From Sparse Coding Significance to Perceptual Quality: A New Approach for Image Quality Assessment

An increasing number of image processing applications require an automated quality prediction of the visual content as perceived by humans. Since, sparse coding is suggested to be an underlying strategy of the brain's neural system, it would be logical to assume that specific tasks like quality assessment also attempt to adhere to this strategy. However, existing perceptual quality predictors, often mimicking the different stages of the human visual system and deploying machine learning strategies, such as neural networks, rarely integrate the concept of sparse coding in their design. In this paper, we first investigate the validity of such assumption by performing an empirical analysis on the relation between the structural information of the scene - captured via sparseness significance - and perceptual quality. Subsequently, we propose a new approach to integrate the significance of sparse coding features in the future imagequality measure (IQM) designs. We utilize the Fourier transform as a case study, which leads to a new IQM called sparseness significance ranking measure (SSRM). This measure essentially deploys a Fourier basis for sparse coding, a ranking mechanism based upon the amplitudes of the sparse coefficients and subsequently a complex correlation metric that assesses the correspondence between the ranked coefficient amplitude profiles of the reference and the distorted image. Moreover, we introduce a new methodology, namely separation ratio analysis, to assess the prediction quality of individual features or quality predictors given a target perceptual quality. The quality predictions by the proposed SSRM show excellent compatibility with perceptual quality scores. A set of routine benchmarking experiments utilizing the LIVE and CSIQ, IVC and TID2008 databases indicates a highly competitive performance with state of the art IQMs. Moreover, it delivers this performance at a low computational cost.