高动态范围图像显示再现技术的研究
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摘要
高动态范围(High Dynamic Range)图像也称为场景相关的图像,即图像的各个像素记录了场景真实亮度关系。通常HDR图像的最大亮度和最小亮度的比值超过1000:1。它可以直接记录亮度从10-6cd/m2到108cd/m2的场景。普通低动态范围图像也称为设备相关的图像,它只记录阶调相对关系,不记录场景的具体亮度,显示的效果由具体的显示媒介决定,显示动态范围通常只有100：1。现在一般商用的显示设备(或显示媒介)的动态范围比较低,例如,CRT显示器的最大显示亮度大约为80 cd/m2,实际的动态范围通常都不高于100：1；纸张所能显示的动态范围更低,如亚光纸张动态范围大约为50：1,而非亚光的纸张的动态范围只有30：1上下。显然低动态范围图像在这些显示媒介上显示时,对原先记录的场景的改变并不是很大,但HDR图像显示在这些显示媒介上时,动态范围将被大量压缩,场景的亮度会发生较大的改变。此动态范围的压缩和亮度的变化可能会使表现真实场景的纹理细节和颜色都发生很大变化,HDR图像所记录的高动态范围场景的真实光影效果也就不复存在。这一显示问题会严重影响影视动画、远程医疗、军事、印刷等领域对HDR图像的应用。
本论文围绕HDR图像在低动态范围显示媒介上显示的问题进行研究,以保证人眼对参考图像的感知的一致性。论文提出非线性色度适应性算法、积分-微分双空间的图像视觉计算模型、基于颜色视觉过程HDR图像映射算法、基于多尺度边缘机相阻制(Multi-Scale Center-Surround Lateral Inhibition Mechanism, MSCSLIM)的HDR图像映射算法,以及基于对比度和空间相关的边缘相阻机制(Spatial and Contrast Related Lateral Inhibition Mechanism, SCRLIM)的HDR图像映射算法。
要解决HDR图像在低动态范围显示媒介上的显示问题,通过物理(仅仅指光)匹配方式显然不能保持原有的HDR图像中的场景,必须根据人眼视觉对HDR图像的感知特性,从视觉的角度进行映射处理,保持原有场景关键特征的视觉感知。对于静态场景来说,视觉感知的最关键特征是颜色和纹理细节。
根据对颜色视觉的研究发现,人眼存在两种视觉机制可以产生颜色或亮度在视觉上的恒常性。第一种为消色机制,即感光(或称为光感应)细胞的感光色素的漂白机制,这一机制使得即使显示的光源发生了变化,视觉感知的颜色还是可以保持不变。在此基础上本论文针对光源导致颜色变化的问题提出了非线性的色度适应性算法。第二种视觉机制是中心-周边感知结构细胞的空间作用机制。此机制中的边缘相阻效应同样使得各颜色在周边颜色的影响下可以产生颜色的恒常性。此颜色的恒常机制描述了颜色的空间影响关系,适合图像的颜色描述。因此,本论文提出了积分-微分双空间图像的视觉颜色计算模型。
在人眼视觉系统中,颜色和纹理细节的产生是并生的,即纹理细节是通过视觉空间各维颜色的空间关系而产生的。为了在显示HDR图像时维持原场景的纹理细节和颜色的视觉感知,本论文基于颜色视觉机制提出了三种HDR图像的映射算法。尽管人眼能识别的总共亮度的动态范围可达到1014：1,但适应某一亮度场景条件下的动态范围非常有限,只有1000：1。人眼在观察HDR图像时,必然根据观察条件不同,作相应动态范围的压缩。显示媒介的动态范围也比较有限,如果在显示前模拟人眼视觉处理方式,就可以达到正确显示高动态场景的目的。本文根据这一理论,建立基于颜色视觉过程的HDR图像映射算法。我们知道,人眼纹理细节和颜色的识别,不是通过对象的绝对信息而是通过对象的相对信息而获得。因此,保持视觉空间各维颜色的相对感知信息,则可以达到显示HDR图像保持正确的视觉感知的目的。据此,本文通过计算光感知细胞在各适应性亮度关系下的相对视觉感知信息,提出可保持视觉空间中各维颜色的相对视觉感知信息的MSCSLIM映射算法。由于HDR图像的亮度范围非常大,人眼要获得图像的整个场景,必定是经过多个适应性亮度。本算法根据MSCSLIM获得各像素的适应性亮度。但是,此MSCSLIM只是考虑视觉的空间性,而没有考虑其视场内亮度变化剧烈程度的影响。因此,本文在MSCSLIM映射算法的基础上,提出SCRLIM映射算法,利用SCRLIM获得各像素的适应亮度,从而改善了映射的质量。
High dynamic range images, which information stored typically corresponds to the physical value of luminance or radiance that can be observed in the real world, allow a great higher dynamic range of luminance between the lightest and darkest areas than 1000:1. This higher dynamic range allows HDR images to more accurately represent the wide range of intensity levels found in real scenes, ranging from direct sunlight (108cd/m2)to faint starlight (10-6 cd/m2). Therefore, HDR images are often called scene-referred images in contrast to traditional low dynamic range images, which are device-referred images or output-referred images with dynamic range of about 100:1. Now, standard displays (CRT, LCD, al. et.) have a limited dynamic range. Taking typical CRT displays for example, the maximum luminance level of these CRT displays is about 80cd/m2 and their dynamic range is actually about 100:1. Paper even has lower dynamic range with about 50:1 for semi-gloss paper and about 30:1 for matte paper. For displaying on the standard displays, the dynamic range of the captured HDR scene must be compressed significantly and luminance levels also must be changed, which can induce a loss of contrast and then result in a loss of detail visibility and change of perceived color. If the impact of change of dynamic range for displaying LDR images on visible details is a case of cake, the loss of detail visibility and change of perceived color for displaying HDR images on the standard displays pose a difficult challenge for applying HDR images to fields of film, game animation, medical, military and printing and so on. Therefore, my work focus on problems of displaying HDR images on the standard displays without changing visual perception of the scenes in HDR images。It includes a nonlinear chromatic adaptation algotithm, spatial-integration and spatial-differentiation based color computation model for images, color vision based HDR image rendering algorithm, Multi-Scale Center-Surround Lateral Inhibition Mechanism based HDR image rendering algorithm, and Spatial and Contrast Related Lateral Inhibition Mechanism based HDR image rendering algorithm.
