MLSIM: A Multi-Level Similarity index for image quality assessment
Image Communication
| Hi-index | 0.43 |
In this paper, we propose a new image quality metric using the gradient information. When an image is degraded, the difference exists between the reference and distorted images. This difference is an important factor in image quality assessment. To assess the quality of an image, we use gradient information of the pixels having large differences between the reference and distorted images. In this paper, the Harris response (HR), a well-known feature, is used to obtain the gradient information for assessing the image quality. That is, HR values at pixels having the nonzero 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.