Combination of Image Quality Scores Based on Particle Swarm Optimization
Yadanar Khaing, Yosuke Sugiura, Tetsuya Shimamura · 2018
In this paper, we propose a new combination technique for full-reference image quality assessment (IQA) by utilizing three better-recognized IQA methods. It gives the best performance for various databases. The parameter values employed in the new IQA score are optimized using the particle swarm optimization algorithm. In the combination approach, we firstly pick up the most appropriate IQA index for image quality databases and then add other two indices which have the most dissimilar features with the first index. By experiments, it is validated that the proposed method outperforms the other previous combination methods.