计算机科学
人工智能
场景统计
计算机视觉
图像质量
统计的
图像(数学)
模式识别(心理学)
期限(时间)
数学
统计
感知
量子力学
生物
物理
神经科学
作者
Yuan Chen,Yang Zhao,Shujie Li,Wangmeng Zuo,Wei Jia,Xiaoping Liu
出处
期刊:IEEE Transactions on Circuits and Systems for Video Technology
[Institute of Electrical and Electronics Engineers]
日期:2020-09-01
卷期号:30 (9): 3282-3288
被引量:20
标识
DOI:10.1109/tcsvt.2019.2931589
摘要
Current blind image quality assessment (BIQA) algorithms are mainly designed for natural images. Unfortunately, cartoon and cartoon-like images are quite different from natural images. Hence, recent BIQA methods are not very robust to cartoon images. In this paper, we propose a specific BIQA algorithm designed for cartoon images, which consists of the following terms. First, a cartoon image is divided into edge areas and nonedge areas via a Tchebichef moment (TM)-based process. Second, a multiorder sharpness statistic term is used to measure the quality of the edges, and a sharpness statistic prior model of high-quality (HQ) cartoon images is built. Finally, a local encoding statistic term is adopted to describe the textural complexity in the nonedge areas, and a texture statistic prior model is also established. The experimental results on the cartoon image datasets demonstrate that the proposed method can accurately evaluate the visual quality of cartoon images and is more suitable for cartoon scenarios than some traditional BIQA algorithms.
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