(46-1) 14 * << * >> * Русский * English * Содержание * Все выпуски

wEscore: quality assessment method of multichannel image visualization with regard to angular resolution
D.S. Sidorchuk 1

1 Institute for Information Transmission Problems of Russian Academy of Sciences (Kharkevich Institute),
127051 Moscow, Bolshoy Karetny pereulok 19, Russia

 PDF, 1607 kB

DOI: 10.18287/2412-6179-CO-911

Страницы: 113-120.

Язык статьи: English.

Аннотация:
This work considers the problem of quality assessment of multichannel image visualization methods. One approach to such an assessment, the Escore quality measure, is studied. This measure, initially proposed for decolorization methods evaluation, can be generalized for the assessment of hyperspectral image visualization methods. It is shown that Escore does not account for the loss of local contrast at the supra-pixel scale. The sensitivity to the latter in humans depends on the observation conditions, so we propose a modified wEscore measure which includes the parameters allowing for the adjustment of the local contrast scale based on the angular resolution of the images. We also describe the adjustment of wEscore parameters for the evaluation of known decolorization algorithms applied to the images from the COLOR250 and the Cadik datasets with given observational conditions. When ranking the results of these algorithms and comparing it to the ranking based on human perception, wEscore turned out to be more accurate than Escore.

Ключевые слова:
hyperspectral image visualization, decolorization, Escore, local contrast.

Благодарности
This work was supported by Russian Science Foundation (Project No. 20-61-47089).

Citation:
Sidorchuk DS. wEscore: quality assessment method of multichannel image visualization with regard to angular resolution. Computer Optics 2022; 46(1): 113-120. DOI: 10.18287/2412-6179-CO-911.

