IEEE Transactions on Image Processing, vol. 13, no. 4, Apr. 2004

Image Quality Assessment: From Error Visibility to Structural Similarity

Zhou Wang¹, Alan C. Bovik², Hamid R. Sheikh² and Eero P. Simoncelli¹

¹Laboratory for Computational Vision (LCV), New York University, New York, NY 10003

²Laboratory for Image and Video Engineering (LIVE), The University of Texas at Austin, Austin, TX 78712

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Abstract: Objective methods for assessing perceptual image quality have traditionally attempted to quantify the visibility of errors 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. A MATLAB implementation of the proposed algorithm is available online at http://www.cns.nyu.edu/~lcv/ssim/.

Index Terms – Image quality assessment, perceptual quality, human visual system, error sensitivity, structural similarity, structural information, image coding, JPEG, JPEG2000