No-Reference image quality assessment using gradient-based structural integrity and latent noise estimation

Image quality assessment (IQA) plays a crucial role in monitoring quality control in image communication systems, and in benchmarking and optimizing parameters in enhancement algorithms. The full-reference IQA metrics require a good-quality reference image, obtaining which may not be practical in real-life applications. This paper, therefore, proposes a no-reference IQA metric based on the hypothesis that every image has latent additive white Gaussian noise (AWGN). A mathematical model was developed on a dataset of fifty test images by computing gradient-based structural similarity of corrupted images w.r.t. the original. Statistical modeling of the observations were found to fit an exponential parametric model. The standard deviation of the latent (or apparent) AWGN present in any image was estimated using an SVD-based approach. The proposed metric, referred to as the no-reference gradient-based structural integrity (NRGSI), is then computed by a simple backprojection of the estimated noise deviation on the exponential model. The accuracy of the proposed objective metric is characterized by its comparison with subjective quality scores given by ten subjects, and with a classical perceptual quality measure.