Optimized three scores combination for image quality assessment
Kei Ishiyama, Yosuke Sugiura, Tetsuya Shimamura · 2016
Digital images are distorted by a variety of processes. Thus we need objective image quality assessment (IQA) equivalent to subjective assessment. Several objective IQA methods have been proposed, and recently a combination method has been derived successfully. The combination technique is able to assess distorted images correctly. However, it is weak against Meanshift images. In this paper, we propose a new objective IQA method adding another IQA score to the original combination technique. Adjustable parameters included in the new IQA measure are optimized with the genetic algorithm. By experiments, it is validated that the proposed method provides a superior performance on various images including Meanshift.