A Survey on Full Reference Image Quality Assessment Algorithms

Image quality measurement is very important for various image processing applications such as recognition, retrieval, classification, compression, restoration and similar fields. The images may contain different types of distortions like blur, noise, contrast change etc. So it is essential to rate the image quality appropriately. Traditionally subjective rating methods are used to evaluate the quality of the image, in which humans rated the image quality based on time requirements. This is a costly process and it needs experts for evaluating image quality. Nowadays many image quality assessment algorithms are available for finding the quality of images. These are mainly based on the properties of human visual system. These image quality assessment algorithms are the objective methods for finding quality. Most of the methods are heuristic and limited to specific application environment. Whereas some methods perform efficiently and having comparable performance with the subjective ratings. Objective methods are easy for integrating into various image compression techniques and other image processing applications. Based on the availability of the image, image quality assessment algorithms are classified into full reference, reduced reference and no reference respectively. Full reference algorithms normally adopt a two stage structure including local quality measurement and pooling to get the quality value. The input for this two stage structure includes a reference image and a distorted image. This paper presents a survey on the existing image quality assessment algorithms based on full reference method, in which a reference image will be available for finding the quality of the