An Image Quality Assessment Method based on Sparse Neighbor Significance

In this paper, the image quality assessment problem is tackled from a sparse coding perspective, and a new automated image quality assessment algorithm is presented. Specifically, the input image is first divided into non-overlapping blocks and sparse coding is used to reconstruct a central sub-block using the neighboring sub-blocks as dictionaries. The resulting 2D sparse vectors from each neighboring sub-block, are devised as significance maps that are then used in similarity measures between the reference and distorted images. The proposed method is compared against various recently introduced shallow and deep methods across four datasets and multiple distortion types. The experimental results that have been obtained show that it possesses a strong correlation with the Human Visual System and outperforms its counterparts.