A No-reference Image Quality Assessment Method for Real Foggy Images

The image quality assessment results for foggy images are of great significance in the objective measurement of image quality and the design and optimization of dehazing algorithm. Initially, to address the issue that there are few no-reference evaluation algorithms for foggy image quality in real scenes, this paper proposes a no-reference quality assessment method for foggy image quality in real scenes. Firstly, we establish a real scene foggy image database and evaluate it subjectively to obtain the mean opinion score (MOS). Then, we propose a feature selection method combining correlation coefficients and union ideas, which can pick out features positively correlated with haze image quality, to simplify the features without affecting the prediction accuracy of the model. Finally, we use the support vector regression method to learn the regression mapping between features and subjective scores of the foggy images, by which we can obtain the image quality assessment results. The experimental results on the database show that the algorithm in this paper is better than other algorithms. The objective image quality evaluation results of the proposed algorithm are in good agreement with the human eye's subjective perception results. Besides, the experimental results prove that the model in this paper has better performance in predicting the quality of the image after defogging.