Subset Ratio Dynamic Selection for Consistency Enhancement Evaluation

Abstract

Due to poor imaging conditions, large-scale underwater images need the consistency enhancement. According to the subset-guided consistency enhancement evaluation criterion, the existing subset selection methods need too much candidate samples from a whole imageset without any adaptation on data content. Therefore, this paper proposes a subset ratio dynamic selection method for consistency enhancement evaluation. The proposed method firstly divides the candidate samples into several sampling subsets. Based on a non-return sampling strategy, the consistency enhancement degree of an enhancement algorithm is obtained for each sampling subset. By using the student-t distribution under a certain confidence level, the proposed method can adaptively determine the subset ratio for a whole imageset, and the candidate subset is used to predict the consistency enhancement degree of the enhancement algorithm on the whole imageset. Experimental results show that as compared with the existing subset selection methods, the proposed method can reduce the subset ratio in all cases, and correctly judge the consistency performance of each enhancement algorithm. With similar evaluation error, the subset ratio of the proposed method can decrease by 2%~20% over that of the subset fixed ratio method, and decrease by 3%~13% over that of the subset gradual addition method, and thus the complexity is reduced for subset-guided consistency enhancement evaluation.