A statistical evaluation of Sparsity-based Distance Measure (SDM) as an image quality assessment algorithm
2014
Sparsity-based Distance Measure (SDM), a sparse
reconstruction-based image similarity measure was recently
proposed and shown to have promising applications in image
classification, clustering and retrieval. In this paper, we present
a statistical evaluation of SDM’s performance as an image qual-
ity assessment (IQA) algorithm. This evaluation is carried out
on the LIVE image database. We show that the SDM performs
fairly in comparison with the state-of-the-art while possessing
several attractive properties. Specifically, we demonstrate its
robustness to rotation (
90
o
,
180
o
), scaling, and combinations of
distortions – properties that are highly desirable of any IQA
algorithm
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