  In this paper, an efficient Minkowski Distance based Metric (MDM) for
no-reference (NR) quality assessment of contrast distorted images is proposed.
It is shown that higher orders of Minkowski distance and entropy provide
accurate quality prediction for the contrast distorted images. The proposed
metric performs predictions by extracting only three features from the
distorted images followed by a regression analysis. Furthermore, the proposed
features are able to classify type of the contrast distorted images with a high
accuracy. Experimental results on four datasets CSIQ, TID2013, CCID2014, and
SIQAD show that the proposed metric with a very low complexity provides better
quality predictions than the state-of-the-art NR metrics. The MATLAB source
code of the proposed metric is available to public at
http://www.synchromedia.ca/system/files/MDM.zip.
