OVERVIEW

The construction of visual aesthetic element system in graphic design based on big data

Author: Jing Shen

Date: September, 2023

STATEMENT OF THE PROBLEM

TARGET AUDIENCE

RESULT

Various exquisite graphic designs that can be seen everywhere beautify people’s vision and influence people’s visual aesthetics subtly.

This paper starts with the visual aesthetic elements of graphic design and discusses the construction of visual aesthetic element systems in graphic design by analyzing the composition elements and aesthetic characteristics. Secondly, it proposes to use the biological visual perceptual machine model to process information through two visual pathways in a hierarchical manner. The features of graphic design images are expressed, stored, and extracted using the visual information model. 

Under the continuous development of big data technology and the expansion of new media platforms, the era of digitalization has arrived, and traditional print media can no longer meet the audience’s demand for information dissemination, and the big data platform has pushed the development of communication media to a climax. The study of the dynamic design of graphic design elements differs from the traditional graphic design presentation method, which retains the design laws of graphic design and carries out deep construction and innovation based on the original characteristics. Through the analysis of the characteristics of graphic design elements, new ideas suitable for developing dynamic design are summarized in terms of graphic expressiveness, creative forms of screen transformation, and expressiveness in content

Driven by the economy and technology, people’s perception of visual experience sense is getting

higher and higher. This paper constructs a bio-visual perception machine model applied to the

construction of visual aesthetic elements in graphic design and verifies the effectiveness of the bio-

visual perception model applied to the construction of visual aesthetic elements in graphic design by

using Caltech5 database, Caltech256 database, and Scene15 database.

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