Modeling Image Quality Score Distribution Using Alpha Stable Model

Abstract

In recent years, image quality is generally described by a mean opinion score (MOS). However, we observe that an image’s quality ratings given by a group of subjects may not follow a Gaussian distribution and the image quality can not be fully described by a MOS. In this paper, we propose to describe the image quality using a parameterized distribution rather than a MOS, and an objective method is also proposed to predict the image quality score distribution (IQSD). Specifically, we selected 100 images from the LIVE database and invited a large group of subjects to evaluate the quality of these images. By analyzing the subjective quality ratings, we find that the IQSD can be well modeled by an alpha stable model and this model can reflect much more information than MOS. Therefore, we propose an algorithm to model the IQSD described by an alpha stable model. Features are extracted from images based on natural scene statistics and support vector regressors are trained to predict the IQSD described by an alpha stable model. We validate the proposed IQSD prediction model on the collected subjective quality ratings. Experimental results verify the effectiveness of the proposed algorithm in modeling the IQSD.