Statistical Metric Fusion for Image Quality Assessment

In this paper, we propose two novel Statistical Metric Fusion (SMF) methods for Image Quality Assessment (IQA) metric enhancement. First, local quality map is constructed from existing state-of-the-art IQA algorithm. After that several statistical indices are extracted from local quality map. Finally, the extracted statistical indices are fused by Supervised Statistical Metric Fusion (SMF-S) based on Support Vector Regression (SVR) and Unsupervised Statistical Metric Fusion (SMF-U) based on Reciprocal Rank Fusion (RRF) to obtain the final quality score, respectively. Experimental results on the largest public IQA database TID2013 have demonstrated that the two proposed SMF methods can generally enhance the quality prediction performance of the fused IQA metric in terms of high correlation with human opinion scores.