Center for Signal and Image Processing, School of Electrical and Computer Engineering, Georgia Institute of Technology, Atlanta, GA 30332, USA.
IEEE Trans Image Process. 2012 Sep;21(9):3902-14. doi: 10.1109/TIP.2012.2200490. Epub 2012 May 22.
Although several subjective and objective quality assessment methods have been proposed in the literature for images and videos from single cameras, no comparable effort has been devoted to the quality assessment of multicamera images. With the increasing popularity of multiview applications, quality assessment of multicamera images and videos is becoming fundamental to the development of these applications. Image quality is affected by several factors, such as camera configuration, number of cameras, and the calibration process. In order to develop an objective metric specifically designed for multicamera systems, we identified and quantified two types of visual distortions in multicamera images: photometric distortions and geometric distortions. The relative distortion between individual camera scenes is a major factor in determining the overall perceived quality. In this paper, we show that such distortions can be translated into luminance, contrast, spatial motion, and edge-based structure components. We propose three different indices that can quantify these components. We provide examples to demonstrate the correlation among these components and the corresponding indices. Then, we combine these indices into one multicamera image quality measure (MIQM). Results and comparisons with other measures, such as peak signal-to noise ratio, mean structural similarity, and visual information fidelity show that MIQM outperforms other measures in capturing the perceptual fidelity of multicamera images. Finally, we verify the results against subjective evaluation.
尽管文献中已经提出了几种用于单摄像机图像和视频的主观和客观质量评估方法，但对于多摄像机图像的质量评估却没有进行相应的研究。随着多视角应用的日益普及，多摄像机图像和视频的质量评估对于这些应用的发展变得至关重要。图像质量受到多个因素的影响，例如摄像机配置、摄像机数量和校准过程。为了开发专门针对多摄像机系统的客观度量，我们确定并量化了多摄像机图像中的两种类型的视觉失真：光度失真和几何失真。各个摄像机场景之间的相对失真程度是确定整体感知质量的主要因素。在本文中，我们表明这些失真可以转换为亮度、对比度、空间运动和基于边缘的结构分量。我们提出了三个不同的指标，可以量化这些分量。我们提供了一些示例来说明这些分量和相应指标之间的相关性。然后，我们将这些指标组合成一个多摄像机图像质量度量（MIQM）。结果与其他度量（如峰值信噪比、平均结构相似度和视觉信息保真度）的比较表明，MIQM 在捕捉多摄像机图像的感知逼真度方面优于其他度量。最后，我们根据主观评估来验证结果。