References:
  1. Zhizhin MN, Elwidge K, Poyda AA, Godunov AI, Velikhov VE, Erokhin GN, Alsynbaev KS, Bryksin VM. Using remote sensing data to monitor hydrocarbon production [In Russian]. Informatsionnye Tekhnologii i Vychslitel'nye Sistemy 2014; 3: 97-111.
  2. ENVI – Image processing and analysis solution. Source: <https://www.ittvis.com/envi/>.
  3. ERDAS Imagine. Source: <https://www.hexagongeospatial.com/products/power-portfolio/erdas-imagine>.
  4. Sarycheva A, Grigoryev A, Sidorchuk D, Vladimirov G, Khaitovich P, Efimova O, Gavrilenko O, Stekolshchikova  E, Nikolaev E, Kostyukevich Y. Structure-preserving and perceptually consistent approach for visualization of mass spectrometry imaging datasets. Anal Chem 2020; 93(3): 1677-1685. DOI: 10.1021/acs.analchem.0c04256.
  5. Smets T, Verbeeck N, Claesen M, Asperger A, Griffioen G, Tousseyn T, Waelput W, Waelkens E, De Moor B. Evaluation of distance metrics and spatial autocorrelation in uniform manifold approximation and projection applied to mass spectrometry imaging data. Anal Chem 2019; 91(9): 5706-5714. DOI: 10.1021/acs.analchem.8b05827.
  6. Bratchenko IA, Alonova MV, Myakinin OO, Moryatov AA, Kozlov SV, Zakharov VP. Hyperspectral visualization of skin pathologies in visible region. Computer Optics 2016; 40(2): 240-248. DOI: 10.18287/2412-6179-2016-40-2-240-248.
  7. Ready P, Wintz P. Information extraction, SNR improvement, and data compression in multispectral imagery. IEEE Trans Commun 1973; 21(10): 1123-1131. DOI: 10.1109/TCOM.1973.1091550.
  8. Tyo JS, Konsolakis A, Diersen DI, Olsen RC. Principal-components-based display strategy for spectral imagery. IEEE Trans Geosci Remote Sens 2003; 41(3): 708-718. DOI: 10.1109/TGRS.2003.808879.
  9. Sidorchuk DS, Volkov VV, Nikonorov AV. Comparison of the nonlinear contrast-preserving visualization method for multispectral images with well-known decolorization algorithms [In Russian]. Information Processes 2020; 20(1): 41-54.
  10. Sokolov V, Nikolaev D, Karpenko S, Schaefer G. On contrast-preserving visualisation of multispectral datasets. International Symposium on Visual Computing 2010: 173-180.
  11. Socolinsky DA, Wolff LB. A new visualization paradigm for multispectral imagery and data fusion. Proc IEEE Computer Society Conference on Computer Vision and Pattern Recognition 1999: 319-324. DOI: 10.1109/CVPR.1999.786958.
  12. Sidorchuk DS, Konovalenko IA, Gladilin SA, Maksimov YI. Noise estimation for multispectral visualization. Sensornye Sistemy 2016; 30(4): 344-350.
  13. Sidorchuk DS, Volkov VV. Fusion of radar, visible and thermal imagery with account for differences in brightness and chromaticity perception. Sensornye Sistemy 2018; 32(1): 14-18. DOI: 10.7868/S0235009218010031.
  14. Zhang B, Yu X. Hyperspectral image visualization using t-distributed stochastic neighbor embedding. Proc SPIE 2015; 9815: 981504. DOI: 10.1117/12.2205840.
  15. Liao D, Qian Y, Zhou J. Visualization of hyperspectral imaging data based on manifold alignment. 22nd Int Conf on Pattern Recognition 2014: 70-75. DOI: 10.1109/ICPR.2014.22.
  16. Myasnikov EV. Nonlinear mapping methods with adjustable computational complexity for hyperspectral image analysis. Proc SPIE 2015; 9875: 987508. DOI: 10.1117/12.2228831.
  17. Myasnikov EV. Fast techniques for nonlinear mapping of hyperspectral data. Proc SPIE 2017; 10341: 103411D. DOI: 10.1117/12.2268707.
  18. Lu C, Xu L, Jia J. Contrast preserving decolorization with perception-based quality metrics. International journal of computer vision 2014; 110(2): 222-239.
  19. Lu C, Xu L, Jia J. Contrast preserving decolorization. IEEE Int Conf on Computational Photography 2012: 1-7. DOI: 10.1109/ICCPhot.2012.6215215.
  20. Hayes AE, Finlayson GD, Montagna R. RGB-NIR image fusion: metric and psychophysical experiments. Proc SPIE 2015; 9396: 93960U. DOI: 10.1117/12.2079224.
  21. Liao D, Chen S, Qian Y. Visualization of hyperspectral images using moving least squares. 24th Int Conf on Pattern Recognition 2018: 2851-2856. DOI: 10.1109/ICPR.2018.8546018.
  22. Coliban RM, Marincas M, Hatfaludi C, Ivanovici M. Linear and non-linear models for remotely-sensed hyperspectral image visualization. Remote Sens 2020; 12(15): 2479. DOI: 10.3390/rs12152479.
  23. Kang X, Duan P, Li S, Benediktsson JA. Decolorization-based hyperspectral image visualization. IEEE Trans Geosci Remote Sens 2018; 56(8): 4346-4360. DOI: 10.1109/TGRS.2018.2815588.
  24. Gabarda S, Cristóbal G. Quality evaluation of blurred and noisy images through local entropy histograms. Proc SPIE 2007; 6592: 659214. DOI: 10.1117/12.721952.
  25. Tang R, Liu H, Wei J, Tang W. Supervised learning with convolutional neural networks for hyperspectral visualization. Remote Sens Lett 2020; 11(4): 363-372. DOI: 10.1080/2150704X.2020.1717014.
  26. Sowmya V, Govind D, Soman KP. Significance of incorporating chrominance information for effective color-to-grayscale image conversion. Signal Image Video Process 2017; 11(1): 129-136. DOI: 10.1007/s11760-016-0911-8.
  27. Zhao H, Zhang H, Jin X. Efficient image decolorization with a multimodal contrast-preserving measure. Comput Graph 2018; 70: 251-260. DOI: 10.1016/j.cag.2017.07.009.
  28. Zhang X, Liu S. Contrast preserving image decolorization combining global features and local semantic features. Vis Comput 2018; 34(6): 1099-1108.
  29. Ĉadík M. Perceptual evaluation of color-to-grayscale image conversions. Comput Graph Forum 2008; 27(7): 1745-1754. DOI: 10.1111/j.1467-8659.2008.01319.x.
  30. McLaren K. XIII–The development of the CIE 1976 (L* a* b*) uniform colour space and colour-difference formula. J Soc Dye Colour 1976; 92(9): 338-341. DOI: 10.1111/j.1478-4408.1976.tb03301.x.
  31. Van der Horst GJC, Bouman MA. Spatiotemporal chromaticity discrimination. J Opt Soc Am 1969; 59(11): 1482-1488. DOI: 10.1364/JOSA.59.001482.
  32. Kendall MG. Rank correlation methods. London: Charles Griffin and Co Ltd; 1948.
  33. Grundland M, Dodgson NA. Decolorize: Fast, contrast enhancing, color to grayscale conversion. Pattern Recognit 2007; 40(11): 2891-2896. DOI: 10.1016/j.patcog.2006.11.003.
  34. Gooch AA, Olsen SC, Tumblin JE, Gooch BS. Color2gray: salience-preserving color removal. ACM Trans Graph 2005; 24(3): 634-639. DOI: 10.1145/1073204.1073241.
  35. Smith K, Landes PE, Thollot J, Myszkowski K. Apparent greyscale: A simple and fast conversion to perceptually accurate images and video. Comput Graph Forum 2008; 27(2): 193-200. DOI: 10.1111/j.1467-8659.2008.01116.x.
  36. Bala R, Braun KM. Color-to-grayscale conversion to maintain discriminability. Proc SPIE 2003; 5293: 196-202. DOI: 10.1117/12.532192.
  37. Neumann L, Čadik M, Nemcsics A. An efficient perception-based adaptive color to gray transformation. Proc Third Eurographics Conf on Computational Aesthetics in Graphics, Visualization and Imaging 2007: 73-80.
  38. Rasche K, Geist R, Westall J. Detail preserving reproduction of color images for monochromats and dichromats. IEEE Comput Graph Appl 2005; 25(3): 22-30. DOI: 10.1109/MCG.2005.54.
  39. Thurstone LL. A law of comparative judgment. Psychol Rev 1927; 34(4): 273-286. DOI: 10.1037/h0070288.

© 2009, IPSI RAS
Россия, 443001, Самара, ул. Молодогвардейская, 151; электронная почта: journal@computeroptics.ru; тел: +7 (846) 242-41-24 (ответственный секретарь), +7 (846) 332-56-22 (технический редактор), факс: +7 (846) 332-56-20