Metaverse-oriented visual art quality enhancement strategies: a field architecture design and fuzzy assessment theory perspective

Abstract

Visual art was originally measured by viewing and appreciating graphic works, and there was no previous research into ways to improve the quality of visual art. With the rapid development of visual arts and technology, the question of how to improve quality has become an urgent one. As the most cutting-edge and hottest concept in the international arena today, the development and application of metaverse technology has widely drawn the close attention of various industries, including management, economy, education, and art. However, there is no in-depth and clear research on the concept of metaverse in the field of art, especially in the field of visual art. We believe that the creation of visual art in the context of metaverse will be an important direction for art development in the future, and can also greatly contribute to the improvement of the quality of metaverse visual art presentation. Therefore, we focus on the issue of visual art quality assessment in our research, and propose a theory and method of metaverse-oriented future visual art quality assessment. The method focuses on the G1-entropy value method to calculate the weights in visual arts, combines qualitative research with quantitative research, and proposes the improvement path and countermeasures for visual arts. In summary, our research aims to address the theoretical approaches to the design of the metaverse field architecture and the assessment of art quality for the future introduction of the metaverse. The main contributions of our research are focused on the following three aspects: 1. The construction of the visual art field architecture draws on the functional requirements analysis method of system science simulation, considering that the entire visual art metaverse field architecture is constructed at three levels: the bottom data support layer, the middle technical support layer and the upper technical application layer. 2. The G1-entropy combination weighting method is used to derive the importance ranking of visual art quality indicators and identify key factors, and to derive suggestions for quality improvement based on the key indicator factors. More importantly, we also build a field architecture for future-oriented visual arts in this study, which bridges the gap in the structural design of visual arts after the introduction of the future concept. Our present study makes a great contribution to the application of visual art quality enhancement, focusing on the analysis of new concepts and the improvement of old methods, building a new scene of organic combination of new technologies and traditional visual art, with practical research theoretical support for the promotion and progress of the disciplinary field.