1 Anikeeva I.A.

Roscartography, JSC

An aerial image quality assessment for mapping purposes using open image databases is carried out. While using methods based on reference images, it is a generally adopted practice to use open databases, designed to provide researchers with reference data, i.e. shots whose quality can be accepted as a reference. Such databases are widely used to test the efficiency of methods and algorithms for assessing and improving photographic quality. The possibility of applying this approach for aerial photography assessment in terms of visual properties is shown. The values of fine quality indicators for reference sample of open image library LIVE Database are determined: spatial resolution, photographic sharpness, color balance, random noise level, information completeness indicators – haze brightness, radiometric resolution, percentage of information loss in shadows and illumination. Statistical analysis of reference sample images quality was performed. As a result of its indicators joint assessment, obtained analytically and through calculations, based on a reference sample of LIVE Database images, their recommended and acceptable values are obtained. The next step of the research is an experimental verification and more accurate definition of determined values for fine quality indicators based on airborne imagery, obtained for mapping purposes.
1.   Anikeeva I.A. (2021) Method of numerical estimating aerial images indicators quality for mapping purposes. Geodezia i Kartografia, 82(2), pp. 29-37. (In Russian). DOI: 10.22389/0016-7126-2021-968-2-29-37.
2.   Gmurman V. E. Teoriya veroyatnostei i matematicheskaya statistika. Moskva: Vyssh. shk., 1979, 400 p.
3.   Kadnichanskiy S.A. (2018) Сontrast evaluation of digital aerial and satellite images. Geodezia i Kartografia, 79(3), pp. 46-51. (In Russian). DOI: 10.22389/0016-7126-2018-933-3-46-51.
4.   Lapshenkov E. M. Neetalonnaya otsenka urovnya shuma tsifrovogo izobrazheniya na osnove garmonicheskogo analiza. Komp'yuternaya optika, 2012, Vol. 36, no. 3, pp. 439–447.
5.   Lapshenkov E. M. Realizatsiya metodov otsenki urovnya shuma izobrazheniya v srede MATLAB. Vestnik Moskovskogo gosudarstvennogo universiteta priborostroeniya i informatiki. Ser.: Priborostroenie i informatsionnye tekhnologii, 2013, no. 44, pp. 96–106.
6.   Monich Yu. I., Starovoitov V. V. Otsenki kachestva dlya analiza tsifrovykh izobrazhenii. Iskusstvennyi intellekt, 2008, no. 4, pp. 376–386.
7.   Chandler D. M. (2013) Seven Challenges in Image Quality Assessment: Past, Present, and Future Research. ISRN Signal Processing, no. 905685, 53 p. DOI: 10.1155/2013/905685.
8.   QUALINET Databases. URL: qualinet.github.io/databases/databases/
9.   Learn OpenCV. URL: www.learnopencv.com/image-quality-assessment-brisque/
10.   Laboratory for Image and Video Engineering. URL: https://www.live.ece.utexas.edu/research/quality/subjective.htm (accessed: 13.01.2020).
11.   Liu Y. H., Yang K.-F., Yan H.-M. (2019) No-Reference Image Quality Assessment Method Based on Visual Parameters. Journal оf Electronic Science And Technology, Volume 17, no. 2, pp. 171–184. DOI: 10.11989/JEST.1674-862X.70927091.
12.   Sheikh H.R., Sabir M.F. and Bovik A.C. (2006) A statistical evaluation of recent full reference image quality assessment algorithms. IEEE Transactions on Image Processing, Volume 15, no. 11, pp. 3440-3451.
13.   Sheikh H. R., Wang Z., Cormack L., Bovik A. C. LIVE Image Quality Assessment Database Release 2. URL: live.ece.utexas.edu/research/quality (accessed: 13.01.2020).
14.   Wang Z., Bovik A.C., Sheikh H. R., Simoncelli E. P. (2004) Image quality assessment: from error visibility to structural similarity. IEEE Transactions on Image Processing, Volume 13, no. 4, pp. 600-612.
Anikeeva I.A., 
(2021) Application of open image databases at airborne image quality assessing for mapping purposes. Geodesy and cartography = Geodezia i Kartografia, 82(5), pp. 51-60. (In Russian). DOI: 10.22389/0016-7126-2021-971-5-51-60
Publication History
Received: 07.08.2020
Accepted: 17.02.2021
Published: 20.06.2021


2021 May DOI:

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