Development of an advanced method for compressing the dynamic component of a differential-represented frame

DOI: 10.31673/2412-4338.2020.036783

Abstract

For a typical low complexity video sequence, the weight of each P-frame in the stream is approximately three times smaller than the I-frame weight. However, taking into account the number of P-frames in the group, they make the main contribution to the total video data amount. Therefore, the possibility of upgrading coding methods for P-frames is considered on preliminary blocks' type identification with the subsequent formation of block code structures. As the correlation coefficient between adjacent frames increases, the compression ratio of the differential-represented frame's binary mask increases. The compression ratio of the differential-represented frame's binary mask varies from 3 to 21 depending on the correlation coefficient between adjacent frames. Estimation of the bit representation's information content of the differential-represented frame's binary mask on the basis of accounting for the nonequilibrium of the bases of the lengths of the binary series does not require an increase in the complexity of the software-hardware implementation. Due to the double-alphabetic power code, the differential-represented frame's binary mask is relative to the single-alphabet code will decrease by 17%. The most preferable method for constructing the compact representation technology of the binary masks of frames represented in a differential form is the approach. This is based on the identification and description of the lengths of one-dimensional binary series. A binary series is a consecutive binary elements sequence with the same value. In this case, sequences of identical binary elements are replaced by their lengths. And since the elements of the binary masks of the frames represented in the differential form take only two possible values 0 or 1, it is suggested to form the lengths of the binary series without indicating their level.

Keywords: image, redundancy, coding, quantization, matrix, binary mask, data.

References
1. Larin V. Development of an advanced method of video information resource compression in navigation and traffic control systems. / S. Yevseiev, Ahmed Abdalla, S. Osiievskyi, V. Larin, M. Lytvynenko. // EUREKA: Physics and Engineering 5.(2020), P. 31-42. https://doi.org/ 10.21303/2461-4262.2020.001405.
2. Ruban I. Method of neural network recognition of ground-based air objects. / I.Ruban, K.Smelyakov, N.Bolohova // Proceedings of 2018 IEEE 9th International Conference on Dependable Systems, Services and Technologies, DESSERT 2018, pp. 589-592.
3. Sumtsov D. Development of a method for the experimental estimation of multimedia data flow rate in a computer network. / D.Sumtsov, S.Osiievskyi, V. Lebediev // Eastern-European Journal of Enterprise Technologies. Volume 2, Issue 2-92, 2018, Pages 56-64.
4. Mistry D. Network traffic measurement and analysis. / D.Mistry, P.Modi, K.Deokule, A.Patel, H.Patki, O.Abuzaghleh // 2016 IEEE Long Island Systems, Applications and Technology Conference (LISAT).
5. Tkachov V. The Problem of Big Data Transmission in the Mobile "Multi-Copter – Sensor Network" System / V.Tkachov, V.Tokariev, V.Radchenko, V.Lebediev // Control, Navigation and Communication Systems. 2017. Issue 2. P. 154–157.
6. Pavlenko M. Hybrid model of knowledge for situation recognition in airspace. / M. Pavlenko, A.Timochko, N.Korolyuk, M.Gusak //Automatic Control and Computer SciencesVolume 48, Issue 5, 2014, Pages 257-263.
7. Gonzales R.C. Digital image processing / R.C. Gonzales, R.E. Woods. – Prentice Inc. Upper Saddle River, New Jersey, 2002. – 779 p.
8. Prett U. Tsifrovaya obrabotka izobrazheniy: v 2 t.; per. s angl. Moskva: Mir, 1985. 736 s.
9. Miano Dzh. Formaty i algoritmy szhatiya izobrazheniy v deystvii: ucheb. posobiye; per. s angl. Moskva: Triumf, 2003. 336 s.
10. Zakharchenko M. V. Systemy peredavannya danykh / M.V. Zakharchenko, M.M. Hadzhyyev, V.YE. Basov, O.M. Martynova ta in. // – T.1: Zavadostiyke koduvannya: pidruchnyk [dlya stud. vyshch. tekhn. navch. zakl.]. / – Odesa «Feniks», 2009. – 406 s.
11. Korchynskii V.V. The increase of transmission protection based on multiplexing of timer signal constructions / Korchynskii V.V., Kildishev V.I., Osadchuk E.A.// Zb. Naukovi pratsi ONAZ, Odesa – ONAZ – 2018 – №1, – P. 93-97.
12. Zakharchenko N.V. Uvelychenye ynformatsyonnoy emkosty naykvystovoho élementa pry peredache 2-kh symvolʹnykh ansambley taymernymy syhnalamy / N.V. Zakharchenko, V.V. Hordeychuk, E.A. Sevasteev // “Suchasni informatsiyni tekhnolohiyi u sferi bezpeky ta oborony”. – K.: NUOU im. I. Chernyakhovsʹkoho - № 2(26) 2016 – S. 21 26.
13. Pavlenko, M., Kolmykov, M., Tymochko, O., Khmelevskiy, S., Larin, V. (2020). Conceptual Basis of Cascading Differential Masking Technology. 2020 IEEE 11th International Conference on Dependable Systems, Services and Technologies (DESSERT). doi: https://doi.org/10.1109/dessert50317.2020.9125024.
14. Tyurin, V., Martyniuk, O., Mirnenko, V., Open’ko, P., Korenivska, I. (2019). General Approach to Counter Unmanned Aerial Vehicles. 2019 IEEE 5th International Conference Actual Problems of Unmanned Aerial Vehicles Developments (APUAVD). doi: https://doi.org/10.1109/apuavd47061.2019.8943859.