A photo-based quality assessment model for the estimation of PM2.5 concentrations

Abstract

Rapid economic growth has caused severe environmental pollution, which has aroused great concern. This pollution affects public health and impairs visibility, therefore, it should be given greater consideration. In this paper, a photo-based PM2.5 concentration predictor is proposed based on the natural scene statistics without artificial assistance or extra information. Given that the quality of PM2.5 concentration images is determined by many factors, three types of influencing factors are analysed: the colourfulness, the structural degradation and the contrast. The first feature consists of the hue, saturation and colour descriptors, which measure the colourfulness of the PM2.5 concentration images. The second feature is determined based on the contrast can effectively portray the quality of PM2.5 concentration in the images. The third feature is extracted based on the natural scene statistics model, which measures the local and global structural degradation information and the naturalness of the PM2.5 concentration images. Finally, the three features are used to train a random forest model that can be used to predict the concentration of PM2.5. Experimental results illustrate that the performance of the proposed model is better than those of popular competitors on AQID.