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Digital image quality evaluation based on multi-scale aesthetic features and graph convolutional neural networks.

Jiati Wu1, Dan Li2

  • 1College of Design and Art, Xingzhi College Zhejiang Normal University, Jinhua, 321000, China. wujiati6677@163.com.

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|December 29, 2025
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Summary

This study introduces a dual-branch model for digital image aesthetic evaluation, improving how local image semantics and spatial composition are understood. The model achieves high accuracy in aesthetic quality assessment, balancing performance and efficiency.

Keywords:
Aesthetic featuresDigital imageGraph convolutional neural networkMulti-scaleQuality evaluation

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Area of Science:

  • Computer Vision
  • Artificial Intelligence
  • Image Processing

Background:

  • Digital image aesthetic quality evaluation is crucial for content screening and recommendation on visual platforms.
  • Existing methods struggle to model local semantics and spatial composition, limiting subjective beauty assessment.
  • Social media growth necessitates advanced image quality evaluation tools.

Purpose of the Study:

  • To propose a dual-branch collaborative model for digital image aesthetic perception.
  • To enhance feature expression and model spatial relationships for more accurate beauty assessment.
  • To provide a comprehensive automated solution for image aesthetic evaluation.

Main Methods:

  • Developed a dual-branch model comprising a semantic guided multi-scale perception network and a composition structure perception graph network.
  • Employed a semantic attention mechanism and multi-scale fusion for enhanced feature expression.
  • Utilized graph convolution to model spatial relationships between image regions.

Main Results:

  • Achieved a Pearson linear correlation coefficient of 0.884 in the high segment for aesthetic quality evaluation.
  • Obtained a Kendall rank correlation coefficient of 0.797 for landscape images.
  • Demonstrated outstanding performance in rating consistency, semantic perception, and structural modeling with efficient parameters (8.6M) and high inference speed (54.2 FPS).

Conclusions:

  • The proposed dual-branch model effectively addresses limitations in current image aesthetic evaluation methods.
  • The model offers a more accurate and comprehensive automated solution for image quality assessment.
  • This research contributes to intelligent image understanding and quality control applications.