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Technological Development of Image Aesthetics Assessment

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Image and Graphics (ICIG 2021)

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Abstract

Quantitative research on aesthetics is a classic interdisciplinary research. With the rapid development of deep learning, various approaches have been made in image aesthetics assessment (IAA). Starting from the concept of image aesthetics, this report roughly follows the chronological sequence and first introduces the manual design of image aesthetic features. We divide IAA into generic image aesthetics assessment (GIAA) and personalized image aesthetics assessment (PIAA) to introduce separately in the deep learning part. Majority of approaches are GIAA, which purpose is to simulate general aesthetics. In this section, we separately reviewed representative studies of five assessment methods (aesthetic classification, aesthetic regression, aesthetic distribution, IAA with attributes, aesthetic description). Due to the subjectivity of aesthetics, human’s aesthetics will more or less deviate from the generic value. PIAA aims to model the aesthetic preferences of specific user, and the research is of great value. We introduced this novel research in the fifth section. Finally, image aesthetic datasets of different uses are summarized. We hope this comprehensive survey can be helpful to researchers in the field of image and enhance the connection between computer and art.

This work is supported by Research on Quality Evaluation Method of UHD Video based on Hevc (GJ181901).

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Acknowledgements

This work is supported by Training of Outstanding Talents in Beijing in 2017, Research on Quality Evaluation Method of UHD Video based on HEVC (GJ181901).

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Authors and Affiliations

  1. State Key Laboratory of Media Convergence and Communication, Communication University of China, Beijing, China

    Ruoyu Zou, Jiangbo Xu & Ziyu Xue

  2. Academy of Broadcasting Science (NRTA), Beijing, China

    Ziyu Xue

Authors
  1. Ruoyu Zou
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  2. Jiangbo Xu
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  3. Ziyu Xue
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Editors and Affiliations

  1. Peking University, Beijing, China

    Yuxin Peng

  2. Tsinghua University, Beijing, China

    Shi-Min Hu

  3. Tampere University, Tampere, Finland

    Moncef Gabbouj

  4. Zhejiang University, Hangzhou, China

    Kun Zhou

  5. Technion – Israel Institute of Technology, Haifa, Israel

    Michael Elad

  6. Tsinghua University, Beijing, China

    Kun Xu

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Zou, R., Xu, J., Xue, Z. (2021). Technological Development of Image Aesthetics Assessment. In: Peng, Y., Hu, SM., Gabbouj, M., Zhou, K., Elad, M., Xu, K. (eds) Image and Graphics. ICIG 2021. Lecture Notes in Computer Science(), vol 12890. Springer, Cham. https://doi.org/10.1007/978-3-030-87361-5_28

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  • DOI: https://doi.org/10.1007/978-3-030-87361-5_28

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