基于hvs的无参考图像质量.doc
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基于hvs的无参考图像质量,摘 要图像质量评价对图像处理起着重要的指导作用。在很难得到原始图像的情况下，图像的无参考评价方法受到了普遍的重视。图像为人服务的本质决定了它的评价结果必须与人视觉系统特征相吻合，因此，图像质量评价就是设计有效的算法以得出与人主观感知相一致的评价值。jpeg图像依然是当前网络和数据库中应用最为广泛和最受欢迎的图像格式之一...
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摘 要
图像质量评价对图像处理起着重要的指导作用。在很难得到原始图像的情况下，图像的无参考评价方法受到了普遍的重视。图像为人服务的本质决定了它的评价结果必须与人视觉系统特征相吻合，因此，图像质量评价就是设计有效的算法以得出与人主观感知相一致的评价值。JPEG图像依然是当前网络和数据库中应用最为广泛和最受欢迎的图像格式之一。据此，本论文对基于人视觉系统的无参考JPEG图像质量评价方法进行了较深入的研究，研究主要内容有：
1．在研究当前图像质量评价方法的基础上，重点探讨了无参考图像质量评价方法，指出了当前有影响力的无参考评价方法。
2．将人视觉系统特征归纳为掩盖效应和视觉敏感度两个特征。针对掩盖效应，在DCT域分别采用不同的数学模型提取纹理边沿掩盖效应和亮度掩盖效应特征，作为度量掩盖效应特征的指标。针对人视觉敏感度，则通过使用不同的滤波算子提取出最能反映人视觉敏感度的边沿幅度和长度、背景活动度和亮度这四个特征。实验结果显示，所采用的数学模型提取的特征均具有良好的区分度。
3．提出一种基于掩盖效应的无参考图像质量评价方法。采用了DCT离散检测块提取出最能反映块效应的掩盖效应值，通过Minkowski合成法合成一个可以反映掩盖效应的评价指标，实现评价图像质量的功能。实验对比显示，该评价指标能够较好的捕捉人视觉注意机制，较好的反映图像质量的平均主观评价值。
4．提出一种新的基于人视觉敏感度的无参考图像质量评价方法。采用支持向量回归神经网络寻找和逼近图像质量评价中人视觉敏感度特征与平均主观评价值之间的函数关系，利用边沿幅度和长度、背景活动度和亮度等视觉敏感度特征，实现符合人视觉特征的无参考图像质量评价功能。实验表明，支持向量回归神经网络的自主学习能力能够自动增添新样本的特征，具有优良的泛化能力和普适性，所得到的图像评价结果与平均主观评价值有较高的一致性，充分体现了人视觉特征在图像质量评价中的作用。
关键词 人视觉敏感度；无参考；图像质量评价；支持向量回归；神经网络
Abstract
Image quality assessment had played an important role in the image processing. The no-reference image quality eva luation method had gained a universal attation. Because the essence of image serving people determined that its assessment result must accord with human visual system character, the purpose of image quality assessment is derived to design an effective algorithm which is highly consistent with human sbjective eva luation value of visual perception.The most popular and widely used image format in the Internet as well as in digital cameras happens to be JPEG. Therefore, research object of this paper is NO-reference JPEG image quality assessment based human visual system. The main research results of this paper can be summarized as follows:
1. Summarizing the image quality assessment methods. The no-reference image quality measurement is stressed and then several important no-reference methods are introduced.
2. In this paper, texture edge masking and luminance masking characters are respectively extracted using several of mathematical models and then integrated into a masking map. Human visual sensitivity features such as edge amplitude, edge length, background activity and background luminance are extracted through several filtering operators. The experimental results show that the extracted features all have better discrimination.
3．The No-Reference image quality assessment metric based on masking is presented to predict JPEG image qulity. The masking values on the 8×8 block boundaries are extracted using DCT block discontinuity detection and then can be easily pooled with a Minkowski summation to generate the Mean noticeable Blockiness Score to eva luate image quality. The compared experimental results demonstrate that the metric is highly consistent with mean subjective score, well eva luating image quality.
4. The No-Reference image quality assessment metric based on human visual sensitivity is presented. The support vector regression naural network algorithm is used to search and approximate the functional relationship between human visual sensitivity features and mean subjective score. Then, the measuring of visual quality of JPEG-coded images was realized considering human visual sensitivity features such as edge amplitude, edge length, background activity and background luminance. Experimental results prove that its better generalization performance can add the new features of the sample automatically. Compared with other image quality metrics, the experimental results of the proposed metric exhibit much higher correlation with perception character of HVS. And the role of HVS feature in image quality index is fully reflected.
Keywords human visual sensitivity; NO-Reference; image quality assessment; support vector regression; naural network
目 录
摘 要 I
Abstract II
第1章 绪论 1
1.1研究背景与意义 1
1.2研究现状与发展趋势 2
1.2.1 图像质量评价的研究现状 2
1.2.2 图像质量评价的发展趋势 3
1.3 论文的研究内容及其主要成果 4
1.4 论文的组织结构 4
第2章 图像质量评价综述 7
2.1 引言 7
2.2 图像质量主观评价方法 7
2.3 图像质量客观评价方法 8
2.3.1 全参考图像质量评价方法 8
2.3.1.1 基于误差统..
