<abstract xmlns="http://www.w3.org/1999/xhtml">

<p>Based on big data technology, this paper firstly proposes that visual image generation design based on the improved adversarial generative network becomes one of the ideas to solve the current dilemma of visual communication design, which is not only a convenient, creative means but also changes the traditional way of visual communication design. Secondly, in the extension of visual image samples, in addition to using some operations such as rotation, translation and scale transformation of visual images, an improved DCGAN adversarial generative network algorithm is also used. The improved DCGAN network is then used to augment the number of visual images while maintaining the quality of visual image generation. Finally, intelligent generation models based on visual communication design processes and thinking are investigated, focusing on intelligent data sets and retrieval, image generation techniques, and intelligent solution recommendations. The results show that DCGAN has a lower value of <italic>IS</italic> = 1.5, <italic>FID</italic> = 249.39, <italic>IS</italic> and a higher value of <italic>FID</italic>, indicating that it generates better quality visual images and greatly improves the efficiency and accuracy of visual communication design. This study can extract effective information and generate visual images more accurately and efficiently, and provide technical support for visual communication designers.</p>
</abstract>

Based on big data technology, this paper firstly proposes that visual image generation design based on the improved adversarial generative network becomes one of the ideas to solve the current dilemma of visual communication design, which is not only a convenient, creative means but also changes the traditional way of visual communication design. Secondly, in the extension of visual image samples, in addition to using some operations such as rotation, translation and scale transformation of visual images, an improved DCGAN adversarial generative network algorithm is also used. The improved DCGAN network is then used to augment the number of visual images while maintaining the quality of visual image generation. Finally, intelligent generation models based on visual communication design processes and thinking are investigated, focusing on intelligent data sets and retrieval, image generation techniques, and intelligent solution recommendations. The results show that DCGAN has a lower value of IS = 1.5, FID = 249.39, IS and a higher value of FID, indicating that it generates better quality visual images and greatly improves the efficiency and accuracy of visual communication design. This study can extract effective information and generate visual images more accurately and efficiently, and provide technical support for visual communication designers.

The application of big data technology in the art of visual communication design with information technology

Applied Mathematics and Nonlinear Sciences

This work is licensed under the Creative Commons Attribution 4.0 International License.

Based on big data technology, this paper firstly proposes that visual image generation design based on the improved adversarial generative network becomes one...

The application of big data technology in the art of visual communication design with information technology

Data publikacji: 25 wrz 2023

Zakres stron: -

Otrzymano: 30 gru 2022

Przyjęty: 05 kwi 2023

DOI: https://doi.org/10.2478/amns.2023.2.00370

Słowa kluczowe
Big data technology, Adversarial generative networks, Visual communication design, value, values

© 2023 Peitian Tao et al., published by Sciendo

This work is licensed under the Creative Commons Attribution 4.0 International License.

The application of big data technology in the art of visual communication design with information technology

Data publikacji: 25 wrz 2023

Zakres stron: -

Otrzymano: 30 gru 2022

Przyjęty: 05 kwi 2023

DOI: https://doi.org/10.2478/amns.2023.2.00370

Słowa kluczoweBig data technology, Adversarial generative networks, Visual communication design, value, values

© 2023 Peitian Tao et al., published by Sciendo

This work is licensed under the Creative Commons Attribution 4.0 International License.

Słowa kluczowe
Big data technology, Adversarial generative networks, Visual communication design, value, values