Blind multiply distorted image quality assessment using an ensemble random forest

In this paper, we propose an AdaBoost Random Forest (AdaBoost-RF) based blind multiply distorted image quality assessment (IQA) method. The AdaBoost-RF is an ensemble learning algorithm that uses the principle of the AdaBoost and random forest as Weak Learners. We also operate learning quality-aware features (LQAF) and utilize the AdaBoost-RF to get the image quality score by taking advantage of image features. The AdaBoost-RF increases the degree of difference between Weak Learners, in contrast to other regressions, and can gain higher predictive accuracy. On the LIVE multiply distorted image database (LIVEMD) and MDID2013, experimental results show our proposed model has a better performance than the other mainstream IQA methods, and is a useful and reliable method for image quality assessment.