Gradient information-based image quality metric

In this paper, we propose a new image quality metric using the gradient information. When an image is degraded, the difference between the reference and distorted images occurs. This difference is an important factor in image quality assessment. To assess the image quality, we use gradient information of the pixels having large differences between the reference and distorted images. In this paper, the Harris response, a well-known feature, is used to obtain the gradient information for assessing the image quality. That is, Harris response values at pixels having the difference between the reference and distorted images are compared for evaluating the image quality. For detecting these pixels, we use a cross-projection tensor based edge suppression technique. Experimental results with the LIVE data set show the effectiveness of the proposed quality measure.