No Reference Quality Assessment for Multiply-Distorted Images Based on an Improved Bag-of-Words Model

Multiple distortion assessment is a big challenge in image quality assessment (IQA). In this letter, a no reference IQA model for multiply-distorted images is proposed. The features, which are sensitive to each distortion type even in the presence of other distortions, are first selected from three kinds of NSS features. An improved Bag-of-Words (BoW) model is then applied to encode the selected features. Lastly, a simple yet effective linear combination is used to map the image features to the quality score. The combination weights are obtained through lasso regression. A series of experiments show that the feature selection strategy and the improved BoW model are effective in improving the accuracy of quality prediction for multiple distortion IQA. Compared with other algorithms, the proposed method delivers the best result for multiple distortion IQA.