Aesthetic Enhancement via Color Area and Location Awareness

Yang, Bailin; Wang, Qingxu; Li, Frederick W.B.; Liang, Xiaohui; Wei, Tianxiang; Zhu, Changrui

Abstract

Choosing a suitable color palette can typically improve image aesthetic, where a naive way is choosing harmonious colors from some pre-defined color combinations in color wheels. However, color palettes only consider the usage of color types without specifying their amount in an image. Also, it is still challenging to automatically assign individual palette colors to suitable image regions for maximizing image aesthetic quality. Motivated by these, we propose to construct a contribution-aware color palette from images with high aesthetic quality, enabling color transfer by matching the coloring and regional characteristics of an input image. We hence exploit public image datasets, extracting color composition and embedded color contribution features from aesthetic images to generate our proposed color palettes. We consider both image area ratio and image location as the color contribution features to extract. We have conducted quantitative experiments to demonstrate that our method outperforms existing methods through SSIM (Structural SIMilarity) and PSNR (Peak Signal to Noise Ratio) for objective image quality measurement and no-reference image assessment (NIMA) for image aesthetic scoring.

Citation

Yang, B., Wang, Q., Li, F. W., Liang, X., Wei, T., & Zhu, C. (2022, October). Aesthetic Enhancement via Color Area and Location Awareness. Presented at The 30th Pacific Conference on Computer Graphics and Applications, Pacific Graphics 2022, Kyoto, Japan

• Presentation Conference Type: Conference Paper (published)
• Conference Name: The 30th Pacific Conference on Computer Graphics and Applications, Pacific Graphics 2022
• Start Date: Oct 5, 2022
• End Date: Oct 8, 2022
• Acceptance Date: Aug 25, 2022
• Publication Date: 2022
• Deposit Date: Oct 31, 2022
• Publicly Available Date: Nov 1, 2022
• DOI: https://doi.org/10.2312/pg.20221247
• Public URL: https://durham-repository.worktribe.com/output/1135350
• Related Public URLs: https://diglib.eg.org/handle/10.2312/pg20221247