Objective Image Quality Metrics (IQMs) are introduced with the goal of modeling the perceptual quality scores given by observers to an image. In this study we use a pre-trained Convolutional Neural Network (CNN) model to extract feature maps at different convolutional layers of the test and reference image. We then compare the feature maps using traditional IQMs such as: SSIM, MSE, and PSNR. Experimental results on four benchmark datasets show that our proposed approach can increase the accuracy of these IQMs by an average of 23%. Compared to I I other state-of-the-art IQMs, our proposed approach can either outperform or perform as good as the mentioned I I metrics. We can show that by linking traditional IQMs and pre-trained CNN models we are able to evaluate image quality with a high accuracy.