基于自然计算和视觉注意的图像质量评价
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摘要
近年来,数字图像的科学理论和技术手段迅猛发展,在通信、工业、医学、遥感、军事等领域得到了广泛的应用,极大地促进了人类科学研究的发展、社会生产率的提高和生活方式的改善。然而,在图像的获取、处理、传输和记录的过程中,由于成像系统、处理方法、传输介质和记录设备的不完善等原因,不可避免地带来某种程度的图像失真或降质,这给人们研究解决问题、认识客观世界带来很大的不便。因此,对图像质量客观、有效、合理的评价具有非常重要的意义。
人类视觉系统作为视觉信息的最终受体,对图像质量的感知是最可靠的。如果在图像质量评价算法中考虑视觉系统的感知特性,定将提高算法的准确性。因此基于人类视觉系统的一些感知特性和信息处理机制本文做了如下研究:1)结合视觉系统的多通道和对比度敏感特性,提出了一种基于自然计算Wavelet域尺度因子优选的全参考型图像质量评价方法,该算法利用小波分解模拟视觉系统的多通道特性,利用自然计算优化各个尺度的对比度敏感因子,实验结果表明该算法与主观评价有较高的一致性。2)根据人眼对结构信息的敏感性,提出了一种SSIM的改进算法,该算法区别对待图像的形成因子:亮度、对比度和结构,利用自然计算优化结构因子的敏感系数,从而突出结构信息的重要性。3)利用视觉注意机制选择性地获取图像中所关注的区域,提出了一种基于视觉注意区域综合感知误差的全参考型图像质量评价方法,该方法区别对待图像中显著性不同的区域,可作为一种加权方式优化现有的基于像素域综合感知误差的质量评价算法。4)统计原始和失真图像之间亮度特征、颜色特征和方向特征对应的差异,并利用自然计算获得不同失真类型对不同特征的敏感性因子,提出了一种基于视觉注意特征的部分参考型图像质量评价方法。实验结果表明,该算法的客观评价结果与主观评价结果之间具有较好的一致性,能准确地反映人眼对图像质量的主观感受。
In recent years, the scientific theories and technical means of digital images have developed rapidly, and have been widely applied to such fields as communications, industry, medicine, remote sensing, military, etc. They promote the development of human scientific research, the gains of social productivity and the improvement of life style greatly. However, during the process of acquisition, processing, transmission and recording, images will inevitably suffer from distortion or degradation to some extent because of the imperfections of imaging systems, processing methods, transmission media, and recording equipments, which brings great inconvenience in studying and solving problems, and understanding the objective world. Consequently, it is of great significance to evaluate the quality of images objectively, effectively and reasonably.
As the terminal for receiving visual information, the human visual system is the most reliable instrument to assess the quality of images. It will improve the performance of evaluating algorithms definitely if we take the characters of the human visual perception into account. So based on the perceptual features and information processing mechanism of the human visual system, some methods have been given in this paper: 1) Combining the perceptual characteristics of visual system, including multi-channel and contrast sensitivity, a natural computation based full reference image quality evaluation method in wavelet domain is proposed, which uses the wavelet decomposition to simulate the multi-channel characteristic of human visual system and utilizes natural computation to obtain the scale factor(i.e. the contrast sensitivity factor of each subband). Experimental results show that the proposed method has better consistency with the subjective mean opinion score (MOS). 2) According to that the human eyes are more sensitive to structural information, an improved version of structural similarity (SSIM) algorithm is presented, which differently treats the images’forming factors, including luminance, contrast and structure, and captures the sensitivity coefficient of the structural factor by natural computation to highlight the importance of structural information. 3) Using visual attention mechanism to acquire the attentive regions of an image selectively, a type of attentive regions based pooling perceptual error image quality evaluation method is developed, which differently handles the image regions with different saliency. It can be used as a weighting form to optimize the existing image quality evaluation algorithms which pool perceptual error in pixel domain. 4) A reduced reference image quality assessment metric based on visual attentive features is proposed, which computes the differences of intensity, color and orientation between the original and the distorted images respectively, and furthermore obtains the sensitivity factors of the three features(i.e. intensity, color and orientation) corresponding to different distortion types with natural computation. Experimental results demonstrate that the proposed method has better consistency with the subjective MOS, which can accurately reflect the human eyes’subjective feelings for the image quality.
