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

<p>With the advancement of technologies such as big data, machine learning, and human-computer interaction, there has been a notable increase in the expectations for the perceptual experience offered by graphic visual communication design. This study builds upon existing research to define six principal layout features specific to graphic visual communication design and presents a formula for calculating these features. Employing HSV color space theory, we quantify color perception features and utilize a generative adversarial network to establish a model for generating graphic layouts. An example analysis is conducted to validate the model. Notably, except for Q3 (warm color percentage), where the significance level was not below 0.01, all other variables showed significance levels under 0.01. The seven perceptual features that emerged as most influential on user experience include cold color percentage (0.088), color contrast (0.223), color vibrancy (0.188), hierarchical richness (0.267), number of colors (0.088), number of color palettes (0.212), and number of patches (0.081). This research not only enhances the efficiency and quality of graphic visual communication design layouts but also significantly enriches the perceptual experience of users.</p>
</abstract>

With the advancement of technologies such as big data, machine learning, and human-computer interaction, there has been a notable increase in the expectations for the perceptual experience offered by graphic visual communication design. This study builds upon existing research to define six principal layout features specific to graphic visual communication design and presents a formula for calculating these features. Employing HSV color space theory, we quantify color perception features and utilize a generative adversarial network to establish a model for generating graphic layouts. An example analysis is conducted to validate the model. Notably, except for Q3 (warm color percentage), where the significance level was not below 0.01, all other variables showed significance levels under 0.01. The seven perceptual features that emerged as most influential on user experience include cold color percentage (0.088), color contrast (0.223), color vibrancy (0.188), hierarchical richness (0.267), number of colors (0.088), number of color palettes (0.212), and number of patches (0.081). This research not only enhances the efficiency and quality of graphic visual communication design layouts but also significantly enriches the perceptual experience of users.

Research on Graphic Visual Communication Design for User Perception Experience

Applied Mathematics and Nonlinear Sciences

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

{"article-title":"Research on Graphic Visual Communication Design for User Perception Experience"}

With the advancement of technologies such as big data, machine learning, and human-computer interaction, there has been a notable increase in the expectations...

Research on Graphic Visual Communication Design for User Perception Experience

Pubblicato online: 03 giu 2024

Pagine: -

Ricevuto: 04 feb 2024

Accettato: 19 apr 2024

DOI: https://doi.org/10.2478/amns-2024-1347

Parole chiave
Perceptual feature quantization, Layout generation model, Generator, Discriminator, Visual communication design

© 2024 Feng Yuan, et al., published by Sciendo

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

Research on Graphic Visual Communication Design for User Perception Experience

Pubblicato online: 03 giu 2024

Pagine: -

Ricevuto: 04 feb 2024

Accettato: 19 apr 2024

DOI: https://doi.org/10.2478/amns-2024-1347

Parole chiavePerceptual feature quantization, Layout generation model, Generator, Discriminator, Visual communication design

© 2024 Feng Yuan, et al., published by Sciendo

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

Parole chiave
Perceptual feature quantization, Layout generation model, Generator, Discriminator, Visual communication design