Improved structural similarity metric for the visible quality measurement of images

The visible quality assessment of images is important to evaluate the performance of image processing methods such as image correction, compressing, and enhancement. The structural similarity is widely used to determine the visible quality; however, existing structural similarity metrics cannot correctly assess the perceived human visibility of images that have been slightly geometrically transformed or images that have undergone significant regional distortion. We propose an improved structural similarity metric that is more close to human visible evaluation. Compared with the existing metrics, the proposed method can more correctly evaluate the similarity between an original image and various distorted images.