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

<p>Art design is rich in content, has a strong visual image impact, and is more easily accepted by the public aesthetics. This paper applies the visual modality in multimodal discourse analysis to the study of the functional value of art history design and aesthetic education, combining the RGB color space and HSV color space to quantify the color features of art design works. Local texture features from art and design works can be extracted using the Fourier transform correlation algorithm. Based on the color template under the aesthetic characteristics to measure the color matching degree in the art design, together with the dark channel characteristics analysis of the texture and aesthetic sense of the art. The expert’s and the public’s perspectives were combined to analyze the visual aesthetic preferences of art and design works, and the value of aesthetic education was explored. The results show that the average scores of experts’ visual aesthetic preferences for samples L7 and L8 are 4.18 and 4.27 respectively, and the average values of the public’s visual aesthetic preferences for L7 and L8 are 4.053 and 4.252 respectively, which indicates that experts’ and the public have high visual aesthetic preferences for artworks L7 and L8, and that the aesthetics of the experts and the public are unified, and that the public in the visual modality can obtain the sense and spiritual enjoyment and pleasure. A pleasant aesthetic experience and mental enjoyment.</p>
</abstract>

Art design is rich in content, has a strong visual image impact, and is more easily accepted by the public aesthetics. This paper applies the visual modality in multimodal discourse analysis to the study of the functional value of art history design and aesthetic education, combining the RGB color space and HSV color space to quantify the color features of art design works. Local texture features from art and design works can be extracted using the Fourier transform correlation algorithm. Based on the color template under the aesthetic characteristics to measure the color matching degree in the art design, together with the dark channel characteristics analysis of the texture and aesthetic sense of the art. The expert’s and the public’s perspectives were combined to analyze the visual aesthetic preferences of art and design works, and the value of aesthetic education was explored. The results show that the average scores of experts’ visual aesthetic preferences for samples L7 and L8 are 4.18 and 4.27 respectively, and the average values of the public’s visual aesthetic preferences for L7 and L8 are 4.053 and 4.252 respectively, which indicates that experts’ and the public have high visual aesthetic preferences for artworks L7 and L8, and that the aesthetics of the experts and the public are unified, and that the public in the visual modality can obtain the sense and spiritual enjoyment and pleasure. A pleasant aesthetic experience and mental enjoyment.

A Study of the Functional Value of Design and Aesthetic Education in Art History under the Multimodal Discourse Analysis Approach

Department of Visual Art, College of Fine Art and Design

Applied Mathematics and Nonlinear Sciences

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

Art design is rich in content, has a strong visual image impact, and is more easily accepted by the public aesthetics. This paper applies the visual modality...

A Study of the Functional Value of Design and Aesthetic Education in Art History under the Multimodal Discourse Analysis Approach

Pubblicato online: 31 gen 2024

Pagine: -

Ricevuto: 22 dic 2023

Accettato: 30 dic 2023

DOI: https://doi.org/10.2478/amns-2024-0257

Parole chiave
Visual modality, Color feature extraction, Texture feature, Art history design

© 2024 Heyao Liu, published by Sciendo

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

A Study of the Functional Value of Design and Aesthetic Education in Art History under the Multimodal Discourse Analysis Approach

Pubblicato online: 31 gen 2024

Pagine: -

Ricevuto: 22 dic 2023

Accettato: 30 dic 2023

DOI: https://doi.org/10.2478/amns-2024-0257

Parole chiaveVisual modality, Color feature extraction, Texture feature, Art history design

© 2024 Heyao Liu, published by Sciendo

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

Parole chiave
Visual modality, Color feature extraction, Texture feature, Art history design