Feature selection algorithm for no-reference image quality assessment using natural scene statistics

Images play an essential part in our daily lives and the performance of various imaging applications is dependent on the user's quality of experience. No-reference image quality assessment (NR-IQA) has gained importance to assess the perceived quality, without using any prior information of the nondistorted version of the image. Different NR-IQA techniques that utilize natural scene statistics classify the distortion type based on groups of features and then these features are used for estimating the image quality score. However, every type of distortion has a different impact on certain sets of features. In this paper, a new feature selection algorithm is proposed for distortion identification based image verity and integration evaluation that selects distinct feature groups for each distortion type. The selection procedure is based on the contribution of each feature on the Spearman rank order correlation constant (SROCC) score. Only those feature groups are used in the prediction model that have majority features with SROCC score greater than mean SROCC score of all the features. The proposed feature selection algorithm for NR-IQA shows better performance in comparison to state-of-the-art NR-IQA techniques and other feature selection algorithms when evaluated on three commonly used databases.