Visual Communication Design Based on Machine Vision and Digital Media Communication Technology

Visual communication design (VCD) originates from the masses and is closely related to the masses' lives.The traditional VCD has some problems such as simplification and statism, which cannot meet the personalized design needs of users at different levels.To improve the effect of VCD and enhance the interaction between design efficiency and design results, this paper combined machine vision and digital media communication technology to conduct indepth research on VCD.This paper first used the K-nearest neighbor (KNN) algorithm and support vector machine (SVM) to design the recognition module classifier.It identified and classified the collected design element information, and then combined the perspective transformation method to process the collected graphic design elements visually.Finally, the digital media communication module improved the coding efficiency of design elements such as graphics and the interactivity and quality of VCD.To verify the effectiveness of the method proposed in this paper, it was evaluated from four aspects: information transmission level, visual comfort, interactive experience, and efficiency.The results showed that at the interactive experience level, the average interactive experience scores of the VCD achievements in the two-way, complex and personalized levels were about 86.10 points, 85.01 points, and 83.84 points respectively.The research results of this paper showed that VCD based on machine vision and digital media communication technology can effectively meet different design needs and help to improve the quality of visual communication.