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. 1
As a recent thrust in full-reference image quality assessment, the structural similarity REF (SSIM) outperforms the previous methods.
Image quality assessment: from error visibility to structural similarity
IEEE Transactions on Image Processing
IEEE Transactions on Image Processing
Computer Science Mathematics Medicine