Assessing image or image quality is extremely crucial in various applications in the fields of image processing and computer vision. Numerous methods have been presented to assess the quality of images by comparing between the reference and distorted images. However, most of the previous metrics presented in this field produced a significant amount of correspondence between the reference image and the distorted images. In view of these challenges, we would like to put forward the proposal for a new comprehensive index that gauges the quality of an image with regard to gradient information and statistical coefficients. This index fuses the Gradient Similarity Index (GSI) with Universal Quality Index (UQI) and the gradient-based edge detection method of Sobel. We evaluated the performance of our approach using two well-known databases the LIVE and CSIQ and the results indicate that the proposed approach outperforms the (GSI) and (UQI) even under distortion and noise.