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People have different preferences for image contrast. This study found three distinct groups of observers based on their preferred contrast levels, aiding personalized image quality assessment research.

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Area of Science:

  • Computer Vision
  • Human-Computer Interaction
  • Image Processing

Background:

  • Image quality assessment (IQA) traditionally uses objective metrics.
  • Personalized IQA acknowledges individual observer differences.
  • Understanding user preferences for image attributes like contrast is crucial for personalized IQA.

Purpose of the Study:

  • To investigate individual differences in contrast preferences for natural images.
  • To identify distinct groups of observers based on their contrast preferences.
  • To create a publicly available database of contrast preferences for future research.

Main Methods:

  • An in-lab experiment was conducted with 22 observers.
  • Observers assessed 499 natural images, providing contrast level preferences.
  • A three-alternative forced choice method with a modified adaptive staircase algorithm was employed.

Main Results:

  • Observers were clustered into three distinct groups: low contrast, natural contrast, and high contrast preference.
  • Significant individual variations in contrast preferences were observed.
  • A database of 10,978 contrast preference values was compiled.

Conclusions:

  • Individual contrast preferences exist and can be categorized.
  • These findings support the development of personalized image quality assessment systems.
  • The released database will enable further research into viewer-specific image perception.