计算机科学
人工智能
公制(单位)
任务(项目管理)
动画
感知
质量(理念)
编码(集合论)
计算机视觉
图像质量
相似性(几何)
图像(数学)
模式识别(心理学)
计算机图形学(图像)
神经科学
运营管理
程序设计语言
管理
集合(抽象数据类型)
经济
生物
哲学
认识论
作者
Hangwei Chen,Xiongli Chai,Feng Shao,Xuejin Wang,Qiuping Jiang,Xiangchao Meng,Yo‐Sung Ho
标识
DOI:10.1109/tmm.2021.3121875
摘要
In the animation industry, automatically predicting the quality of cartoon images based on the inputs of general distortions and color change is an urgent task, while the existing no-reference (NR) methods usually measure the perceptual quality of the natural images. In this paper, based on the observation that structure and color are the main factors affecting cartoon images quality, we proposed a new NR quality prediction metric for cartoon images, which fully takes gradient and color information into account. The experimental results on our newly constructed NBU-CIQAD dataset with color change and other existing cartoon image dataset demonstrate that the proposed method significantly outperforms existing no-references methods for the task of cartoon image quality assessment. The database and code will be released at https://github.com/1010075746/NBU-CIQAD .
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