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Accurate representation of objects, large and small, has always been in the forefront of scientific interest. Threedimensional (3D) information of objects are traditionally extracted using projective and descriptive geometry of the objects in two dimensions. Currently, 3D scanners are extensively used to perform such processes. 3D scanners can examine an object to gather information about its physical structure including shape, volume, and texture. Although various 3D scanners utilize different capturing and reconstruction methods, they ultimately produce a form of depth images which are used to generate 3D point-clouds. However, the fundamental problem with depth image-based processing is that, the depth image could contain blocky artifacts, discontinuities and noise. This could result in occlusion and image warping. 3D image quality metrics are critical in the evaluation of 3D images in fields and industries such as automotive, art, biometrics, and biomedical. Although, there are several two-dimensional (2D or color) quality metrics, there is a visible void in the field of objective depth quality assessment. This paper proposes a novel no-reference based depth image measure and further fuses this measure with an extended color quality metric. The Color-Depth image quality measure CDME has no constraint on the 3D images being compared and demonstrates a very high correlation with the human judgment. Extensive computer simulations are performed to evaluate the proposed color-depth image quality measure against other no-reference image error measurements. The effectiveness of the presented measure is evaluated by using the NYU Depth Dataset V2. Experimental results show that the proposed measure provides a clear distinction between lower quality and higher quality images. Eventually, the presented method could be used to provide optimal parameters for 3D post-processing algorithms.
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