Automatic generation of traditional patterns and aesthetic quality evaluation technology

Yi Wu and Kymn Kyungsun ()
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Yi Wu: Seoul National University
Kymn Kyungsun: Seoul National University

Information Technology and Management, 2024, vol. 25, issue 2, No 2, 125-143

Abstract: Abstract As an significant element for the continuation and development of modern design, conventional Chinese decorative patterns satisfy people's emotional dependence on nationality and tradition. At present, traditional patterns are designed mainly by professional designers. Due to the high dependence of the entire design process on labor, the design efficiency is untoward to improve to rapidly meet the explosive growth of business needs. However, a large amount of material data on the Internet and the development of artificial intelligence technology provides an opportunity to work out this development bottleneck. This paper mainly studies the design and implementation of the automatic generation of traditional pattern image layout based on the generation of confrontation network and aesthetic evaluation to score the quality of the generated traditional pattern image. And the scoring results are fed back to the traditional pattern layout generation network to improve the quality of the generated traditional pattern layout pictures so that the entire network structure can adapt to disparate scene requirements. The results show that the SSIM value of the structural similarity obtained by the image of the traditional pattern automatically generated by the model in this paper is larger and closer to 1 than the value of the SSIM obtained by other methods, indicating that the image of the traditional pattern automatically generated by the model has the highest aesthetic quality. Furthermore, the research provides assistance for intelligent technology to solve the development bottleneck of conventional pattern design. Finally, the future research of digital image quality evaluation approach is prospected.

Keywords: Traditional patterns; Automatic generation; Quality evaluation; Aesthetic evaluation (search for similar items in EconPapers)
Date: 2024
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DOI: 10.1007/s10799-022-00356-w

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