Automatic Coloring Algorithm for Hand-Drawn Illustrations Based on Human Vision Model

Objectives: The objective was to develop an advanced, developed-up-to-date method that utilizes visual nonlinearity, contrast sensitivity, and brightness adaptation to colorize human-drawn pictures by itself taking into account human color perception. To minimize the damaging effect of computational findings on the human vision system and also to enhance the visual quality and color accuracy of digital pictures by identifying commonalities among the possible solutions. Methods: This research makes use of visual traits like contrast sensitivity, visual nonlinearity, and brightness adaptation in conjunction with an optimization method for pattern searches. The proposed algorithm is tested and fine-tuned using the xRite Color Checker 24-color card. Mathematical modeling and actual trials compare the model's algorithm performance to that of more conventional approaches like least squares and limited least squares, with the goal of reducing the inaccuracy in color differences. Findings: The studies' best outcome was a mean of 3.25, a standard deviation of 0.72, a maximum of 6.75, and a low of 0.42. Additionally, less than three percent of the corrected color blocks had a color difference of more than forty-one percent. These outcomes highlight the algorithm's capacity to produce visually accurate images by simulating human visual perception. Novelty: This study introduces a novel method for a color correcting system that takes into account features of the human visual system. Visual multichannel processing and nonlinearity are two of its defining characteristics that allow it to make incredibly realistic graphics and packaging designs by bridging the gap between machine-generated results and actual human perception. Keywords: Graphic design, Image coding, Colorization, Human vision, Pattern Search Algorithm