Computer Science

Computer Science ›› 2021, Vol. 48 ›› Issue (2): 153-159.doi: 10.11896/jsjkx.200800188

• Computer Graphics & Multimedia • Previous Articles     Next Articles

Subset Ratio Dynamic Selection for Consistency Enhancement Evaluation

WANG Kai-xun1, LIU Hao1,2, SHEN Gang1, SHI Ting-ting1   

  1. 1 College of Information Science and Technology,Donghua University,Shanghai 201620,China
    2 Key Laboratory of Artificial Intelligence,Ministry of Education,Shanghai 200240,China
  • Received:2020-08-27 Revised:2020-09-25 Online:2021-02-15 Published:2021-02-04
  • About author:WANG Kai-xun,born in 1997,postgraduate,is a member of China Computer Federation.His main research interests include quality evaluation of underwater image set and so on.
    LIU Hao,born in 1977,associate professor,is a member of China Computer Federation.His main research interests include multimedia signal processing and intelligent sensing system.
  • Supported by:
    The Natural Science Foundation of Shanghai(18ZR1400300) and Foundation of Key Laboratory of Artificial Intelligence,Ministry of Education,P.R. China.

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Abstract: Due to poor imaging conditions,a lot of underwater images require the consistency enhancement.In the subset-guided consistency enhancement evaluation criterion,the existing subset selection methods need too much subset samples of 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 further divides the candidate samples into several sampling subsets.Based on a non-replacement 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 me-thods,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 be decreased by 2%~14% over that of the fixed ratio method,and be decreased by 3%~9% over that of the gradual addition method,and thus the complexity is robustly reduced during subset-guided consistency enhancement evaluation.

Key words: Candidate subset, Confidence level, Consistency enhancement, Dynamic selection, Underwater images

CLC Number: 

  • TP391.41
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