局部方差在图像质量评价中的应用

王宇庆

局部方差在图像质量评价中的应用

王宇庆

中国科学院长春光学精密机械与物理研究所,吉林长春 130033

基金项目:

中国科学院二期创新工程基金资助项目(No.C50Top2)

详细信息

作者简介:
王宇庆 (1979-),男,吉林长春人,博士,助理研究员,主要从事图像质量评价、图像融合、图像增强、VLSI数字信号处理等方面的研究。E-mail:wyq7903@yahoo.com.cn

中图分类号: TP391.4
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出版历程
- 收稿日期: 2011-07-11
- 修回日期: 2011-08-13
- 刊出日期: 2011-10-25

Application of local variance in image quality assessment

WANG Yu-qing

Changchun Institute of Optics, Fine Mechanics and Physics, Chinese Academy of Sciences, Changchun 130033, China

摘要

摘要: 将灰度图像的局部方差分布(QLS)作为表征图像结构信息的一个重要特征,对局部方差分布矩阵进行奇异值分解,计算得到相应的奇异值特征向量;通过计算降质图像与原参考图像局部方差矩阵奇异值特征向量的夹角大小度量两图像的结构相似度,实现了对降质图像的质量评价。实验结果表明:局部方差分布更能突出图像的结构特征,评价结果优于传统的均方误差(MSE)、峰值信噪比(PSNR)、结构相似度(SSIM)以及直接评价图像像素分布的奇异值分析(SVD)等方法,与人眼视觉感知效果的一致性较好。
- 像质评价 /
- 局部方差 /
- 奇异值分解 /
- 人类视觉系统
Abstract: The local variance distribution of a gray level image is taken as an important characteristic to express image structural information, and the Singular Value Decomposition(SVD) is performed on a local variance distribution matrix. The angle between the singular vectors of the reference image and distorted image is used to measure the structural similarity of the two images, and then the image quality assessment is achieved. Experimental result shows that the local variance distribution can emphasize the structural information. It is better consistent with human visual perception characteristics and the assessment results are superior to those from Mean Square Error(MSE), Peak Signal to Noise(PSNR), Structure Similarity(SSIM) and SVD methods based on pixel value distribution.
- image quality assessment /
- local variance /
- singular value decomposition /
- human visual system

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参考文献(1)

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