基于均匀色差空间扩展的彩色图像质量评价研究
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摘要
现代社会中,伴随着图像信息在数字电视、会议电视和可视电话等数字图像处理系统领域的广泛应用,它已进入千家万户的日常生活。由于在图像的处理和存储等不同步骤中,因为处理方式和存储设备等技术方面的不完善,导致了某些图像的失真和降质。为了使得人们正确认知客观世界,图像质量评价方法的研究越来越受到研究者的重视。目前图像质量评价主要研究的都是客观图像质量评价方法,目的是使得客观评价结果和主观视觉感受相吻合,重点是降低主、客观评价结果的误差。由于彩色视觉系统较黑白系统更为复杂,从而增加了彩色图像质量评价模型建立的难度,因此目前对彩色图像质量评价模型的研究是一个富有挑.战性的课题。
目前,彩色图像质量评价方法主要是从显像三基色空间映射到三要素空间或者均匀色差空间,再类比黑白图像的评价方法处理。但是基于三要素(亮度、色调和饱和度)空间的方法缺乏视觉均匀色差思想的支持,而基于均匀色差空间的方法又需要其他有效评价模型的补充。为此,本论文从均匀色差模型着手,基于CIEDE2000色差公式和NBS单位分级方法建立均匀色差空间模型,进而将均匀色差空间模型与彩色空间结构相似分解模型、HVS空间中视觉非线性、多通道分解和视觉掩盖模型分别两两结合,最后将几个模型综合起来提出了基于均匀色差空间扩展的综合彩色图像质量评价方法。该方法覆盖了视觉感受基本实验的物理特征及其对应数学模型,可以有效地解释单独模型评价的不确定性,有利于提高评价模型的有效性和稳定性,获取与主观视觉感受进一步吻合的彩色图像质量客观评价方法。
具体研究内容和创新点如下：
1.提出了均匀色差空间模型。该模型将均匀色差计算出的客观评价结果与图像质量主观评分标准直接对应,其实质是从亮度、色调和饱和度角度对韦伯一费赫涅尔定律进行了补充。与现有评价方法比较,该模型提高了彩色三要素与视觉感受间线性化程度。另外,为了考察彩色图像的亮度、色调和饱和度对主观视觉感受的影响,利用CIEDE2000色差公式与人眼主观感受较为吻合的特点,设计了亮度、色调和饱和度测试图像信号。
2.提出了彩色空间结构相似分解模型和均匀色差空间结构相似分解模型。彩色空间结构相似分解模型通过利用结构化评价方法中结构因子的思想,将误差图像分解为均值分量、交变相似分量和交变无关分量。其实质是从形状上对HVS模型的扩展和修正。均匀色差空间结构相似分解模型将彩色空间结构相似分解模型与均匀色差模型相结合,考察了在均匀色差空间中结构相似分解模型对主观视觉的影响。其实质对传统图像质量评价方法从结构和彩色方面进行了补充。与现有评价方法比较,对于包含均值分量和交变相似分量的彩色图像,均匀色差空间结构相似分解模型进一步提高了与主观视觉感受的吻合度。
3.提出了DCT高斯量化多通道分解模型和均匀色差空间DCT高斯量化多通道分解模型。DCT高斯量化多通道分解模型考察了物体空间尺寸对主观视觉的影响。它主要特点是概念清楚,计算简便。均匀色差空间DCT高斯量化多通道分解模型是将多通道分解空间模型与均匀色差模型相结合,全面考察了物体空间尺寸在均匀色差空间中的主观视觉感受。与现有评价方法比较,对于具有图像空间尺寸变化的彩色图像,均匀色差空间DCT高斯量化多通道分解模型进一步提高了与主观视觉感受的吻合度。
4.提出了彩色空间综合视觉掩盖模型和均匀色差空间视觉掩盖模型。彩色空间综合视觉掩盖模型反映了“一个激励的存在,将导致另一激励视觉阈值的改变”的视觉感受现象。该模型将亮度掩盖、对比度掩盖和纹理掩盖相结合,全面的考察了图像质量。均匀色差空间视觉掩盖模型将彩色空间综合视觉掩盖模型与均匀色差模型相结合,考察了在均匀色差空间中平均幅度、平均变化幅度、平均变化频率对主观视觉的影响。与现有评价方法比较,对于会产生掩盖效应的彩色图像,均匀色差空间视觉掩盖模型进一步提高了与主观视觉感受的吻合度。
5.提出了均匀色差空间综合模型。该模型将均匀色差空间模型、彩色空间结构相似分解模型、HVS空间模型(非线性视觉感知、多通道分解、视觉掩盖),三者有机地结合,扩大了评价模型的适用范围。与论文前面建立的模型比较,均匀色差空间综合模型更为有效、稳定和全面的评价彩色图像质量,获取与视觉感受进一步吻合的评价效果。
In modern society, image information has entered our household's daily life such as in digital image processing system of digital TV, video conference and video phone. Due to the faultiness of imaging system, processing method, transmission media and storage equipment, image will be inevitably distorted and deformed. Thus image quality assessment becomes more and more important and attracts more and more researchers. Presently the main research direction in this field is objective image quality assessment. The goal is to make the objective quality assessment model accurately reflect the subjective quality of human vision. The focus is to reduce deviation between the subjective and the objective quality assessment results. Color vision system is more complex compared with monochrome image, thus it is more involved to establish an objective color image quality assessment model. For this challenging research topic, every new result can be regarded as an important progress and innovation of image quality assessment theory.
