Image Quality Assessment: From Error Measurement to Structural Similarity

Abstract—Objective methods for assessing perceptual im-age quality traditionally attempt to quantify the visibility of errors (differences) between a distorted image and a ref-erence image using a variety of known properties of the hu-man visual system. Under the assumption that human visual perception is highly adapted for extracting structural information from a scene, we introduce an alternative com-plementary framework for quality assessment based on the degradation of structural information. As a specific exam-ple of this concept, we develop a Structural Similarity Index and demonstrate its promise through a set of intuitive ex-amples, as well as comparison to both subjective ratings and state-of-the-art objective methods on a database of images compressed with JPEG and JPEG2000. A MatLab imple-mentation of the proposed algorithm is available online at