GES: a new image quality assessment metric based on energy features in Gabor transform domain

We propose Gabor energy similarity (GES), a new full reference image quality assessment metric based on the measuring of Gabor energy features of images. It has been recognized that: 1) 2D Gabor filters can attain the theoretical conjoint resolution limit in spatial and frequency domain defined by Heisenberg's uncertainty principle; 2) it is widely reported that simple cells in visual cortex can be well modeled by 2D Gabor functions; and 3) the feature of Gabor energy is closely related to the model of complex cells in primary visual cortex that are very sensitive to minor changes in nature scene. Motivated by these facts we attempt to design a new image quality assessment by exploring the similarity in Gabor energy functions between the original and the distorted image. The images are firstly decomposed by a filter bank consists of 48 Gabor filters (6 directions, 4 spatial resolutions, and symmetric or anti-symmetric). Then the local energy feature is extracted by computing the modulus of the responses of symmetric and anti-symmetric kernel filters at each point. We finally propose an image quality metric based on averaged cross correlation between two images. Extensive experimental results are used to justify the effectiveness of the proposed GES