Normally, color image quality assessment normally maps three primary colors into three elements space or uniform color difference space and then uses monochrome image quality assessment to process respectively. However, this kind of methods based on the three elements space lacks the support of uniform color difference space. Similarly, the methods based on the uniform color difference space need complement of other effective evaluation model. In this dissertation, we proposed an objective color image quality assessment based on extended uniform color difference space by combining uniform color difference space model, color space structure similarity decomposition model and HVS (Human Visual System) model. This method covers the physical characteristic and the corresponding mathematical model of basic visual experiment. In addition, it can explain the uncertainty of independent assessment model result and improve the effectiveness and stability of assessment model. The content and innovation are listed as following:
1. In contrast with previous methods lacking direct comparability with human subjective feeling, this dissertation firstly presents a uniform color space model which can achieve a linear relationship between changes of brightness, hue, saturation of subjective perception. The essence of uniform color difference space model is an expansion of the brightness, hue and saturation point for Web-Fechner law. In order to investigate the subjective perception of luminance, hue and saturation, this dissertation also designs a set of test image signals of brightness, hue and saturation utilizing the consistent characteristic of CIEDE2000color difference formula and the subjective perception.
2. As visual structure similarity component in color error image can cause uncertain phenomenon in normal color image evaluation model, this dissertation presents a color space structure similarity decomposition model by extending the HVS model in shapes. This uniform color difference space structure similarity decomposition model combines color space structure similarity decomposition model and uniform color difference model so that it can be used to investigate the subjective perception of structure similarity decomposition model in uniform color difference space. This method can be regarded as a extension of traditional image quality assessment methods from the aspects of structure and color.
3. HVS's multichannel decomposition model mainly investigates the influence of object space size on the subjective vision. This dissertation presents a convenient multichannel decomposition model of DCT gauss quantization model. This method can be used to investigate the subjective perception of object space size in uniform color difference space by combining multichannel decomposition model of DCT gauss quantization model and uniform color difference model.
4. HVS's visual masking space model reflects visual perception phenomenon of "existing an incentive can lead to change of another incentive threshold". Different from the previous masking models mainly focus on gray image, this dissertation presents a color space comprehensive visual masking model. In addition, a uniform color difference space visual masking model is proposed by combining color space comprehensive visual masking model and uniform color difference model. Therefore, the subjective perception of visual masking model in uniform color difference space can be investigated.
5. This dissertation proposes a uniform color difference space extension model which can assess color image quality more effective, stable and comprehensive by combing uniform color difference model, color space structure similarity decomposition model and HVS mode. This method can be applied in various conditions and can make assessment results more coincide with subjective perception.
目前,彩色图像质量评价方法主要是从显像三基色空间映射到三要素空间或者均匀色差空间,再类比黑白图像的评价方法处理。但是基于三要素(亮度、色调和饱和度)空间的方法缺乏视觉均匀色差思想的支持,而基于均匀色差空间的方法又需要其他有效评价模型的补充。为此,本论文从均匀色差模型着手,基于CIEDE2000色差公式和NBS单位分级方法建立均匀色差空间模型,进而将均匀色差空间模型与彩色空间结构相似分解模型、HVS空间中视觉非线性、多通道分解和视觉掩盖模型分别两两结合,最后将几个模型综合起来提出了基于均匀色差空间扩展的综合彩色图像质量评价方法。该方法覆盖了视觉感受基本实验的物理特征及其对应数学模型,可以有效地解释单独模型评价的不确定性,有利于提高评价模型的有效性和稳定性,获取与主观视觉感受进一步吻合的彩色图像质量客观评价方法。
具体研究内容和创新点如下：
1.提出了均匀色差空间模型。该模型将均匀色差计算出的客观评价结果与图像质量主观评分标准直接对应,其实质是从亮度、色调和饱和度角度对韦伯一费赫涅尔定律进行了补充。与现有评价方法比较,该模型提高了彩色三要素与视觉感受间线性化程度。另外,为了考察彩色图像的亮度、色调和饱和度对主观视觉感受的影响,利用CIEDE2000色差公式与人眼主观感受较为吻合的特点,设计了亮度、色调和饱和度测试图像信号。
2.提出了彩色空间结构相似分解模型和均匀色差空间结构相似分解模型。彩色空间结构相似分解模型通过利用结构化评价方法中结构因子的思想,将误差图像分解为均值分量、交变相似分量和交变无关分量。其实质是从形状上对HVS模型的扩展和修正。均匀色差空间结构相似分解模型将彩色空间结构相似分解模型与均匀色差模型相结合,考察了在均匀色差空间中结构相似分解模型对主观视觉的影响。其实质对传统图像质量评价方法从结构和彩色方面进行了补充。与现有评价方法比较,对于包含均值分量和交变相似分量的彩色图像,均匀色差空间结构相似分解模型进一步提高了与主观视觉感受的吻合度。
3.提出了DCT高斯量化多通道分解模型和均匀色差空间DCT高斯量化多通道分解模型。DCT高斯量化多通道分解模型考察了物体空间尺寸对主观视觉的影响。它主要特点是概念清楚,计算简便。均匀色差空间DCT高斯量化多通道分解模型是将多通道分解空间模型与均匀色差模型相结合,全面考察了物体空间尺寸在均匀色差空间中的主观视觉感受。与现有评价方法比较,对于具有图像空间尺寸变化的彩色图像,均匀色差空间DCT高斯量化多通道分解模型进一步提高了与主观视觉感受的吻合度。
4.提出了彩色空间综合视觉掩盖模型和均匀色差空间视觉掩盖模型。彩色空间综合视觉掩盖模型反映了“一个激励的存在,将导致另一激励视觉阈值的改变”的视觉感受现象。该模型将亮度掩盖、对比度掩盖和纹理掩盖相结合,全面的考察了图像质量。均匀色差空间视觉掩盖模型将彩色空间综合视觉掩盖模型与均匀色差模型相结合,考察了在均匀色差空间中平均幅度、平均变化幅度、平均变化频率对主观视觉的影响。与现有评价方法比较,对于会产生掩盖效应的彩色图像,均匀色差空间视觉掩盖模型进一步提高了与主观视觉感受的吻合度。
