PRECISION, SPEED AND COMPLEXITY OF DEVICES FOR IMAGE CODING BY CONTROL POINTS
Read the full article
For citation: Almahrouq M.M., Bobrovsky A.I., Eid M.M., Sokolov Y.M., Salem A., Fahmi Sh.S. Precision, speed and complexity of devices for image coding by control points. Scientific and Technical Journal of Information Technologies, Mechanics and Optics, 2016, vol. 16, no. 4, pp. 678–688. doi: 10.17586/2226-1494-2016-16-4-678-688
Subject of Research. We have proposed a method, algorithms and devices for image coding and decoding by control points without switching to the spectral domain of the signal in order to determine information quality indicators of image processing systems. Formalized consideration of the coding devices complexity stimulated by the creation and development of VLSI and systems on a chip requires revision of "efficient" encoding concept, since not only the transmission accuracy (error) and the transmission speed, but also complexity enters the circle of considered variables taken into account at the source encoding. Method. The proposed approach is based on the application of: firstly, spatial-recursive method of partitioning the images into polygons of various shape and size when searching the reference feature points of the images; secondly, regular and irregular triangulation algorithms at the recovery phase of the resulting image. Main Results. As a result of the simulation of image coding and decoding algorithms based on control points we have obtained three-dimensional graphic comparison of data quality indicators: error, transmission speed and complexity of devices for coding and decoding by control points with the other known image coding algorithms. Practical Relevance. The proposed algorithms for images partition and search of control points along with low significance of difficulty give the possibility: firstly, to reduce the transmission rate by 1.5-2 times as compared to standard algorithms for spectral conversion; secondly, to have a compact representation of video information in the form of one-dimensional dynamic array of control points with the possibility of semantic analysis and the transition from standard formats for video data transmission and storage to specific forms, focused on solving specific problems.
1. Tsytsulin A.K., Adamov D.Yu., Mantsvetov A.A., Zubakin I.A. Solid-State Cameras: the Accumulation of Information Quality. St. Petersburg, SPBSETU "LETI" Publ., 2014, 271 p.
2. Berezin V.V., Fakhmi Sh.S., Tsytsulin A.K. Initial design stage of video systems on a chip. Journal of Optical Technology, 2012, vol. 79, no. 11, pp. 733–737.
3. Aleksandrov V.V., Kuleshov S.V., Tsvetkov O.V. Digital Infocommunication Technology. Transmission, Storage and Semantic Analysis of Text, Audio, Video. St. Petersburg, Nauka Publ., 2008, 244 p. (In Russian)
4. Sakthi Bharathi D., Manimegalai A. 3D digital reconstruction of brain tumor from MRI scans using Delaunay triangulation and patches. ARPN Journal of Engineering and Applied Sciences, 2015, vol. 10, no. 20, pp. 9227–9232.
5. Fakhmi Sh.S. Encoding and decoding of video. Voprosy Radioelektroniki. Seriya: Tekhnika Televideniya, 2007, no. 2, pp. 43–51.
6. Fakhmi Sh.S., Zubakin I.A. Non-stationary images classification and development of source coding algorithms estimation method. Scientific and Technical Journal of Information Technologies, Mechanics and Optics, 2010, no. 2, pp. 54–59.
7. Zhang N.A Novel parallel prefix sum algorithm and its implementation on multi-core platforms. Proc. 2nd Int. Conf. on Computer Engineering and Technology. Chengdu, China, 2010, vol. 2, pp. V266–V270. doi: 10.1109/ICCET.2010.5485315
8. Zhang N. Working towards efficient parallel computing of integral images on multi-core processors. Proc. 2nd Int. Conf. on Computer Engineering and Technology. Chengdu, China, 2010, vol. 2, pp. V230–V234. doi: 10.1109/ICCET.2010.5485338
9. Blelloch G.E. Prefix sums and their applications. In Synthesis of Parallel Algorithms. Ed. J.H. Reif. San Francisco, Morgan Kaufmann Publ., 1990, pp. 35–60.
10. Baburin V.A., Kostikova E.V., Fakhmi Sh.S. Development of the videoinformation system architecture of coding and decoding of the basis of the space-recursive method. Zhurnal Universiteta Vodnykh Kommunikatsii, 2012, no. 1, pp. 89–97. (In Russian)
11. Misra J. Powerlist: a structure for parallel recursion. ACM Transactions on Programming Languages and Systems, 1994, vol. 16, no. 6, pp. 1737–1767. doi: 10.1145/197320.197356
12. Smolov V.B., Barashenkov V.V., Baikov V.D. Spetsializirovannye TsVM [Specialized Digital Computer]. Moscow, Vyshaya Shkola, 1981, 279 p.
13. Zhao J., Zhu S., Huang X. Real-time traffic sign detection using surf features on fpga. IEEE High Performance Extreme Computing Conference, HPEC. Waltham, USA, 2013, art. 6670350. doi: 10.1109/HPEC.2013.6670350
14. Wang W., Huang X. An fpga co-processor for adaptive lane departure warning system. IEEE Int. Symposium on Circuits and Systems, ISCAS. Beijing, China, 2013, pp. 1380–1383. doi: 10.1109/ISCAS.2013.6572112
15. Berezin V.V., Zolotukho R.N., Fakhmi Sh.S. Debugging of hardware and software of reconfigurable systems-on-chip. Komponenty i Tekhnologii, 2003, no. 33, pp. 118–122. (In Russian)
16. Voevodin V.V., Voevodin Vl.V. Parallel Computing. St. Petersburg, BKhV-Peterburg, 2002, 608 p. (In Russian)
17. Moiseev N.N. Mathematical Problems of System Analysis. Moscow, Nauka Publ., 1981, 488 p. (In Russian)
18. Shannon C.E. Works on Information Theory and Cybernetics. Moscow, 1963, 832 p.
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License