When we look at images on a display, we often have an impression of plausible real world depicition, although luminance and dynamic ranges are far lower than in reality. So, the key issue in displaying HDR image on the standard displays is not obtaining an optical match, but rather plausible reproduction of all important perception of scenes such as visible details and color. That is, my work is to reduce the dynamic range or contrast ratio of the entire HDR image, while retaining localized contrast, tapping into research on how the human eye and visual cortex perceive a scene, trying to represent the whole dynamic range while retaining realistic color and contrast.
For preserving color of HDR scenes, it means that color of HDR scenes must be keep constancy. Therefore, I focus on two types of mechanisms which can result in color constancy. One results from bleaching of photopigments in photorecptors which discounting color change induced by change of spectral distribution of illuminants. In this case, a non-linear chromatic adaptation algorithm is proposed in the dissertation. The other mechanism of color constancy comes from the center-surround spatial interaction of some cells in retinal and visual cortex. For example, lateral inhibition of receptive field can make different intensity of color look the same under different surroundings. This mechanism is suitable for depiction of image color. In this case, an image color computation model is proposed.
Visible contrast (or visible details) and color are not unrelated aspects in human visual system as head and tail of coin. Usually, visual contrast represents spatial relationship between a color and its neighbors for each color channel in human visual system. Therefore, this dissertation proposes three HDR image rendering algorithms that deal with problems of loss of visible details and change of color simultaneously by processing in visual space. Dynamic range adapted by human eye at one sight is only about 1000:1, though human eye can finally adapt great wide dynamic range. This situation is similar one that the standard displays have a limited dynamic range but we expect that they can finally display wide dynamic range scenes. Therefore, what the algorithm needs to do is what human visual system does. In this case, the first HDR image rendering algorithm is called color vision based HDR image rendering algorithm, which is based on mimicking pathway of color vision. Usually, visual information captured by human eye is not absolute but relative so that retaining relative visual information can attain the aim of retaining perception of scenes. The relative visual information of each pixel is computed by the photoreceptor response curves. Therefore, one important work is to obtain adapted luminance levels for each pixel. A multi-scale lateral inhibition mechanism is used to compute the adapted luminance levels. In this case, the second rendering algorithm is called Multi-Scale Center-Surround Lateral Inhibition Mechanism based HDR image rendering algorithm. However, In MSCSLIM rendering algorithm, the multi-scale lateral inhibition mechanism does not consider the influence of contrast between center and surrounds but consider the influence of spatial of receptive field, so that the MSCSLIM rendering algorithm may produce some black halos for some testing HDR images. In this case, another rendering algorithm is proposed, which uses a spatial and contrast related lateral inhibition mechanism to compute each adapted luminance level, and finally good visual results can produced.
