The mandala thangka, as a religious art in Tibetan Buddhism, is an invaluable cultural and artistic heritage.
We hope that our survey could serve as a comprehensive reference source for future research on computational aesthetics in visual media. Finally, challenges and potential future directions for this field are discussed. Moreover, the possibility of aesthetic-aware color enhancement, recomposition of photo images, and automatic generation of aesthetic-guided art paintings through computational approaches are summarized. Besides, we systematically evaluate recent deep learning techniques that are useful for developing robust models for aesthetic prediction tasks in scoring, distribution, attribute, and description. Then, conventional aesthetic evaluation approaches based on handcrafted features are reviewed. We firstly introduce aesthetic assessment benchmark datasets in different categories. In this survey, we present a comprehensive listing of the reviewed works on aesthetic assessment of photographs and paintings, mainly highlighting the contributions and innovations of the existing approaches.
However, there have been few attempts in reviewing works from different types of visual arts, due to their significant differences in visual features and aesthetic principles. Various approaches have been proposed in copious literature trying to solve this challenging problem. Computational image aesthetic evaluation is a computable human aesthetic perception and judgment realized by machines, which has a significant impact on a variety of applications such as image advanced search and promotional exhibition of painting arts.
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