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