基于局部最大梯度的无参考图像质量评价

蒋平; 张建州

doi:10.11999/JEIT141447

基于局部最大梯度的无参考图像质量评价

doi: 10.11999/JEIT141447

蒋平¹,
张建州²

2.
(四川大学计算机学院成都 610065) ②(榆林学院信息工程系榆林 719000)

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- 被引次数: 0
出版历程
- 收稿日期: 2014-11-20
- 修回日期: 2015-07-15
- 刊出日期: 2015-11-19

No-reference Image Quality Assessment Based on Local Maximum Gradient

Jiang Ping¹,
Zhang Jian-zhou²

2.
(Computer College, Sichuan University, Chengdu 610065, China)

摘要

摘要: 图像质量评价在数字图像处理中应用广泛，无参考图像质量评价更是近些年来的研究热点。该文提出一种基于局部结构的无参考图像质量评价方法，该方法首先利用局部梯度选择强边缘区域，然后通过强边缘的信息来评价图像的质量。该方法的创新之处在于：基于局部最大梯度的像素点质量评价；利用强边缘点的局部质量来估计全局图像质量。该方法可以同时评价噪声图像和模糊图像，图像失真越严重，该方法的评价分数就越低。与图像质量评价数据库的主观评价结果比较表明，该文方法与主观评价结果相关性很强，能很好地反映图像质量的视觉感知效果。
- 图像质量评价 /
- 梯度模板 /
- 高斯噪声 /
- 高斯模糊
Abstract: Image Quality Assessment (IQA) is widely used in digital image processing, and No Reference IQA (NR-IQA) has become the research focus recently. This paper proposes an NR-IQA method based on local structure, which chooses strong structure areas by using local gradients, and assesses the quality of image by utilizing the Maximum Local Gradients (MLG) of strong structure areas. The main novelties are: pixel,s quality assessment based on MLG; whole image quality based on strong edge points, quality. The proposed method can assess noise image and blur image at the same time, and the score of the proposed method is smaller when the distortion is more serious. The results show that the proposed no-reference method for the quality prediction of noise and blur images has a comparable performance to the leading metrics available in literature.
- Image quality assessment /
- Gradient mask /
- Gaussian noise /
- Gaussian blur

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参考文献(19)

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