Learning no-reference quality metric by examples

In this paper, a novel learning based method is proposed for No-Reference image quality assessment. Instead of examining the exact prior knowledge for the given type of distortion and finding a suitable way to represent it, our method aims to directly get the quality metric by means of learning. At first, some training examples are prepared for both high-quality and low-quality classes; then a binary classifier is built on the training set; finally the quality metric of an un-labeled example is denoted by the extent to which it belongs to these two classes. Different schemes to acquire examples from a given image, to build the binary classifier and to model the quality metric are proposed and investigated. While most existing methods are tailored for some specific distortion type, the proposed method might provide a general solution for No-Reference image quality assessment. Experimental results on JPEG and JPEG2000 compressed images validate the effectiveness of the proposed method.