  The state-of-the-art pooling strategies for perceptual image quality
assessment (IQA) are based on the mean and the weighted mean. They are robust
pooling strategies which usually provide a moderate to high performance for
different IQAs. Recently, standard deviation (SD) pooling was also proposed.
Although, this deviation pooling provides a very high performance for a few
IQAs, its performance is lower than mean poolings for many other IQAs. In this
paper, we propose to use the mean absolute deviation (MAD) and show that it is
a more robust and accurate pooling strategy for a wider range of IQAs. In fact,
MAD pooling has the advantages of both mean pooling and SD pooling. The joint
computation and use of the MAD and SD pooling strategies is also considered in
this paper. Experimental results provide useful information on the choice of
the proper deviation pooling strategy for different IQA models.
