Preview

Advanced Engineering Research (Rostov-on-Don)

Advanced search

Arithmetic coder optimization for compressing images obtained through remote probing of water bodies

https://doi.org/10.23947/1992-5980-2019-19-1-86-92

Abstract

Introduction. The fast program algorithm of arithmetic coding proposed in the paper is for the compression of digital images. It is shown how the complexity of the arithmetic coder algorithm depends on the complexity measures (the input size is not considered). In the course of work, the most computationally complex parts of the arithmetic coder algorithm are determined. Performance optimization of their software implementation is carried out. Codecs with the new algorithm compress photo and video records obtained through the remote probing of water bodies without frame-to-frame difference.

Materials and Methods. In the presented paper, a selection of satellite images of the Azov Sea area was used. At this, the software algorithm of the arithmetic coder was optimized; a theoretical study was conducted; and a computational experiment was performed.

Research Results. The performance of the software implementation of the arithmetic coder is increased by the example of the VP9 video codec. Numerous launches of reference and modified codecs were made to measure the runtime. Comparison of the average time of their execution showed that the modified codec performance is 5.21% higher. The overall performance improvement for arithmetic decoding was 7.33%.

Discussion and Conclusions. Increase in the speed of the latest digital photo and video image compression algorithms allows them to be used on mobile computing platforms, also as part of the onboard electronics of unmanned aerial vehicles. The theoretical results of this work extend tools of the average-case complexity analysis of the algorithm. They can be used in case where the number of algorithm steps depends not only on the input size, but also on non-measurable criteria (for example, on the common RAM access scheme from parallel processors).

About the Author

R. V. Arzumanyan
Institute of Computer Technology and Information Security, Southern Federal University
Russian Federation

Arzumanyan, Roman V., postgraduate of the Intelligent Multiprocessor Systems Department,

22, ul. Chekhova, Taganrog, Rostov Region, 347922



References

1. WebP Compression Study [Электронный ресурс] / Google Developers. — Режим доступа: https://developers. google.com/ speed/webp/docs/webp_study (дата обращения 01.02.19).

2. Nguyenand, T. Objective Performance Evaluation of the HEVC Main Still Picture Profile / T. Nguyenand,

3. D. Marpe // IEEE Transactions on Circuits and Systems for Video Technology. — 2015. — Vol. 25, № 5. — P. 790–797.

4. Блейхут, Р. Быстрые алгоритмы цифровой обработки сигналов / Р. Блейхут. — Москва : Мир, 1989. — 448 с.

5. Wallace, G. K. The JPEG still picture compression standard / G. K. Wallace // IEEE Transactions on Consumer Electronics. — 1992. — Vol. 38, № 1. — P. XVIII–XXXIV.

6. Дворкович, А. В. Цифровые видеоинформационные системы (теория и практика) / А. В. Дворкович, В. П. Дворкович. — Москва : Техносфера, 2012. — 1009 c.

7. Asaduzzaman, A. Performance-power analysis of H.265/HEVC and H.264/AVC running on multicore cache systems [Электронный ресурс] / A. Asaduzzaman, V. R. Suryanarayana, M. Rahman // Intelligent Signal Processing and Communications Systems. — Режим доступа: https://ieeexplore.ieee.org/document/6704542 (дата обращения 01.02.19).

8. Sedgewick, R. Algorithms. Fourth edition / R. Sedgewick, K. Wayne. — Upper Saddle River : AddisonWesley, 2016. — 960 p.

9. Introduction to Algorithms / T. H. Cormen [et al.]. — 3rd edition. — Cambridge ; London : The MIT Press, 2009. — 1296 p.

10. Welch, W. J. Algorithmic complexity: three NP — hard problems in computation all statistics / W. J. Welch // Journal of Statistical Computation and Simulation. — 1982. — Vol. 15, № 1. — P. 17–25.

11. High efficiency video coding [Электронный ресурс] / Fraunhofer Heinrich Hertz Institute. — Режим доступа: http://hevc.info/ (дата обращения: 01.02.19).

12. Sze, V. Parallelization of CABAC transform coefficient coding for HEVC [Электронный ресурс] / V. Sze, M. Budagavi // Semantic Scholar / Allen Institute for Artificial Intelligence Logo. — Режим доступа: https://www.semanticscholar.org/paper/Parallelization-of-CABAC-transform-coefficient-for-SzeBudagavi/0653a22ff7b82bdd0130cea8b597a7024ab46882 (дата обращения: 01.02.19).

13. Salomon. D. Handbook of data compression / D. Salomon, G. Motta. — London ; Dordrecht ; Heidelberg ; New York : Springer-Verlag, 2010. — 1360 p.

14. Anderson, S. E. Bit Twiddling Hacks [Электронный ресурс] / S. E. Anderson. — Режим доступа: https://graphics.stanford.edu/~seander/bithacks.html (дата обращения 01.02.19).

15. Гервич, Л. Р. Программирование экзафлопсных систем / Л. Р. Гервич, Б. Я. Штейнберг, М. В. Юрушкин // Открытые системы. СУБД. — 2013. — Т. 8.— C. 26–29.

16. Уоррен-мл., Г. С. Алгоритмические трюки для программистов / Г.-С. Уоррен- мл. — 2-е изд. — Москва : Вильямс, 2013. — 512 с.


Review

For citations:


Arzumanyan R.V. Arithmetic coder optimization for compressing images obtained through remote probing of water bodies. Vestnik of Don State Technical University. 2019;19(1):86-92. https://doi.org/10.23947/1992-5980-2019-19-1-86-92

Views: 647


Creative Commons License
This work is licensed under a Creative Commons Attribution 4.0 License.


ISSN 2687-1653 (Online)