Application of visual attribute transfer technology in analysing changes in emotional expression in picture books

In picture books, readers can obtain different emotional perceptions according to different image style attributes. Artists often use different combinations of colours, textures, materials, and other style elements in images to convey different emotions in their creations. Especially in picture books for children, there is a strong correlation between the perceived effect of the work and the accuracy and degree of emotional expression. In the process of creating picture books, various factors will affect the efficiency of artists trying to transfer styles to meet their creative needs. With the development of image style transfer technology based on a deep convolutional neural network, artists can use this technology to create works with different styles of emotional changes efficiently. In this paper, we select illustrations of picture books and use deep convolutional neural networks to transfer image styles from three aspects: colour style transfer, texture style, and material style transfer. Through sampling survey experiments, we discuss the changes in image attributes, emotional expression, and emotional perception in picture books for children. The survey results found that the most direct and evident influence on the emotional changes of picture book images is the transfer of colour style attributes, material style attributes, and texture style attributes. The results of this study can provide a valuable reference for improving the accuracy of emotional expression, the depth of meaning extension, and the height of artistic value in picture books for children during the process of an artist's creation. This research stands out by systematically analysing the distinct impact of each style attribute transfer, offering a comprehensive framework that can be utilized by artists and technologists alike to enhance the emotional and artistic quality of children's picture books.