A Novel Approach to Image Assessment by Seeking Unification of Subjective and Objective Criteria Based on Supervised Learning

Image quality assessment is a challenge research topic in imaging engineering and applications, especially in the case where the reference image cannot be accessed, such as aerial images. In view of such an issue, a novel learning based evaluation approach was developed. In practice, only objective quality criteria usually cannot achieve desired result. Based on the analysis of multiple objective quality assessment criteria, a boosting algorithm with supervised learning, LassBoost (Learn to Assess with Boosting), was employed to seek the unification of the multiple objective criteria with subjective criteria. This new approach can effectively fuse multiple objective quality criteria guided by the subjective quality level such that the subjective /objective criteria can be unified using weighted regression method. The experimental results illustrate that the proposed method can achieve significantly better performance for image quality assessment, thus can provide a powerful decision support in imaging engineering and practical applications.