Non-Reference Blur Image Quality Evaluation Based on Saliency Object Classification

In recent years, there have been a large number of studies on the quality evaluation of non-reference blur images, but many methods ignore the influence of image content on the evaluation results. The blur evaluation methods of the no-saliency object image with pure background and the saliency object image with background are different. Based on the human attention mechanism, the former focuses on the overall blur of the image, while the latter focuses more on the local detail blur of the image. Overall blur refers to the sharpness information of the overall content of the image, while local detail blur refers to the local sharpness information of different locations of the image. The two can better combine visual salience and image content. To solve the above problems, this paper proposes a non-reference blur image quality evaluation method based on saliency object classification. Firstly, this paper proposes an object classification algorithm based on saliency detection, which classifies the saliency objects of the evaluation image, and extracts the local and global blur features according to the classification results. Finally, the two features are fused to obtain the final quality evaluation score. The experimental results show that the algorithm not only achieves the optimal evaluation effect on the BLUR database, but also has good results on the LIVE, CSIQ, and TID2013 databases, with good robustness. In addition, the algorithm in this paper also shows excellent statistical performance in various databases.