ВИЗНАЧЕННЯ ПРІОРИТЕТНОСТІ ФАКТОРІВ ЯКОСТІ РАСТРОВИХ ЗОБРАЖЕНЬ ЗА МЕТОДОМ РАНЖУВАННЯ

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.