Application of Deep Learning Algorithms to Visual Communication Courses.

Zewen Wang,Jiayi Li,Hui Xu,Jieting Wu

doi:10.3389/fpsyg.2021.713723

Abstract

There are rare studies on the combination of visual communication courses and image style transfer. Nevertheless, such a combination can make students understand the difference in perception brought by image styles more vividly. Therefore, a collaborative application is reported here combining visual communication courses and image style transfer. First, the visual communication courses are sorted out to obtain the relationship between them and image style transfer. Then, a style transfer method based on deep learning is designed, and a fast transfer network is introduced. Moreover, the image rendering is accelerated by separating training and execution. Besides, a fast style conversion network is constructed based on TensorFlow, and a style model is obtained after training. Finally, six types of images are selected from the Google Gallery for the conversion of image style, including landscape images, architectural images, character images, animal images, cartoon images, and hand-painted images. The style transfer method achieves excellent effects on the whole image besides the part hard to be rendered. Furthermore, the increase in iterations of the image style transfer network alleviates lack of image content and image style. The image style transfer method reported here can quickly transmit image style in less than 1 s and realize real-time image style transmission. Besides, this method effectively improves the stylization effect and image quality during the image style conversion. The proposed style transfer system can increase students’ understanding of different artistic styles in visual communication courses, thereby improving the learning efficiency of students.

Highlights

Of recent years, with the rapid economic and social developments in China, the public has a new understanding of talent training, and the concept of talent training has shifted (Wang et al, 2015)
Many people think that visual communication design is “graphic design,” which is inaccurate (Jun, 2018; Yanuarsari and Setiawan, 2018)
In the designed image style transfer network, the image style can be better transferred, and the style of the image is consistent with the style image

Summary

Introduction

With the rapid economic and social developments in China, the public has a new understanding of talent training, and the concept of talent training has shifted (Wang et al, 2015). The core of talent training in modern society has changed from having students master knowledge to allowing students to adapt to a lifelong learning society; with positive attitudes to master knowledge and skills, students should have the ability to knowledge conversion, critical thinking, and solve practical problems (Craik and Wyatt-Rollason, 2002; Fryer and Vermunt, 2018). These are consistent with the learning methods advocated by deep learning and the overall. The influence and participation of multimedia technology on art and design keep increasing, and the educational methods of visual communication design have attracted full attention (Kim and Lee, 2016; Delhey and Peters, 2017)

Methods

Results

Discussion

Conclusion