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题名	基于局部视觉特征的图像质量客观评价方法研究
作者	卢彦飞
学位类别	博士
答辩日期	2015-05
授予单位	中国科学院大学
导师	张涛
关键词	人类视觉系统 局部信息失真 Riesz变换 韦伯定律 差异激励 视觉显著性 单演相位一致性
其他题名	Research on Objective Image Quality Assessment Methods Based on Local Visual Features
学位专业	光学工程
中文摘要	图像信息是人类认识外部世界的主要途径之一。在图像数据的获取、传输、压缩和处理等过程中，会引入各种各样的失真，从而导致图像退化，使图像的视觉效果下降。图像质量评价方法可以为成像系统的参数设计和算法优化提供参考，因此，如何合理的评价图像质量正在成为图像处理领域的一个研究热点。本文围绕图像失真信息的表示和提取，通过考虑人类视觉系统的特性，探索利用局部视觉特征来对失真信息进行建模，重点研究了全参考图像质量评价方法和无参考模糊图像质量评价方法，并将图像质量评价方法应用在医学图像融合领域。本文的主要工作和创新成果如下： 针对传统的结构相似度图像质量评价方法(Structural SIMilarity, SSIM) 对噪声图像和严重模糊图像评价不够准确，不够符合人眼主观感受的问题，提出了一种基于局部信息失真建模的图像质量评价方法。该方法将图像失真分为三种：像素灰度失真、局部对比度失真和局部结构失真，并利用像素的灰度值而不是邻域均值来对像素灰度失真进行建模，利用局部二值模式对局部对比度失真进行建模，利用局部方差对局部结构失真进行建模。该方法计算比较简单，而且在LIVE图像库中5种失真类型上的表现很好，具有很好的评价性能。此外，针对SSIM方法中结构信息的度量指标过于简单的问题，提出了一种基于Riesz变换的结构相似度图像质量评价方法。该方法根据Riesz变换能够很好的表达图像的局部结构信息的特点，利用Riesz变换提取的图像一阶和二阶特征图，对结构信息的度量指标进行重新建模，得到了改进的结构相似度图像质量评价方法。该方法与SSIM等方法相比，具有更好的评价性能。 人眼对亮度的主观感知是符合韦伯定律的，即能感受到的刺激改变量和原始刺激成正比关系。利用图像的差异激励图来反映韦伯定律，利用视觉显著性图来描述视觉注意机制，并考虑了观察条件对人眼判断结果的影响，提出了一种基于人眼视觉特性的图像质量评价方法。此外，根据差异激励图计算过程中没有考虑对比度信息的情况，利用图像的梯度来反映对比度信息，在此基础上提出了一种基于差异激励相似度的图像质量评价方法。最后，提出了一种log-Gabor韦伯特征，该特征能较好的提取图像局部信息，在此基础上提出了一种能够同时用于评价彩色和灰度图像的质量评价方法。以上算法在LIVE、CSIQ等主要图像库上均有很好的表现。 针对现有的无参考模糊图像质量评价方法计算复杂度较高的问题，提出了一种基于局部标准差和显著性图的模糊图像质量评价指标。首先利用图像的再模糊效应对待评价图像进行高斯低通滤波，得到参考副本。再根据图像的两个简单特征，即局部标准差图和显著性图在再模糊前后的变化程度来对待评价图像的模糊程度进行衡量。实验结果表明，该算法和人眼的主观感受具有很好的一致性，与当前较好的无参考模糊图像质量评价指标相比，具有相近的评价性能，而计算复杂度大大降低。 将不同模态的医学图像进行融合是现在的一个研究热点。针对现有的融合图像质量评价指标不能很好的反映人眼主观感受的情况，提出了一种基于单演特征的医学图像融合质量评价方法。该方法利用单演信号能够充分表达图像局部信息的优点，得到了一种新的相位一致性，即单演相位一致性，并提出了一种单演特征相似度图像质量评价方法，该方法考虑了显著性边缘信息和单演相位一致性信息。然后利用原始图像的单演相位一致性图像作为权重因子模拟人眼对于不同区域的重视程度，得到了加权单演特征相似度。最后利用融合前图像的信息熵和边缘能量进行权重分配，得到了融合图像的质量评价因子。与其他方法的对比结果表明，该方法的评价结果更加符合人眼的主观感受。
英文摘要	Image information is one of the main routes for human beings to understand the outside world. During the process of image data acquisition, transmission, compression and processing, various distortions will be introduced to result in image degradation, and decrease the visual effect of the image. Image quality assessment method can help to design the parameters of imaging system and to optimize the algorithms, and thus how to evaluate the image quality reasonably is becoming one of the hot topics in the field of image processing. This dissertation focuses on representation and extraction of features of image distortion, and explores to use local visual features to model the distortion information while considering the characteristics of the human visual system. It mainly studies the full-reference image quality assessment method and no-reference blur image quality assessment method and applies the image quality assessment method to medical image fusion. The main work and innovations are as follows: A new image quality assessment method based on local information distortion modeling is proposed in this dissertation as traditional structural similarity image quality metric cannot evaluate noisy image and severely blurred image accurately enough and does not correlate well with human subjective perception. This method separates image distortion into three types, which are pixel grayscale distortion, local contrast distortion and local structure distortion. The gray level instead of neighborhood average of pixel is used to model the grayscale distortion, the local binary pattern is used to model the local contrast distortion and the local variance is employed to model the local structure distortion. This method owns a simple calculation and behaves well on 5 types of distortions in LIVE database, indicating a good evaluation performance. In addition, since the structure information