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

<p>Based on the connection between visual psychology and landscape painting, the article investigates the spatiality characteristics of images and the visual balance factors affecting the creation of landscape paintings. By applying a vector median filter, this study successfully removes noise from color landscape painting images and uses vectors to represent these noise-reduced images. Lab color model was constructed and combined with cluster analysis technique to calculate the color difference between image pixel points. Binarization of the image is achieved by preprocessing the segmented image. The study estimated and analyzed landscape paintings’ aesthetic characteristics and quantitative values. The results show that among the 30 selected landscape paintings, the percentage of white space area is more than 0.4 is 36.67%, while the rate of less than 0.4 is 63.33%. Among the landscape painting creators who participated in the study, the mean of the total scores of the experienced ones on the four visual psycho-spatial abilities was 83.39, which was 14.217 points higher than that of the inexperienced group. This indicates that the method proposed in this paper is very effective in assessing the visual psychospatial abilities of landscape painting creators, and that the creators can improve their creative skills after long-term training.</p>
</abstract>

Based on the connection between visual psychology and landscape painting, the article investigates the spatiality characteristics of images and the visual balance factors affecting the creation of landscape paintings. By applying a vector median filter, this study successfully removes noise from color landscape painting images and uses vectors to represent these noise-reduced images. Lab color model was constructed and combined with cluster analysis technique to calculate the color difference between image pixel points. Binarization of the image is achieved by preprocessing the segmented image. The study estimated and analyzed landscape paintings’ aesthetic characteristics and quantitative values. The results show that among the 30 selected landscape paintings, the percentage of white space area is more than 0.4 is 36.67%, while the rate of less than 0.4 is 63.33%. Among the landscape painting creators who participated in the study, the mean of the total scores of the experienced ones on the four visual psycho-spatial abilities was 83.39, which was 14.217 points higher than that of the inexperienced group. This indicates that the method proposed in this paper is very effective in assessing the visual psychospatial abilities of landscape painting creators, and that the creators can improve their creative skills after long-term training.

The Visual Psychological Space of Chinese Traditional Landscape Painting Creators in the New Media Background

Applied Mathematics and Nonlinear Sciences

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

Based on the connection between visual psychology and landscape painting, the article investigates the spatiality characteristics of images and the visual...

The Visual Psychological Space of Chinese Traditional Landscape Painting Creators in the New Media Background

Published Online: Feb 26, 2024

Page range: -

Received: Jan 10, 2024

Accepted: Jan 17, 2024

DOI: https://doi.org/10.2478/amns-2024-0491

Keywords
Image spatial features, Vector median filtering, Lab color model, Pixel chromatic aberration, Visual psycho-spatial

© 2024 Jin Shang, published by Sciendo

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

The Visual Psychological Space of Chinese Traditional Landscape Painting Creators in the New Media Background

Published Online: Feb 26, 2024

Page range: -

Received: Jan 10, 2024

Accepted: Jan 17, 2024

DOI: https://doi.org/10.2478/amns-2024-0491

KeywordsImage spatial features, Vector median filtering, Lab color model, Pixel chromatic aberration, Visual psycho-spatial

© 2024 Jin Shang, published by Sciendo

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

Keywords
Image spatial features, Vector median filtering, Lab color model, Pixel chromatic aberration, Visual psycho-spatial