IJCAT - International Journal of Computing and Technology, Volume 1, Issue 11, December 2014 ISSN : 2348 - 6090 www.IJCAT.org 582 Image Quality Assessment Technique Using Gradient Magnitude Similarity with Phase Congruency 1 Rohit Kumar, 2 Vishal Moyal 1, 2 Department of Electronics & Communication Abstract - The goal of quality assessment (QA) research is to design algorithms that can automatically assess the quality of images in a perceptually consistent manner. Image QA algorithms generally interpret image quality as fidelity or similarity with a “reference” or “perfect” image in some perceptual space. In order to improve the assessment accuracy of white noise, Gauss blur, JPEG2000 compression and other distorted images, this paper puts forward an image quality assessment method based on phase congruency and gradient magnitude. The experimental results show that the image quality assessment method has a higher accuracy than traditional method and it can accurately reflect the image visual perception of the human eye. In this paper, we propose an image information measure that quantifies the information that is present in the reference image and how much of this reference information can be extracted from the distorted image. Keywords - Image quality assessment (IQA), Structural similarity index (SSIM), Phase congruency (PC), Gradient magnitude (GM), Low level feature. 1. Introduction Image quality assessment is an important study topic in the image processing area. Image quality is a fundamental characteristic of any image which measures the perceived image degradation .Generally, compared with an ideal or perfect image. Digital images are subject to a wide variety of distortions during acquisition, processing, compression, storage, transmission and reproduction, any of which may result in a degradation of visual quality. Imaging systems introduces some amount of distortion or artifacts which reduces the quality assessment and here it is our point of interest. By defining image quality in terms of a deviation from the ideal situation, quality measures become technical in the sense that they can be impartially determined in terms of deviations from the ideal models. Generally speaking, visual quality assessment can be divided into two categories one is subjective visual quality assessment and another one is objective visual quality assessment. Subjective quality assessment is done by humans which represents the realistic opinion towards an Image. Image quality objective assessment uses the mathematical model to quantitative the assessment index and simulates human visual perception system to assess the image quality. Common image quality objective assessment indexes include PSNR (Peak Signal to Noise Ratio), MSE(mean square error) and SSIM (Structural Similarity). Based upon the Availability of Reference Objective quality assessment is classified as no reference (NR), reduced reference (RR),full reference(FR)[1]methods. If there is no reference signal available for the distorted (test) one to compare with, then a quality evaluation method is termed as a No-reference (NR).If the information of the reference medium is partially available, e.g., in the form of a set of extracted features, then this is the so-called Reduced-Reference (RR) method. FR method needs the complete reference medium to assess the distorted medium. Since it has the full information about original medium, it is expected to have the best quality prediction performance. Most existing quality assessment schemes belong to this category. The following are implemented for image quality assessment algorithms as Mean square error (MSE), peak signal to noise (PSNR), structural similarity index (SSIM). Mean Squared Error is the average squared difference between a reference image and a distorted image. It is computed pixel-by-pixel by adding up the squared differences of all the pixels and dividing by the total pixel count. Peak Signal-to-Noise Ratio is the ratio between the reference signal and the distortion signal in an image, given in decibels. The higher the PSNR, the closer the distorted image is to the original SNR(Peak