This paper consists of a comparative analysis of full reference image quality measures. The quality evaluation model of Peak signal to noise ratio (PSNR), Structural Similarity Index (SSIM) as well as Visual Information Fidelity (VIF) has been discussed. This paper emphasizes on the quality evaluation of images after their recovery from their noisy counterparts. The image quality assessment algorithms discussed are used to develop further image processing algorithms so that quality of images recovered is superior. The paper stresses on the image quality metrics from the point of view of correlation with subjective measure of image quality for successful implementation in research and development in the image processing industry.