本论文围绕HDR图像在低动态范围显示媒介上显示的问题进行研究,以保证人眼对参考图像的感知的一致性。论文提出非线性色度适应性算法、积分-微分双空间的图像视觉计算模型、基于颜色视觉过程HDR图像映射算法、基于多尺度边缘机相阻制(Multi-Scale Center-Surround Lateral Inhibition Mechanism, MSCSLIM)的HDR图像映射算法,以及基于对比度和空间相关的边缘相阻机制(Spatial and Contrast Related Lateral Inhibition Mechanism, SCRLIM)的HDR图像映射算法。
要解决HDR图像在低动态范围显示媒介上的显示问题,通过物理(仅仅指光)匹配方式显然不能保持原有的HDR图像中的场景,必须根据人眼视觉对HDR图像的感知特性,从视觉的角度进行映射处理,保持原有场景关键特征的视觉感知。对于静态场景来说,视觉感知的最关键特征是颜色和纹理细节。
根据对颜色视觉的研究发现,人眼存在两种视觉机制可以产生颜色或亮度在视觉上的恒常性。第一种为消色机制,即感光(或称为光感应)细胞的感光色素的漂白机制,这一机制使得即使显示的光源发生了变化,视觉感知的颜色还是可以保持不变。在此基础上本论文针对光源导致颜色变化的问题提出了非线性的色度适应性算法。第二种视觉机制是中心-周边感知结构细胞的空间作用机制。此机制中的边缘相阻效应同样使得各颜色在周边颜色的影响下可以产生颜色的恒常性。此颜色的恒常机制描述了颜色的空间影响关系,适合图像的颜色描述。因此,本论文提出了积分-微分双空间图像的视觉颜色计算模型。
在人眼视觉系统中,颜色和纹理细节的产生是并生的,即纹理细节是通过视觉空间各维颜色的空间关系而产生的。为了在显示HDR图像时维持原场景的纹理细节和颜色的视觉感知,本论文基于颜色视觉机制提出了三种HDR图像的映射算法。尽管人眼能识别的总共亮度的动态范围可达到1014：1,但适应某一亮度场景条件下的动态范围非常有限,只有1000：1。人眼在观察HDR图像时,必然根据观察条件不同,作相应动态范围的压缩。显示媒介的动态范围也比较有限,如果在显示前模拟人眼视觉处理方式,就可以达到正确显示高动态场景的目的。本文根据这一理论,建立基于颜色视觉过程的HDR图像映射算法。我们知道,人眼纹理细节和颜色的识别,不是通过对象的绝对信息而是通过对象的相对信息而获得。因此,保持视觉空间各维颜色的相对感知信息,则可以达到显示HDR图像保持正确的视觉感知的目的。据此,本文通过计算光感知细胞在各适应性亮度关系下的相对视觉感知信息,提出可保持视觉空间中各维颜色的相对视觉感知信息的MSCSLIM映射算法。由于HDR图像的亮度范围非常大,人眼要获得图像的整个场景,必定是经过多个适应性亮度。本算法根据MSCSLIM获得各像素的适应性亮度。但是,此MSCSLIM只是考虑视觉的空间性,而没有考虑其视场内亮度变化剧烈程度的影响。因此,本文在MSCSLIM映射算法的基础上,提出SCRLIM映射算法,利用SCRLIM获得各像素的适应亮度,从而改善了映射的质量。
High dynamic range images, which information stored typically corresponds to the physical value of luminance or radiance that can be observed in the real world, allow a great higher dynamic range of luminance between the lightest and darkest areas than 1000:1. This higher dynamic range allows HDR images to more accurately represent the wide range of intensity levels found in real scenes, ranging from direct sunlight (108cd/m2)to faint starlight (10-6 cd/m2). Therefore, HDR images are often called scene-referred images in contrast to traditional low dynamic range images, which are device-referred images or output-referred images with dynamic range of about 100:1. Now, standard displays (CRT, LCD, al. et.) have a limited dynamic range. Taking typical CRT displays for example, the maximum luminance level of these CRT displays is about 80cd/m2 and their dynamic range is actually about 100:1. Paper even has lower dynamic range with about 50:1 for semi-gloss paper and about 30:1 for matte paper. For displaying on the standard displays, the dynamic range of the captured HDR scene must be compressed significantly and luminance levels also must be changed, which can induce a loss of contrast and then result in a loss of detail visibility and change of perceived color. If the impact of change of dynamic range for displaying LDR images on visible details is a case of cake, the loss of detail visibility and change of perceived color for displaying HDR images on the standard displays pose a difficult challenge for applying HDR images to fields of film, game animation, medical, military and printing and so on. Therefore, my work focus on problems of displaying HDR images on the standard displays without changing visual perception of the scenes in HDR images。It includes a nonlinear chromatic adaptation algotithm, spatial-integration and spatial-differentiation based color computation model for images, color vision based HDR image rendering algorithm, Multi-Scale Center-Surround Lateral Inhibition Mechanism based HDR image rendering algorithm, and Spatial and Contrast Related Lateral Inhibition Mechanism based HDR image rendering algorithm.
