Image quality metrics have been widely used in imaging systems to maintain and improve the quality of images being processed and transmitted. Due to the close relationship between image quality perception and the human visual system, the development of image quality metrics has been contributed to by both psychologists and computer scientists. In this paper, three novel image quality metrics are proposed by improving the well-known image quality metric structural similarity index (SSIM). In this new approach, images are not compared directly, but their feature maps are (preprocessing is incorporated to extract the corner, edge and symmetry maps). The similarity measured (by SSIM) between corner, edge and symmetry maps of images being compared is used as an indicator of image quality, and named C SSIM, E SSIM and S SSIM respectively. The experiments show that all the proposed image quality metrics have a better performance than SSIM, and E SSIM has the best performance among them. 1

The performance of several discrepancy measures for the comparison of edge images is analyzed and a novel similarity metric aimed at overcoming their problems is proposed. The algorithm finds an optimal matching of the pixels between the images and estimates the error produced by this matching. The resulting Pixel Correspondence Metric (PCM) can take into account edge strength as well as the displacement of edge pixel positions in the estimation of similarity. A series of experimental tests shows the new metric to be a robust and effective tool in the comparison of edge images when a small localization error of the detected edges is allowed.