term of SSIM method is too simple, a Riesz transform based structural similarity image quality method is proposed. According to the fact that local structure information can be well represented via Reisz transformation, this method uses Riesz transform to extract the first-order and second-order image features to rebuild the structure information term to obtain the improved structural similarity image quality metric. Compared with SSIM and other methods, this metric has a better evaluation performance. The subjective perception of luminance for human eyes obeys the Weber’s law, which says that the noticeable stimulus change is proportional to the original stimulus. An image quality assessment method based on the human visual characteristics is proposed, in which the differential excitation map of image is used to model the Weber’s law, the visual saliency map is used to model the visual attention strategy, and the impact factors to human judgment with different viewing conditions. Further, since the calculation of differential excitation does not cater the contrast information, the image gradient is used as a complementary feature. A differential excitation similarity based image quality assessment metric is proposed based on the two features. At last, a log-Gabor Weber features is presented to better extract the local information of image, and it is used to design an image quality assessment method that can evaluate both the color and grayscale images. The proposed algorithms above have very good performance on several benchmark image databases such as LIVE and CSIQ. A blur image quality metric based on local standard deviation and saliency map is proposed due to the high computational complexity of existing no-reference blur image quality assessment method. Firstly, the reblur effect of image is used to construct a reference image with a Gaussian low-pass filter. Then the two simple features namely the local standard deviation map and saliency map are used to evaluate the blur image quality according to how much they change after reblurring. The experimental results show that the proposed algorithm correlates well with human subjective perception and it owns a close evaluation performance and much lower computational complexity compared with the state-of-the-art no-reference blur image quality assessment method. Fusion of multi-modality medical image is a hot topic. As existing image fusion quality assessment indices are not well consistent with human subjective perception, a medical image fusion quality assessment method based on monogenic features is proposed. The proposed algorithm takes advantage of the monogenic signal which can well represent the local information of image to produce a new kind of phase congruency namely monogenic phase congruency. Then a monogenic feature similarity based image quality assessment method is presented which takes into account the salient edge information and monogenic phase congruency information. The monogenic phase congruency map of original image is used as weighting factor to simulate the different importance of different regions for human eyes, so as to get the weighted monogenic feature similarity. Finally, entropy and edge energy of the original image are used as the importance factor to obtain the quality evaluation index of fused image. Compared with other methods, the proposed method is more consistent with the subjective human visual perception.
公开日期	2015-12-24
内容类型	学位论文
源URL	[http://ir.ciomp.ac.cn/handle/181722/48878]
专题	长春光学精密机械与物理研究所_中科院长春光机所知识产出
推荐引用方式 GB/T 7714	卢彦飞. 基于局部视觉特征的图像质量客观评价方法研究[D]. 中国科学院大学. 2015.

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