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Image quality assessment: from error visibility to structural similarity

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4 Author(s)
Zhou Wang ; Center for Neural Sci., New York Univ., NY, USA ; Bovik, A.C. ; Sheikh, H.R. ; Simoncelli, E.P.

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. A MATLAB implementation of the proposed algorithm is available online at http://www.cns.nyu.edu/∼lcv/ssim/.

Published in:

Image Processing, IEEE Transactions on  (Volume:13 ,  Issue: 4 )

Date of Publication:

April 2004

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