INTERVAL QUALITY INDICATORS OF THE DYNAMIC RANGE COMPRESSION OF INFRARED IMAGES ON THE BASIS OF A TONE MAPPING MATRIX
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Abstract
The article proposes a dynamic range compression model for infrared (IR) images based on a tone mapping matrix, the elements of which relate the brightness levels of the original image with a wide dynamic range and the brightness levels of a non-linearly transformed image with a narrow dynamic range, and also indicate, depending on the variant of formation of this matrices on: a) loss of discrimination between adjacent pixels due to compression of the dynamic range; b) the level of non-linear compression distortions; c) ambiguity of tone mapping. Based on this model, interval indicators of the quality of compression of the dynamic range of infrared images are proposed, which allow estimating the potential distinguishing power, the real loss of discrimination between adjacent pixels after transformation, the magnitude of nonlinear compression distortions, the uniformity of the use of the dynamic range, and the ambiguity of tone mapping for the selected interval of the dynamic range. The proposed indicators improve the accuracy of assessing the quality of compression of the dynamic range of IR images by expanding the system of known indicators that evaluate the contrast, entropy, statistical naturalness of the converted images, and the structural accuracy of tone mapping.
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References
Nithyananda, C. R., Ramachandra, A. C., & Preethi. (2016). Review on Histogram Equalization based Image Enhancement Techniques. In International Conference on Electrical, Electronics, and Optimization Techniques (ICEEOT), Chennai (2512–2517). DOI: 10.1109/ICEEOT.2016.7755145.
Kim, T. K., Paik, J. K., & Kang, B. S. (1998). Contrast enhancement system using spatially adaptive histogram equalization with temporal filtering. IEEE Trans. Consum. Electron, 44(1), 82–87. DOI: 10.1109/30.663733.
Kim, J.-Y., Kim, L.-S., & Hwang, S.-H. (2001). An advanced contrast enhancement using partially overlapped subblock histogram equalization. IEEE Transactions on Circuits and Systems for Video Technology, 11(4), 475–484. DOI: 10.1109/76.915354.
Reza, A. M. (2004). Realization of the contrast limited adaptive histogram equalization (CLAHE) for real-time image enhancement. Journal of VLSI Signal Process.-Syst. Signal Image Video Technol., 38(1), 35–44. DOI: 10.1023/B:VLSI.0000028532.53893.82.
Huang, S.-C., & Yeh, C.-H. (2013). Image contrast enhancement for preserving mean brightness without losing image features. Engineering Applications of Artificial Intelligence, 26(5), 1487–1492. DOI: 10.1016/j.engappai.2012.11.011.
Al-Sammaraie, M. F. (2015). Contrast enhancement of roads images with foggy scenes based on histogram equalization. In 10th International Conference on Computer Science and Education (ICCSE) (95–101). Cambridge. DOI: 10.1109/ICCSE.2015.7250224.
Rudikov, S. I., Tsvetkov, V. Yu., & Shkadarevich, A. P. (2021). Umen'shenie dinamicheskogo diapazona infrakrasnykh izobrazhenii na osnove adaptivnogo vyravnivaniya, rastyazheniya i szhatiya gistogrammy [Dynamic range reduction of infrared images based on adaptive equalization, stretch and compression of histogram]. Vestsі Natsyyanal'nai akademіі navuk Belarusі. Seryya fіzіka-tekhnіchnykh navuk. [Proceedings of the National Academy of Sciences of Belarus. Physical-technical serie], 66(4), 470–482. DOI: 10.29235/1561-8358-2021-66-4-470-482. (In Russ., abstr. in Engl.).
Golub, Yu. I., Starovoitov, F. V., & Starovoitov, V. V. (2020). Vliyanie umen'sheniya razmerov izobrazheniya na vychislenie otsenki ego kachestva [Impact of image size reducing for image quality assesment]. Sistemnyi analiz i prikladnaya informatika [System analysis and applied information science], (2), 35–45. (In Russ., abstr. in Engl.).
Mante, V., Frazor, R. A., Bonin, V., Geisler, W. S., & Carandini, M. (2005). Independence of luminance and contrast in natural scenes and in the early visual system. Nat Neurosci, (8), 1690–1697. DOI: 10.1038/nn1556.
Wang, Z., Simoncelli, E. P., & Bovik, A. C. (2003). Multiscale structural similarity for image quality assessment. 37th Asilomar Conference on Signals, Systems & Computers. Pacific Grove, CA, USA, 2, 1398–1402. DOI: 10.1109/ACSSC.2003.1292216.
Yeganeh, H., & Wang, Z. (2013). Objective Quality Assessment of Tone-Mapped Images. IEEE Transactions on Image Processing, 22(2), 657–667. DOI: 10.1109/TIP.2012.2221725.