An Image Quality Metric Based on Biologically Inspired Feature Model

Objective image quality assessment (IQA) metrics have been widely applied to imaging systems to preserve and enhance the perceptual quality of images being processed and transmitted. In this paper, we present a novel IQA metric based on biologically inspired feature model (BIFM) and structural similarity index (SSIM). The SSIM index map is first generated through the well-known IQA metric SSIM between the reference image and the distorted image. Then, saliency map of the distorted image is extracted via BIF to define the most salient image locations. Finally, according to the saliency map, a feature weighting model is employed to define the different weights for the different samples in the SSIM index map. Experimental results confirm that the proposed IQA metric improves the performance over PSNR and SSIM under various distortion types in terms of different evaluation criteria.