(untitled) (Eureka Journal of Artificial Intelligence and Data Innovation, 2026)
This paper proposes an approach based on statistical and information metrics for assessing the quality of grayscale images. In the study, image pixel intensities were normalized using the Box-Cox transform, and then their main statistical moments (mean, standard deviation, skewness, kurtosis), entropy, sharpness based on the Laplace operator, and Michelson contrast were calculated. The relationship between objective metrics and visual assessment (MOS – mean score) was analyzed using the Spearman and Kendall color correlation coefficients. Experimental results show that the proposed transform stabilizes the image intensity distribution and allows us to determine the statistical relationship between objective indicators and subjective assessment. The results of the study can be used in automatic image quality assessment systems, agricultural monitoring, and digital image processing.