DETERMINATION OF THE PRIORITY OF RASTER IMAGE QUALITY FACTORS USING THE RANKING METHOD

Authors

DOI:

https://doi.org/10.20998/2079-0023.2025.02.16

Keywords:

ranking, factor, priority, raster image, quality assessment, priority influence model, interrelation between factors, direct influence, indirect influence

Abstract

Theoretical principles regarding the quality of raster images are provided. A wide range of application areas of raster graphic information is defined, including education, medicine, and printing. An analysis of recent studies and publications is conducted. The aim and main objectives of the research are formulated. A methodological approach to identifying the priority levels of factors influencing raster image quality based on ranking is demonstrated. A set of influencing factors is distinguished, including resolution, color depth, color model, file format, file size, image dimensions, compression level, brightness, saturation, and sharpness. To structure the interrelationships among these parameters, predicate logic constructions are applied. It is established that certain factors may exert both direct and indirect influence on other elements. Tables are developed to represent the connections for each factor. Hierarchical trees of direct and indirect influences and dependencies are constructed. An example of hierarchical trees for one of the selected factors is presented. Based on the analysis of the structure of interconnections, the ranking of quality factors is carried out. For this purpose, the number of each type of connection is counted, and corresponding weight coefficients are introduced. Positive weight values are assigned to influences, while negative ones are assigned to dependencies. The importance scores of the factors are calculated. A normalization of the values is performed to transform the scale into a positive domain. A final evaluation is conducted, taking into account the normalization coefficient. Factor ranks and the corresponding levels of priority are determined. Input data and ranking results are presented in tabular form. A model that reflects the priority levels of influencing factors on raster image quality is developed. The obtained results can be applied for image quality assessment based on fuzzy logic and machine learning methods, followed by the development of a corresponding fuzzy system.

Author Biographies

Alona Kudriashova, Lviv Polytechnic National University

Doctor of Technical Sciences, Docent, Lviv Polytechnic National University, Associate Professor at the Department of Virtual Reality Systems, Lviv, Ukraine

Taras Oliyarnyk, Lviv Polytechnic National University

Postgraduate, Lviv Polytechnic National University, Lviv, Ukraine

References

Piskozub Y. Z., Kudriashova A. V., Oliyarnyk T. I. Model faktoriv vplyvu na yakist tsyfrovykh zobrazhen [Model of factors affecting digital image quality]. Vymiriuvalna ta obchysliuvalna tekhnika v tekhnolohichnykh protsesakh [Measuring and Computer Equipment in Technological Processes]. 2024, vol. 2, pp. 107–111. (In Ukr.).

Kudriashova A., Pikh I., Senkivskyy V., Oliyarnyk T., Bilyk O. De-signing an intelligent system for digital image classification. 6th International Workshop on Intelligent Information Technologies & Systems of Information Security. 2025, vol. 3963, pp. 208–218.

Liang J., Cao J., Sun G., Zhang K., Van Gool L., Timofte R. Swinir: Image restoration using swin transformer. IEEE/CVF International Conference on Computer Vision. 2021, pp. 1833–1844.

Park J., Hwang D., Kim K. Y., Kang S. K., Kim Y. K., Lee J. S. Com-puted tomography super-resolution using deep convolutional neural network. Physics in Medicine & Biology. 2018, vol. 63, no. 14, article 145011.

Negrin I., Kripka M., Yepes V. Multi-criteria optimization for sus-tainability-based design of reinforced concrete frame buildings. Journal of Cleaner Production. 2023, vol. 425, article 139115.

Eybposh M. H., Cai C., Moossavi A. et al. ConIQA: A deep learning method for perceptual image quality assessment with limited data. Sci Rep. 2024, vol. 14, article 20066.

Tian D., Khan M. U., Luo M. R. The Development of three image quality evaluation metrics based on a comprehensive dataset. Color and Imaging Conference. 2021, vol. 29, pp. 77–82.

Jamil S. Review of image quality assessment methods for com-pressed images. Journal of Imaging. 2024, vol. 10, no. 5, article 113.

Zhang Z., Wu W., Cheng Y., Min X., Zhai G. Perceptual quality assessment for fine-grained compressed images. Journal of Visual Communication and Image Representation. 2023, vol. 90, article 103696.

Testolina M., Upenik E., Sneyer J., Ebrahimi T. Towards JPEG AIC part 3: visual quality assessment of high to visually lossless image coding. Applications of Digital Image Processing XLV. 2022, vol. 12226, pp. 90–98.

Dornauer B., Felderer M. Web image formats: Assessment of their real-world-usage and performance across popular web browsers. In-ternational Conference on Product-Focused Software Process Im-provement. 2023, pp. 132–147.

Chen T., Yang X., Li N. et al. Underwater image quality assessment method based on color space multi-feature fusion. Sci Rep. 2023, vol. 13, article 16838.

Li C., Tian Y., Ling X., Zhang Z., Duan H., Wu H., Jia Z., Liu X., Min X., Lu G., Lin W., Zhai G. Image quality assessment: From human to machine preference. Computer Vision and Pattern Recognition Conference. 2025, pp. 7570–7581.

Frackiewicz M, Machalica Ł, Palus H. New combined metric for full-reference image quality assessment. Symmetry. 2024, vol. 16, no. 12, article 1622.

Senkivskyi V. M., Kudriashova A. V. Modeli informatsiinoi tekhnolohii proektuvannia pisliadrukarskykh protsesiv: monohrafiia [Models of information technology for postpress process design: monograph]. Lviv, UAD, 2022. 204 p. (In Ukr.).

Kudriashova A. V., Selmenskyi R. A. Rol ontolohii v otsiniuvanni kompetentnosti ekspertiv. Metodyka opratsiuvannia ekspertnykh vysnovkiv shchodo faktoriv vplyvu na yakist pisliadrukarskoho opratsiuvannia knyzhkovykh vydan [The role of ontology in assessing expert competence. Methodology for processing expert opinions on factors affecting the quality of postpress book processing]. Polihrafiia i vydavnycha sprava [Printing and Publishing]. 2022, vol. 2, no. 84, pp. 36–43. (In Ukr.).

Downloads

Published

2025-12-29

How to Cite

Kudriashova, A., & Oliyarnyk, T. (2025). DETERMINATION OF THE PRIORITY OF RASTER IMAGE QUALITY FACTORS USING THE RANKING METHOD. Bulletin of National Technical University "KhPI". Series: System Analysis, Control and Information Technologies, (2 (14), 112–116. https://doi.org/10.20998/2079-0023.2025.02.16

Issue

No. 2 (14) (2025)

Section

INFORMATION TECHNOLOGY

License

Creative Commons License

This work is licensed under a Creative Commons Attribution 4.0 International License.

Authors who publish with this journal agree to the following terms:

  • Authors retain copyright and grant the journal right of first publication with the work simultaneously licensed under a Creative Commons Attribution License that allows others to share the work with an acknowledgement of the work's authorship and initial publication in this journal.
  • Authors are able to enter into separate, additional contractual arrangements for the non-exclusive distribution of the journal's published version of the work (e.g., post it to an institutional repository or publish it in a book), with an acknowledgement of its initial publication in this journal.
  • Authors are permitted and encouraged to post their work online (e.g., in institutional repositories or on their website) prior to and during the submission process, as it can lead to productive exchanges, as well as earlier and greater citation of published work (See The Effect of Open Access).