Abstract
With the rapid development of computer and digital media technology, more and more visual artworks are created, preserved, and transmitted digitally, which has become an indispensable spiritual wealth in this era. However, the aesthetic evaluation of traditional visual artworks can only rely on artists or appreciation experts for qualitative description, and the results have great subjective uncertainty due to their different personal knowledge and experience. Therefore, computational aesthetics came into being, that is, using a computer model to assist in quantitative evaluation of visual artworks. These models have broad application prospects in the fields of aesthetic evaluation and correction, art style identification, and so on. Based on this, this paper proposes a design of visual communication effect evaluation method of artworks based on machine learning. First, some characteristic variables are constructed to quantify the balance, contrast, and harmony in icon design criteria, and these three common design criteria are transformed into a mathematical expression. Based on the powerful fitting, classification, and generalization capabilities of the SVM method, we choose it as our base model. Then, the artificial evaluation scores are regressed to the calculated characteristic variables to obtain the statistical linear regression models corresponding to the three design criteria. The experimental results show that the evaluation model and manual evaluation results can reach a significant correlation in the same dimension, which verifies the effectiveness of the model.
Highlights
Visual art refers to the use of certain material materials to transform human emotions and understanding of the world into intuitive visual images in people’s sight, including twodimensional planar works, three-dimensional sculpture, dynamic film, and television [1]
The traditional aesthetic analysis of visual art can only rely on artists or appreciation experts to make qualitative descriptions according to their personal knowledge and opinions, and the results are highly dependent on the expert level and have subjective uncertainty [5]. e computer, as a general digital processing machine, can assist people to objectively count out some aesthetic or style characteristics of artistic works, so the use of computer-assisted human aesthetic analysis of artistic works is a perfect complement to the artistic aesthetic simplicity
If we can use a computer to analyze their aesthetic characteristics and efficiently generate artistic patterns, we can quantitatively reveal the aesthetic law of artistic patterns, which is of great significance to modern pattern design and digital protection of national artistic patterns
Summary
Visual art refers to the use of certain material materials to transform human emotions and understanding of the world into intuitive visual images in people’s sight, including twodimensional planar works, three-dimensional sculpture, dynamic film, and television [1]. E computer, as a general digital processing machine, can assist people to objectively count out some aesthetic or style characteristics of artistic works, so the use of computer-assisted human aesthetic analysis of artistic works is a perfect complement to the artistic aesthetic simplicity. Is research establishes a bridge between computer and visual artworks, and its research purpose is to enable the computer to carry out quantitative analysis, calculation, and evaluation of the aesthetic feeling of graphics and images independently. En, the artificial evaluation scores are regressed to the calculated characteristic variables to obtain the statistical linear regression models corresponding to the three design criteria is paper selects the digital art form in the field of graphics and images and carries out the research of computational aesthetics in a new dimension Some characteristic variables are constructed to quantify the balance, contrast, and harmony in icon design criteria, and these three common design criteria are transformed into a mathematical expression. en, the artificial evaluation scores are regressed to the calculated characteristic variables to obtain the statistical linear regression models corresponding to the three design criteria is paper selects the digital art form in the field of graphics and images and carries out the research of computational aesthetics in a new dimension
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