  A successful approach to image quality assessment involves comparing the
structural information between a distorted and its reference image. However,
extracting structural information that is perceptually important to our visual
system is a challenging task. This paper addresses this issue by employing a
sparse representation-based approach and proposes a new metric called the
\emph{sparse representation-based quality} (SPARQ) \emph{index}. The proposed
method learns the inherent structures of the reference image as a set of basis
vectors, such that any structure in the image can be represented by a linear
combination of only a few of those basis vectors. This sparse strategy is
employed because it is known to generate basis vectors that are qualitatively
similar to the receptive field of the simple cells present in the mammalian
primary visual cortex. The visual quality of the distorted image is estimated
by comparing the structures of the reference and the distorted images in terms
of the learnt basis vectors resembling cortical cells. Our approach is
evaluated on six publicly available subject-rated image quality assessment
datasets. The proposed SPARQ index consistently exhibits high correlation with
the subjective ratings on all datasets and performs better or at par with the
state-of-the-art.
