Petersburg state electrotechnical University. V. I. Ulyanov (Lenin) “LETI”

Email: ngryzhov@etu.ru

Assistant Professor

Russian Federation, St. Petersburg

Y. A. Hasan

St. Petersburg state electrotechnical University. V. I. Ulyanov (Lenin) “LETI”

Email: ngryzhov@etu.ru

post-graduate student

Russian Federation, St. Petersburg

E. S. Kalinina

Saint-Petersburg University of state fire service of Emercom of Russia

Email: elenakali21@yandex.ru

Assistant Professor

Russian Federation, St. Petersburg

References

  1. Rusul A., Hussein D., Sohani L., Saeed A. Applications of Artificial Intelligence in Transport: An Overview. Sustainability 2019, 11, 189; doi: 10.3390/su11010189.
  2. Krol A. The Application of the Artificial Intelligence Methods for Planning of the Development of the Transportation Network, Transp. Res. Procedia, 2016, vol. 14, pp. 4532—4541.
  3. Fahmi S. Sh., Sycolin A. K. et al. the dominant information Principle and its application to videoinformation, Questions of radio electronics, ser. The technique of television, 2018, iss. 4, pp. 3—16 (in Russian).
  4. Hossen M. K., Tuli S. H. A surveillance system based on motion detection and motion estimation using optical flow. In Informatics, Electronics and Vision (ICIEV), 2016 5th International Conference on IEEE, 2016, pp. 646—651.
  5. Huang D.-Y., Chen C.-H., Hu W.-C., Yi S.-C., Lin Y.-F. et al. Feature-based vehicle flow analysis and measurement for a real-time traffic surveillance system, Journal of Information Hiding and Multimedia Signal Processing, 2012, vol. 3, pp. 279—294.
  6. Porwal S., Chaudhary Y., Joshi J., Jain M. International Journal of Engineering Science and Innovative Technology (IJESIT), 2013, vol. 2, no. 2, pp. 142—147.
  7. Vijayvargiya S. G., Pandey R. P. International Journal of Computer Science and Information Security, 2013, vol. 11, no. 10.
  8. Fahmi S. Sh. Journal of University of water communications, 2013, iss. II (XVIII), pp. 79—88 (in Russian).
  9. Richardson I. Video coding. H. 264 and MPEG — 4-new generation standards, Moscow, Technosphere, 2005, 368 p. (in Russian).
  10. Fahmi S. Sh., Sycolin A. K., Kolesnikov E. I., Ochkur S. V. Information Technology, 2011, no. 4, pp. 71—77 (in Russian).
  11. Moiseev N. N. Mathematical problems of system analysis, Moscow, Science, 1981, 488 p. (in Russian).
  12. Castrillon-Candas J. E., Amaratunga K. Fast Estimation of Continuous Karhunen-Loeve Eigenfunctions Using Wavelets, IEEE Transactions on Signal Processing, 2002.
  13. Mulla A., Baviskar J., Baviskar A., Warty C. Image compression scheme based on zig-zag 3D-DCT and LDPC coding, Proc. International Conference on Advances in Computing, Communications and Informatics, 2014, pp. 2380—2384.
  14. Thirumoorthi C., Karthikeyan T. A study on discrete wavelet transform compression algorithm for medical images, Biomedical Research, 2017, vol. 28 (4), pp. 1574—1580.
  15. Fahmi S. Sh. Journal of University of Water Communications, 2013, iss. II (XVIII), pp. 79—88 (in Russian).
  16. Abdelhalim M. B., Salama A. E. Implementation of 3D DCT based Video Encoder Decoder System”, IEEE International Symposium on signals, Circuits and Systems, 2003, vol. 2, pp. 389—392.
  17. Saponara S., Fanucci L., Terreni P. Low- Power VLSI architectures for 3D Discrete Cosine Transform”, IEEE 48th Midwest Symposium on Circuits and Systems, 2013, vol. 3, pp. 1567—1570.
  18. Fan Y., Shan-Ann C., Kuo-Gi W., Jun-Lin Y. 3D-DCT Chip Design for 3D Multi-view Video Compression, Appl. Math. Inf. Sci., 2012, vol. 6, no. 2S, pp. 567S—572S.
  19. Fahmi S. Sh., Zubakin I. A. Classification of non-stationary images, and developing methods of evaluation algorithms of source coding, Nauch. Technical Bulletin of SPBU ITMO, 2010, no. 2 (66), pp. 54—59 (in Russian).
  20. Btoush M. H., Dawahdeh Z. E. A Complexity Analysis and Entropy for Different Data Compression Algorithms on Text Files, Journal of Computer and Communications, 2018, Jan. 12, рр. 301—315, doi: 10.4236/jcc.2018.61029

Supplementary files

Supplementary Files
Action
1. JATS XML

Copyright (c) 2019 Informacionnye Tehnologii



СМИ зарегистрировано Федеральной службой по надзору в сфере связи, информационных технологий и массовых коммуникаций (Роскомнадзор).
Регистрационный номер и дата принятия решения о регистрации СМИ: серия ПИ № 77 - 15565 от 02 июня 2003 г.