No-reference image quality assessment using Gabor-based smoothness and latent noise estimation

Abstract

No-reference image quality assessment is a challenging task due to the absence of a reference image in practical situations to quantify image quality. This paper proposes a new no-reference image quality metric for natural images using latent noise estimation, Gabor response, and contrast deviation. The algorithm employs an extension of gradient-based SSIM into the no-reference application using SVD-based AWGN estimation, and defines attributes such as Gabor-based smoothness and contrast deviation. The proposed metric arrives at an overall quality score by computing a linear weighted summation of the three image attributes. The proposed algorithm has been tested on several public databases (i.e. LIVE, TID 2013 and CSIQ), and the overall results display a noteworthy correlation of nearly 80% with the human visual system.