Gradient Magnitude Similarity Deviation:
A Highly Efficient Perceptual Image Quality Index
Wufeng Xue, Lei
Zhang, Xuanqin
Mou, and Alan
C. Bovik
To appear, IEEE Trans.
on Image Processing, 2014.
Abstract:
It is an important task to faithfully evaluate the
perceptual quality of output images in many applications such as image
compression, image restoration and multimedia streaming. A good image quality
assessment (IQA) model should not only deliver high
quality prediction accuracy but also be computationally efficient. The
efficiency of IQA metrics is becoming particularly important due to the
increasing proliferation of high-volume visual data in high-speed networks. We
present a new effective and efficient IQA model, called gradient magnitude
similarity deviation (GMSD). The image gradients are sensitive to image distortions,
while different local structures in a distorted image suffer different degrees
of degradations. This motivates us to explore the use of global variation of
gradient based local quality map for overall image quality prediction. We find
that the pixel-wise gradient magnitude similarity (GMS) between the reference
and distorted images combined with a novel pooling strategy ¨C the standard
deviation of the GMS map ¨C can predict accurately perceptual image quality. The
resulting GMSD algorithm is much faster than most state-of-the-art IQA methods,
and delivers highly competitive prediction accuracy.
Paper: download here
Matlab code of GMSD: download here
GMSD performance indices: download here
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