Pan-sharpening (PS) is a method of fusing the spatial details of a high-resolution panchromatic (PAN) image with the spectral information of a low-resolution multi-spectral (MS) image. Visual inspection is a crucial step in the evaluation of fused products whose subjectivity renders the assessment of pansharpened data a challenging problem. Most previous research on the development of PS algorithms has only superficially addressed the issue of qualitative evaluation, generally by depicting visual representations of the fused images. Hence, it is highly desirable to be able to predict pan-sharpened image quality automatically and accurately, as it would be perceived and reported by human viewers. Such a method is indispensable for the correct evaluation of PS techniques that produce images for visual applications such as Google Earth and Microsoft Bing. Here, we propose a new image quality assessment (IQA) measure that supports the visual qualitative analysis of pansharpened outcomes by using the statistics of natural images, commonly referred to as natural scene statistics (NSS), to extract statistical regularities from PS images. Importantly, NSS are measurably modified by the presence of distortions. We analyze six PS methods in the presence of two common distortions, blur and white noise, on PAN images. Furthermore, we conducted a human study on the subjective quality of pristine and degraded PS images and created a completely blind (opinion-unaware) fused image quality analyzer. In addition, we propose an opinion-aware fused image quality analyzer, whose predictions with respect to human perceptual evaluations of pansharpened images are highly correlated.