Fusion Application of Computer Vision and Image Transmission in Digital Image Art Design

Abstract

Combining computer vision and image transmission with digital image art design can expand the creative space and provide more creative tools and technologies. To solve the problems in digital image art design, such as the limitation in high level and semantic understanding of images, this paper discussed the integration of computer vision and image transfer in digital image art design, synthesized the related study of histogram equalization, computer vision, edge computing and image transfer and other fields. The key issues in the process of digital image art design were analyzed and discussed in depth. This article applied computer vision algorithms to the analysis of digital images, used edge detection methods for image contour detection, analyzed the efficiency issues of different fusion methods, and introduced computer vision and image transmission into digital image art design. Specific measures for computer vision and image transmission in digital image art design were proposed, and computer vision algorithms were used for experimental analysis. Through comparison, it can be seen that computer vision had an average increase of 0.27 for real-time interaction of digital image art design, 0.27 for augmented reality, 0.28 for virtual reality, 0.28 for style conversion, 0.29 for image generation, and 0.29 for data and information visualization. The improvements in real-time interaction, augmented reality, virtual reality, style conversion, image generation, and data and information visualization were within a reasonable range. Applying image transmission in digital image art design could significantly improve the creative expression, visual style exploration, artistic creation process, audience experience, and response of digital images, ameliorating the effectiveness of digital images.