No-reference image quality assessment using bag-of-features with feature selection

The aim of no-reference image quality assessment (NR-IQA) is to assess the quality of an image, which is consistent with the mean opinion score, without any prior knowledge about the reference image. This work proposes a new NR-IQA technique based on natural scene statistics properties of the bag-of-features representation and feature selection algorithms. The proposed bag-of-features technique utilizes Harris affine detector and scale invariant feature transform to compute points, which are clustered using the k-means clustering algorithm to extract features for IQA. The extracted features are utilized with a support vector regression model to assess the quality of the image. The proposed technique outperforms state-of-the-art NR-IQA techniques, when tested on three commonly used subjective image quality assessment databases. The experimental results have shown that the features extracted using the proposed technique are database independent and shows high correlation with the mean opinion score.