title: Image quality assessment: from error visibility to structural similarity.
abstract: Objective methods for assessing perceptual image quality traditionally attempted to quantify the visibility of errors (differences) between a distorted image and a reference image using a variety of known properties of the human visual system. Under the assumption that human visual perception is highly adapted for extracting structural information from a scene, we introduce an alternative complementary framework for quality assessment based on the degradation of structural information. As a specific example of this concept, we develop a Structural Similarity Index and demonstrate its promise through a set of intuitive examples, 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.
authors.affiliation: Howard Hughes Medical Institute, the Center for Neural Science and the Courant Institute for Mathematical Sciences, New York University, New York, NY 10012, USA. zhouwang@ieee.org
authors.forename: Zhou
authors.initials: Z
authors.lastname: Wang
authors.forename: Alan Conrad
authors.initials: AC
authors.lastname: Bovik
authors.forename: Hamid Rahim
authors.initials: HR
authors.lastname: Sheikh
authors.forename: Eero P
authors.initials: EP
authors.lastname: Simoncelli
mesh_terms: D000465:Algorithms; D003627:Data Interpretation, Statistical; D019146:Hypermedia; D007089:Image Enhancement / Q000379:methods* / Q000592:standards; D007090:Image Interpretation, Computer-Assisted / Q000379:methods* / Q000592:standards*; D016247:Information Storage and Retrieval / Q000379:methods* / Q000592:standards; D015233:Models, Statistical; D010363:Pattern Recognition, Automated; D011786:Quality Control; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity; D012815:Signal Processing, Computer-Assisted; D013382:Subtraction Technique