DOI: 10.3390/app13179763 ISSN:

Research Progress on the Aesthetic Quality Assessment of Complex Layout Images Based on Deep Learning

Yumei Pu, Danfei Liu, Siyuan Chen, Yunfei Zhong
  • Fluid Flow and Transfer Processes
  • Computer Science Applications
  • Process Chemistry and Technology
  • General Engineering
  • Instrumentation
  • General Materials Science

With the development of the information age, the layout image is no longer a simple combination of text and graphics, but covers the complex layout image obtained from text, graphics, images and other layout elements through the process of artistic design, pre-press processing, typesetting, and so on. At present, the field of aesthetic-quality assessment mainly focuses on photographic images, and the aesthetic-quality assessment of complex layout images is rarely reported. However, the design of complex layout images such as posters, packaging labels, advertisements, etc., cannot be separated from the evaluation of aesthetic quality. In this paper, layout analysis is performed on complex layout images. Traditional and deep-learning-based methods for image layout analysis and aesthetic-quality assessment are reviewed and analyzed. Finally, the features, advantages and applications of common image aesthetic-quality assessment datasets and layout analysis datasets are compared and analyzed. Limitations and future perspectives of aesthetic assessment of complex layout images are discussed in relation to layout analysis and aesthetic characteristics.

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