A new reduced-reference metric for measuring spatial resolution enhanced images

Abstract

Assessment of image quality is critical for many image processing algorithms, such as image acquisition, compression, restoration, enhancement, and reproduction. In general, image quality assessment algorithms are classified into three categories: full-reference (FR), reduced-reference (RR), and no-reference (NR) algorithms. The design of NR metrics is extremely difficult and little progress has been made. FR metrics are easier to design and the majority of image quality assessment algorithms are of this type. A FR metric requires the reference image and the test image to have the same size. This may not the case in real life of image processing. In spatial resolution enhancement of hyperspectral images, such as pan-sharpening, the size of the enhanced images is larger than that of the original image. Thus, the FR metric cannot be used. A common approach in practice is to first down-sample an original image to a low resolution image, then to spatially enhance the down-sampled low resolution image using a subject enhancement technique. In this way, the original image and the enhanced image have the same size and the FR metric can be applied to them. However, this common approach can never directly assess the image quality of the spatially enhanced image that is produced directly from the original image. In this paper, a new RR metric was proposed for measuring the visual fidelity of an image with higher spatial resolution. It does not require the sizes of the reference image and the test image to be the same. The iterative back projection (IBP) technique was chosen to enhance the spatial resolution of an image. Experimental results showed that the proposed RR metrics work well for measuring the visual quality of spatial resolution enhanced hyperspectral images. They are consistent with the corresponding FR metrics.