图像质量评价对图像处理起着重要的指导作用。在很难得到原始图像的情况下，图像的无参考评价方法受到了普遍的重视。图像为人服务的本质决定了它的评价结果必须与人视觉系统特征相吻合，因此，图像质量评价就是设计有效的算法以得出与人主观感知相一致的评价值。JPEG图像依然是当前网络和数据库中应用最为广泛和最受欢迎的图像格式之一。据此，本论文对基于人视觉系统的无参考JPEG图像质量评价方法进行了较深入的研究，研究主要内容有：
1．在研究当前图像质量评价方法的基础上，重点探讨了无参考图像质量评价方法，指出了当前有影响力的无参考评价方法。
2．将人视觉系统特征归纳为掩盖效应和视觉敏感度两个特征。针对掩盖效应，在DCT域分别采用不同的数学模型提取纹理边沿掩盖效应和亮度掩盖效应特征，作为度量掩盖效应特征的指标。针对人视觉敏感度，则通过使用不同的滤波算子提取出最能反映人视觉敏感度的边沿幅度和长度、背景活动度和亮度这四个特征。实验结果显示，所采用的数学模型提取的特征均具有良好的区分度。
3．提出一种基于掩盖效应的无参考图像质量评价方法。采用了DCT离散检测块提取出最能反映块效应的掩盖效应值，通过Minkowski合成法合成一个可以反映掩盖效应的评价指标，实现评价图像质量的功能。实验对比显示，该评价指标能够较好的捕捉人视觉注意机制，较好的反映图像质量的平均主观评价值。
4．提出一种新的基于人视觉敏感度的无参考图像质量评价方法。采用支持向量回归神经网络寻找和逼近图像质量评价中人视觉敏感度特征与平均主观评价值之间的函数关系，利用边沿幅度和长度、背景活动度和亮度等视觉敏感度特征，实现符合人视觉特征的无参考图像质量评价功能。实验表明，支持向量回归神经网络的自主学习能力能够自动增添新样本的特征，具有优良的泛化能力和普适性，所得到的图像评价结果与平均主观评价值有较高的一致性，充分体现了人视觉特征在图像质量评价中的作用。
关键词 人视觉敏感度；无参考；图像质量评价；支持向量回归；神经网络
Abstract
Image quality assessment had played an important role in the image processing. The no-reference image quality eva luation method had gained a universal attation. Because the essence of image serving people determined that its assessment result must accord with human visual system character, the purpose of image quality assessment is derived to design an effective algorithm which is highly consistent with human sbjective eva luation value of visual perception.The most popular and widely used image format in the Internet as well as in digital cameras happens to be JPEG. Therefore, research object of this paper is NO-reference JPEG image quality assessment based human visual system. The main research results of this paper can be summarized as follows:
1. Summarizing the image quality assessment methods. The no-reference image quality measurement is stressed and then several important no-reference methods are introduced.
2. In this paper, texture edge masking and luminance masking characters are respectively extracted using several of mathematical models and then integrated into a masking map. Human visual sensitivity features such as edge amplitude, edge length, background activity and background luminance are extracted through several filtering operators. The experimental results show that the extracted features all have better discrimination.
3．The No-Reference image quality assessment metric based on masking is presented to predict JPEG image qulity. The masking values on the 8×8 block boundaries are extracted using DCT block discontinuity detection and then can be easily pooled with a Minkowski summation to generate the Mean noticeable Blockiness Score to eva luate image quality. The compared experimental results demonstrate that the metric is highly consistent with mean subjective score, well eva luating image quality.
4. The No-Reference image quality assessment metric based on human visual sensitivity is presented. The support vector regression naural network algorithm is used to search and approximate the functional relationship between human visual sensitivity features and mean subjective score. Then, the measuring of visual quality of JPEG-coded images was realized considering human visual sensitivity features such as edge amplitude, edge length, background activity and background luminance. Experimental results prove that its better generalization performance can add the new features of the sample automatically. Compared with other image quality metrics, the experimental results of the proposed metric exhibit much higher correlation with perception character of HVS. And the role of HVS feature in image quality index is fully reflected.
Keywords human visual sensitivity; NO-Reference; image quality assessment; support vector regression; naural network
目 录
摘 要 I
Abstract II
第1章 绪论 1
1.1研究背景与意义 1
1.2研究现状与发展趋势 2
1.2.1 图像质量评价的研究现状 2
1.2.2 图像质量评价的发展趋势 3
1.3 论文的研究内容及其主要成果 4
1.4 论文的组织结构 4
第2章 图像质量评价综述 7
2.1 引言 7
2.2 图像质量主观评价方法 7
2.3 图像质量客观评价方法 8
2.3.1 全参考图像质量评价方法 8
2.3.1.1 基于误差统..