5.提出了均匀色差空间综合模型。该模型将均匀色差空间模型、彩色空间结构相似分解模型、HVS空间模型(非线性视觉感知、多通道分解、视觉掩盖),三者有机地结合,扩大了评价模型的适用范围。与论文前面建立的模型比较,均匀色差空间综合模型更为有效、稳定和全面的评价彩色图像质量,获取与视觉感受进一步吻合的评价效果。
In modern society, image information has entered our household's daily life such as in digital image processing system of digital TV, video conference and video phone. Due to the faultiness of imaging system, processing method, transmission media and storage equipment, image will be inevitably distorted and deformed. Thus image quality assessment becomes more and more important and attracts more and more researchers. Presently the main research direction in this field is objective image quality assessment. The goal is to make the objective quality assessment model accurately reflect the subjective quality of human vision. The focus is to reduce deviation between the subjective and the objective quality assessment results. Color vision system is more complex compared with monochrome image, thus it is more involved to establish an objective color image quality assessment model. For this challenging research topic, every new result can be regarded as an important progress and innovation of image quality assessment theory.
Normally, color image quality assessment normally maps three primary colors into three elements space or uniform color difference space and then uses monochrome image quality assessment to process respectively. However, this kind of methods based on the three elements space lacks the support of uniform color difference space. Similarly, the methods based on the uniform color difference space need complement of other effective evaluation model. In this dissertation, we proposed an objective color image quality assessment based on extended uniform color difference space by combining uniform color difference space model, color space structure similarity decomposition model and HVS (Human Visual System) model. This method covers the physical characteristic and the corresponding mathematical model of basic visual experiment. In addition, it can explain the uncertainty of independent assessment model result and improve the effectiveness and stability of assessment model. The content and innovation are listed as following:
1. In contrast with previous methods lacking direct comparability with human subjective feeling, this dissertation firstly presents a uniform color space model which can achieve a linear relationship between changes of brightness, hue, saturation of subjective perception. The essence of uniform color difference space model is an expansion of the brightness, hue and saturation point for Web-Fechner law. In order to investigate the subjective perception of luminance, hue and saturation, this dissertation also designs a set of test image signals of brightness, hue and saturation utilizing the consistent characteristic of CIEDE2000color difference formula and the subjective perception.
2. As visual structure similarity component in color error image can cause uncertain phenomenon in normal color image evaluation model, this dissertation presents a color space structure similarity decomposition model by extending the HVS model in shapes. This uniform color difference space structure similarity decomposition model combines color space structure similarity decomposition model and uniform color difference model so that it can be used to investigate the subjective perception of structure similarity decomposition model in uniform color difference space. This method can be regarded as a extension of traditional image quality assessment methods from the aspects of structure and color.
3. HVS's multichannel decomposition model mainly investigates the influence of object space size on the subjective vision. This dissertation presents a convenient multichannel decomposition model of DCT gauss quantization model. This method can be used to investigate the subjective perception of object space size in uniform color difference space by combining multichannel decomposition model of DCT gauss quantization model and uniform color difference model.
4. HVS's visual masking space model reflects visual perception phenomenon of "existing an incentive can lead to change of another incentive threshold". Different from the previous masking models mainly focus on gray image, this dissertation presents a color space comprehensive visual masking model. In addition, a uniform color difference space visual masking model is proposed by combining color space comprehensive visual masking model and uniform color difference model. Therefore, the subjective perception of visual masking model in uniform color difference space can be investigated.
5. This dissertation proposes a uniform color difference space extension model which can assess color image quality more effective, stable and comprehensive by combing uniform color difference model, color space structure similarity decomposition model and HVS mode. This method can be applied in various conditions and can make assessment results more coincide with subjective perception.
引文
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