Deep Feature Statistics Mapping for Generalized Screen Content Image Quality Assessment

Baoliang CHEN, Hanwei ZHU, Lingyu ZHU, Shiqi WANG, Sam KWONG

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

Abstract

The statistical regularities of natural images, referred to as natural scene statistics, play an important role in no-reference image quality assessment. However, it has been widely acknowledged that screen content images (SCIs), which are typically computer generated, do not hold such statistics. Here we make the first attempt to learn the statistics of SCIs, based upon which the quality of SCIs can be effectively determined. The underlying mechanism of the proposed approach is based upon the mild assumption that the SCIs, which are not physically acquired, still obey certain statistics that could be understood in a learning fashion. We empirically show that the statistics deviation could be effectively leveraged in quality assessment, and the proposed method is superior when evaluated in different settings. Extensive experimental results demonstrate the Deep Feature Statistics based SCI Quality Assessment (DFSS-IQA) model delivers promising performance compared with existing NR-IQA models and shows a high generalization capability in the cross-dataset settings. The implementation of our method is publicly available at https://github.com/Baoliang93/DFSS-IQA.
Original languageEnglish
Pages (from-to)3227-3241
Number of pages15
JournalIEEE Transactions on Image Processing
Volume33
Early online date1 May 2024
DOIs
Publication statusPublished - 1 May 2024

Bibliographical note

Publisher Copyright:
IEEE

Keywords

  • Image quality assessment
  • distribution deviation
  • no-reference
  • scene statistics
  • screen content image

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