Sparse Representation-based Image Quality Assessment

TL;DR

This paper introduces SPARQ, a novel image quality assessment metric based on sparse representation that models perceptually important structures and shows high correlation with subjective ratings across multiple datasets.

Contribution

It proposes a new sparse representation-based metric for image quality assessment that aligns with visual perception and outperforms existing methods.

Findings

SPARQ correlates highly with subjective ratings across datasets

The method outperforms or matches state-of-the-art IQA metrics

Sparse basis vectors resemble receptive fields in the visual cortex

Abstract

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.