Sparse representation-based image quality assessment

• A sparse representation-based method for image quality assessment is proposed.
• The proposed method is designed to extract and compare perceptually important structures in images.
• Our method achieves competitive performance with the state-of-the-art.
• The proposed method consistently produces (statistically significant) lower error compared to the rival methods.

A highly promising approach to assess the quality of an image involves comparing the perceptually important structural information in this image with that in its reference image. The extraction of the perceptually important structural information is however a challenging task. This paper employs a sparse representation-based approach to extract such structural information. It proposes a new metric called the sparse representation-based quality (SPARQ) index that measures the visual quality of an image. The proposed approach learns the inherent structures of the reference image as a set of basis vectors. These vectors are obtained such that any structure in the image can be efficiently represented by a linear combination of only a few of these basis vectors. Such a sparse strategy 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. To estimate the visual quality of the distorted image, structures in the visually important areas in this image are compared with those in the reference image, in terms of the learnt basis vectors. 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 of all datasets and overall, performs better than a number of popular image quality metrics.