Aesthetic Enhancement via Color Area and Location Awareness
Loading...
Date
2022
Journal Title
Journal ISSN
Volume Title
Publisher
The Eurographics Association
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.
Description
@inproceedings{10.2312:pg.20221247,
booktitle = {Pacific Graphics Short Papers, Posters, and Work-in-Progress Papers},
editor = {Yang, Yin and Parakkat, Amal D. and Deng, Bailin and Noh, Seung-Tak},
title = {{Aesthetic Enhancement via Color Area and Location Awareness}},
author = {Yang, Bailin and Wang, Qingxu and Li, Frederick W. B. and Liang, Xiaohui and Wei, Tianxiang and Zhu, Changrui},
year = {2022},
publisher = {The Eurographics Association},
ISBN = {978-3-03868-190-8},
DOI = {10.2312/pg.20221247}
}