Perceptual image quality assessment: From error visibility to structural similarity

Z Wang, A C Bovik, H R Sheikh and E P Simoncelli

Published in IEEE Trans. Image Processing, vol.13(4), pp. 600--612, Apr 2004.

Recipient, Sustained Impact Paper award, 2016; Best Paper award, 2009 - IEEE SIgnal Processing Society.

DOI: 10.1109/TIP.2003.819861

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    • 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 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/.
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