Regularization of the structural similarity index based on preservation of edge direction

The goal of this paper is to improve the performance of the SSIM indexes while retaining their computational efficiency. To this end, we first design an edge-quality term based on the preservation of edge direction, and then adaptively combine it with the SSIM indexes, yielding the regularized SSIM indexes. The proposed method is based on two assumptions: (1) as the quality of a distorted image declines, the human vision system (HVS) is more likely to judge its quality based on the difficulty of recognizing its content; and (2) the preservation of edge direction is a good measurement of this difficulty. Extensive evaluation shows that the regularized SSIM indexes achieve comparable performance to the state-of-the-art method while requiring much less computation time.