Image quality assessment:
From error visibility to structural similarity
Z Wang, A C Bovik, H R Sheikh, and E P Simoncelli
To appear in:
IEEE Transactions on Image Processing
13(4): 600-612, April 2004
© IEEE Signal Processing Society.
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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