UIQI: A Comprehensive Quality Evaluation Index for Underwater Images

Yutao LIU, Ke GU, Jingchao CAO, Shiqi WANG, Guangtao ZHAI, Junyu DONG, Sam KWONG

Research output: Journal PublicationsJournal Article (refereed)peer-review


Due to the light absorption and scattering in waterbodies, acquired underwater images frequently suffer from color cast, blur, low contrast, noise, etc., which seriously degrade the image quality and affect their subsequent applications. Therefore, it is necessary to propose a reliable and practical underwater image quality assessment (IQA) model that can faithfully evaluate underwater image quality. To this end, in this paper, we establish a novel quality assessment model for underwater images by in-depth analysis and characterization of multiple image properties. Specifically, we propose characterizing the image luminance, color cast, sharpness, contrast, fog density and noise to comprehensively describe the image quality to evaluate the underwater image quality more accurately. Dedicated features are elaborately investigated to characterize those quality-aware image properties. After feature extraction, we employ support vector regression (SVR) to integrate all the quality-aware features and regress them onto the underwater image quality score. Extensive tests performed on standard underwater image quality databases demonstrate the superior prediction performance of the proposed underwater IQA model to state-of-the-art congeneric quality assessment models.

Original languageEnglish
Pages (from-to)1-15
Number of pages15
JournalIEEE Transactions on Multimedia
Publication statusE-pub ahead of print - 2 Aug 2023
Externally publishedYes

Bibliographical note

Publisher Copyright:


  • Colored noise
  • Feature extraction
  • Image color analysis
  • Image quality
  • image quality assessment (IQA)
  • Indexes
  • no-reference (NR)
  • objective metric
  • Predictive models
  • statistical modeling
  • Underwater image
  • Visualization


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