  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 the standard deviation of the GMS map 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.