When we look at images on a display, we often have an impression of plausible real world depicition, although luminance and dynamic ranges are far lower than in reality. So, the key issue in displaying HDR image on the standard displays is not obtaining an optical match, but rather plausible reproduction of all important perception of scenes such as visible details and color. That is, my work is to reduce the dynamic range or contrast ratio of the entire HDR image, while retaining localized contrast, tapping into research on how the human eye and visual cortex perceive a scene, trying to represent the whole dynamic range while retaining realistic color and contrast.
For preserving color of HDR scenes, it means that color of HDR scenes must be keep constancy. Therefore, I focus on two types of mechanisms which can result in color constancy. One results from bleaching of photopigments in photorecptors which discounting color change induced by change of spectral distribution of illuminants. In this case, a non-linear chromatic adaptation algorithm is proposed in the dissertation. The other mechanism of color constancy comes from the center-surround spatial interaction of some cells in retinal and visual cortex. For example, lateral inhibition of receptive field can make different intensity of color look the same under different surroundings. This mechanism is suitable for depiction of image color. In this case, an image color computation model is proposed.
Visible contrast (or visible details) and color are not unrelated aspects in human visual system as head and tail of coin. Usually, visual contrast represents spatial relationship between a color and its neighbors for each color channel in human visual system. Therefore, this dissertation proposes three HDR image rendering algorithms that deal with problems of loss of visible details and change of color simultaneously by processing in visual space. Dynamic range adapted by human eye at one sight is only about 1000:1, though human eye can finally adapt great wide dynamic range. This situation is similar one that the standard displays have a limited dynamic range but we expect that they can finally display wide dynamic range scenes. Therefore, what the algorithm needs to do is what human visual system does. In this case, the first HDR image rendering algorithm is called color vision based HDR image rendering algorithm, which is based on mimicking pathway of color vision. Usually, visual information captured by human eye is not absolute but relative so that retaining relative visual information can attain the aim of retaining perception of scenes. The relative visual information of each pixel is computed by the photoreceptor response curves. Therefore, one important work is to obtain adapted luminance levels for each pixel. A multi-scale lateral inhibition mechanism is used to compute the adapted luminance levels. In this case, the second rendering algorithm is called Multi-Scale Center-Surround Lateral Inhibition Mechanism based HDR image rendering algorithm. However, In MSCSLIM rendering algorithm, the multi-scale lateral inhibition mechanism does not consider the influence of contrast between center and surrounds but consider the influence of spatial of receptive field, so that the MSCSLIM rendering algorithm may produce some black halos for some testing HDR images. In this case, another rendering algorithm is proposed, which uses a spatial and contrast related lateral inhibition mechanism to compute each adapted luminance level, and finally good visual results can produced.
引文
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