人类视觉系统作为视觉信息的最终受体,对图像质量的感知是最可靠的。如果在图像质量评价算法中考虑视觉系统的感知特性,定将提高算法的准确性。因此基于人类视觉系统的一些感知特性和信息处理机制本文做了如下研究:1)结合视觉系统的多通道和对比度敏感特性,提出了一种基于自然计算Wavelet域尺度因子优选的全参考型图像质量评价方法,该算法利用小波分解模拟视觉系统的多通道特性,利用自然计算优化各个尺度的对比度敏感因子,实验结果表明该算法与主观评价有较高的一致性。2)根据人眼对结构信息的敏感性,提出了一种SSIM的改进算法,该算法区别对待图像的形成因子:亮度、对比度和结构,利用自然计算优化结构因子的敏感系数,从而突出结构信息的重要性。3)利用视觉注意机制选择性地获取图像中所关注的区域,提出了一种基于视觉注意区域综合感知误差的全参考型图像质量评价方法,该方法区别对待图像中显著性不同的区域,可作为一种加权方式优化现有的基于像素域综合感知误差的质量评价算法。4)统计原始和失真图像之间亮度特征、颜色特征和方向特征对应的差异,并利用自然计算获得不同失真类型对不同特征的敏感性因子,提出了一种基于视觉注意特征的部分参考型图像质量评价方法。实验结果表明,该算法的客观评价结果与主观评价结果之间具有较好的一致性,能准确地反映人眼对图像质量的主观感受。
In recent years, the scientific theories and technical means of digital images have developed rapidly, and have been widely applied to such fields as communications, industry, medicine, remote sensing, military, etc. They promote the development of human scientific research, the gains of social productivity and the improvement of life style greatly. However, during the process of acquisition, processing, transmission and recording, images will inevitably suffer from distortion or degradation to some extent because of the imperfections of imaging systems, processing methods, transmission media, and recording equipments, which brings great inconvenience in studying and solving problems, and understanding the objective world. Consequently, it is of great significance to evaluate the quality of images objectively, effectively and reasonably.
As the terminal for receiving visual information, the human visual system is the most reliable instrument to assess the quality of images. It will improve the performance of evaluating algorithms definitely if we take the characters of the human visual perception into account. So based on the perceptual features and information processing mechanism of the human visual system, some methods have been given in this paper: 1) Combining the perceptual characteristics of visual system, including multi-channel and contrast sensitivity, a natural computation based full reference image quality evaluation method in wavelet domain is proposed, which uses the wavelet decomposition to simulate the multi-channel characteristic of human visual system and utilizes natural computation to obtain the scale factor(i.e. the contrast sensitivity factor of each subband). Experimental results show that the proposed method has better consistency with the subjective mean opinion score (MOS). 2) According to that the human eyes are more sensitive to structural information, an improved version of structural similarity (SSIM) algorithm is presented, which differently treats the images’forming factors, including luminance, contrast and structure, and captures the sensitivity coefficient of the structural factor by natural computation to highlight the importance of structural information. 3) Using visual attention mechanism to acquire the attentive regions of an image selectively, a type of attentive regions based pooling perceptual error image quality evaluation method is developed, which differently handles the image regions with different saliency. It can be used as a weighting form to optimize the existing image quality evaluation algorithms which pool perceptual error in pixel domain. 4) A reduced reference image quality assessment metric based on visual attentive features is proposed, which computes the differences of intensity, color and orientation between the original and the distorted images respectively, and furthermore obtains the sensitivity factors of the three features(i.e. intensity, color and orientation) corresponding to different distortion types with natural computation. Experimental results demonstrate that the proposed method has better consistency with the subjective MOS, which can accurately reflect the human eyes’subjective feelings for the image quality.
引文
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