  Image quality assessment that aims at estimating the subject quality of
images, builds models to evaluate the perceptual quality of the image in
different applications. Based on the fact that the human visual system (HVS) is
highly sensitive to structural information, the edge information extraction is
widely applied in different IQA metrics. According to previous studies, the
image gradient magnitude (GM) and the Laplacian of Gaussian (LOG) operator are
two efficient structural features in IQA tasks. However, most of the IQA
metrics achieve good performance only when the distorted image is totally
registered with the reference image, but fail to perform on images with small
translations. In this paper, we propose an FR-IQA model with the quadratic sum
of the GM and the LOG signals, which obtains good performance in image quality
estimation considering shift-insensitive property for not well-registered
reference and distortion image pairs. Experimental results show that the
proposed model works robustly on three large scale subjective IQA databases
which contain a variety of distortion types and levels, and stays in the
state-of-the-art FR-IQA models no matter for single distortion type or across
whole database. Furthermore, we validated that the proposed metric performs
better with shift-insensitive property compared with the CW-SSIM metric that is
considered to be shift-insensitive IQA so far. Meanwhile, the proposed model is
much simple than the CW-SSIM, which is efficient for applications.
