[
1. Vajapeyam, S. (2014). Understanding Shannon’s Entropy Metric for Information. eprint arXiv:1405.2061
]Search in Google Scholar
[
2. Kolchinsky, A., & Wolpert, D.H. (2018). Semantic Information, Autonomous Agency and Non-equilibrium Statistical Physics. Interface Focus, 8, 20180041. http://dx.doi.org/10.1098/rsfs.2018.0041.10.1098/rsfs.2018.0041622781130443338
]Search in Google Scholar
[
3. Carlson, Bruce A. (1986). Communication Systems. An Introduction to Signals and Noise in Electric Communication. New York: McGraw-Hill Book Company.
]Search in Google Scholar
[
4. MathWorks. (n.d.). Entropy. Available at https://se.mathworks.com/help/images/ref/entropy.html
]Search in Google Scholar
[
5. MathWorks. (n.d.). Signal. Available at https://www.mathworks.com/helps/signal/ref/pentropy.html#mw_da069ed5-a376-4d11-84d3-ca16946eab9
]Search in Google Scholar
[
6. Seeling, P. (2010). Scene Change Detection for Uncompressed Video. Technological Developments in Education and Automation. doi:https://doi-org.resursi.rtu.lv/10.1007/978-90-481-3656-8_310.1007/978-90-481-3656-8_3
]Search in Google Scholar
[
7. Xuguang Zhang, X. S. (2019). Crowd Panic State Detection Using Entropy of the Distribution. Physica A: Statistical Mechanics and its Applications, 525 (7), 935–945. doi: 10.1016/j.physa.2019.04.03310.1016/j.physa.2019.04.033
]Search in Google Scholar
[
8. Luo, Z. B. (2016). Human Abnormal Behavior Detection Based on RGBD Video’s Skeleton Information Entropy. Lecture Notes in Electrical Engineering. doi:https://doi-org.resursi.rtu.lv/10.1007/978-3-662-49831-6_74
]Search in Google Scholar
[
9. Ferreira, T.A.F.R (2014). Entropy Based Dynamic Ad Placement Algorithms in Video Advertising. PhD Thesis, University of Beira Interior, Covilha, Portugal.
]Search in Google Scholar
[
10. Nowosad, J. & Stepinski, T.F. (2019). Information Theory as a Consistent Framework for Quantification and Classification of Landscape Patterns. Landscape Ecol., 34, 2091–2101.10.1007/s10980-019-00830-x
]Search in Google Scholar
[
11. Černekova, Z., Nikou, C., & Pitas, I. (2002). Entropy Metrics used for Video Summarization. Proceedings of the Spring Conference on Computer Graphics, Budmarice, Slovakia, 73–82.10.1145/584458.584471
]Search in Google Scholar
[
12. Liu, J., Wang, Sh., Ma, Wei-Chiu, Shah, M., Hu, R., Dhawan, P. & Urtasan, R. (2020). Conditional Entropy Coding for Efficient Video Compression. Image and Video Processing. ECCV, LNC3, 12362, 453–468. https://doi.org/10.1007/978-3-030-58520-4_27.10.1007/978-3-030-58520-4_27
]Search in Google Scholar
[
13. Sun, J., Xu, Zh., Liu, J., & Yeo, Y. (2011). An Objective Visual Security Assesment for Cipher-Images Based on Local Entropy. Multimed. Tools Appl., 53, 75–95.10.1007/s11042-010-0491-5
]Search in Google Scholar
[
14. Fei, M., Jiang, W., & Mao, W. (2017). Memorable and Rich Video Summarization. J. Vis. Comun. Image Represent. 42 (C), 207–217.10.1016/j.jvcir.2016.12.001
]Search in Google Scholar
[
15. Alksne, L. (2016). How to produce video lectures to engage students and deliver the maximum amount of information. Proceedings of the International Scientific Conference “Society. Integration. Education”, 503–516. doi: http://dx.doi.org/10.17770/sie2016vol2.142410.17770/sie2016vol2.1424
]Search in Google Scholar
[
16. Wieling, M., & Adriaan Hofman, W.H. A. (2010). The Impact of Online Video Lecture Recordings and Automated Feedback on Student Performance. Computers & Education, 54 (4), :992-–998. doi: 10.1016/j.compedu.2009.10.00210.1016/j.compedu.2009.10.002
]Search in Google Scholar
[
17. Pauliks, R. (2016). Quality Studies of Video Transmission Services in Packet Networks. Summary of PhD Thesis. Riga: RTU Publishing House. (in Latvian).
]Search in Google Scholar
[
18. Jehonovičs, A. (1984). Handbook of Physics and Technics. Rīga: Zvaigzne. (in Latvian).
]Search in Google Scholar
[
19. Smith, J. (2007). Why Can an Opera Singer be Heard over the Much Louder Orchestra? Available at https://www.scientificamerican.com/article/expert-opera-singer/
]Search in Google Scholar
[
20. Horowitz, S. (2013). The Universal Sense: How Hearing Shapes the Mind. USA: Bloomsbury.
]Search in Google Scholar
[
21. Nave, C.R. (2016). Hyperphysics. Atlanta: Georgia State University.
]Search in Google Scholar
[
22. Errede, S. (2002–2017). The Human Ear-Hearing, Sound Intensity and Loudness Levels. UIUC Physics 406 Acoustical Physics of Music,1–33.
]Search in Google Scholar
[
23. Benjamin, A.T., & Quinn, J.J. (2003). The Proofs that Really Count. The Art of Combinatorial Proof. The Dolciani Mathematical Expositions 27, The Mathematical Association of America, ISBN 978-0-88385-333-7.10.5948/9781614442080
]Search in Google Scholar
[
24. Gaisler, W.S, & Banks, M.S. (2010). Visual perfomance. In M. Bass, Handbook of Optics, vol. III. Vision and Vision Optics (pp. 2.1–2.51). New York: McGraw Hill Companies, Inc.
]Search in Google Scholar
[
25. Luizov, A.V. (1989). Colour and Light. Leningrad: Energoatomizdat. (in Russian).
]Search in Google Scholar
[
26. Richards, A. (2011). Alien Vision. Exploring the Electromagnetic Spectrum with Imaging Technology. (2nd ed.). Bellingham, Washington: SPIE Press.10.1117/3.883085
]Search in Google Scholar
[
27. Werner, J.S., Schefrin, B.E., & Bradley, A. (2010). Optics and Vision of the Aging Eye. In M.Bass, Handbook of Optics, vol. III. Vision and Vision Optics (pp.14.1–14.38). New York: McGraw Hill Companies, Inc.
]Search in Google Scholar
[
28. Temnikov, F.E., Afonin, V.A., & Dmitriev, V.I. (1971). Theoretical Foundations of Information Technics. Moscow: Energija. (in Russian).
]Search in Google Scholar
[
29. Markowsky, G. (2017). Information Theory. Encyclopaedia Britannica. Available at https://www.britannica.com/science/information-theory
]Search in Google Scholar
