With the increasing demands of end-users to the visual perception in three-dimension (3D) image, quality assessment for 3D imageis dominantly required as the feedback information for multimedia transmission systems. In this paper, a novel full-reference quality assessment method by considering the depth and integral color information of 3D image under cloud computing environment is proposed. Based on the property of the depth information in 3D image, the depth map is firstly separated into different planes according to the perception of human visual system (HVS). Then, after express the image pixels of every separated plane through quaternions, the structural and energy information are separated by quaternion singular value decomposition (QSVD). The distortion of structural and energy in every plane are calculated in various formulas respectively. The final result is calculated in terms of the global score, which synthesizes the structural and energy distortion scores in every individual depth plane. It should be pointed out that the chrominance information is employed in our mechanism to evaluate the color image quality because of its useful characteristic for 3D color image quality assessment, and its spatial correlation is used for calculating structural distortion through vector cross-product. Our experimental results confirm that the proposed method has achieves better performance under cloud computing environments compared with other existing 3D image quality assessment methods.

中文翻译：

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一种云计算环境下基于矢量信息和四元数矩阵SVD的3D图像质量评估方法

随着终端用户对三维 (3D) 图像视觉感知的需求不断增加，3D 图像的质量评估作为多媒体传输系统的反馈信息成为主要需求。本文提出了一种在云计算环境下考虑3D图像深度和整体颜色信息的全参考质量评估方法。基于3D图像中深度信息的特性，首先根据人类视觉系统（HVS）的感知将深度图划分为不同的平面。然后，通过四元数表示每个分离平面的图像像素后，通过四元数奇异值分解（QSVD）分离结构和能量信息。各平面的结构畸变和能量畸变分别用各种公式计算。最终结果是根据全局分数计算的，它综合了每个单独深度平面的结构和能量失真分数。需要指出的是，色度信息在我们的机制中被用于评估彩色图像质量，因为它对 3D 彩色图像质量评估有用，并且其空间相关性用于通过矢量叉积计算结构失真。我们的实验结果证实，与其他现有的 3D 图像质量评估方法相比，所提出的方法在云计算环境下取得了更好的性能。需要指出的是，色度信息在我们的机制中被用于评估彩色图像质量，因为它对 3D 彩色图像质量评估有用，并且其空间相关性用于通过矢量叉积计算结构失真。我们的实验结果证实，与其他现有的 3D 图像质量评估方法相比，所提出的方法在云计算环境下取得了更好的性能。需要指出的是，色度信息在我们的机制中被用于评估彩色图像质量，因为它对 3D 彩色图像质量评估有用，并且其空间相关性用于通过矢量叉积计算结构失真。我们的实验结果证实，与其他现有的 3D 图像质量评估方法相比，所提出的方法在云计算环境下取得了更好的性能。