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NITS-IQA Database: A New Image Quality Assessment Database.

Jayesh Ruikar1,2, Saurabh Chaudhury1

  • 1Department of Electrical Engineering, National Institute of Technology, Silchar 788010, India.

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A new NITS-IQA database was created for image quality assessment (IQA), featuring real camera images with diverse distortions. This resource aids in developing and validating advanced IQA algorithms.

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image databaseimage quality assessmentsubjective image quality assessment

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Area of Science:

  • Computer Vision
  • Image Processing
  • Multimedia Systems

Background:

  • Existing image quality assessment (IQA) databases often lack diverse, real-world camera distortions.
  • There is a need for comprehensive datasets that include natural images with various types and levels of degradation.

Purpose of the Study:

  • To introduce the NITS-IQA database, a novel resource for image quality assessment.
  • To detail the development process and subjective testing methodology for the database.
  • To evaluate the performance of a state-of-the-art IQA technique against the established Mean Opinion Scores (MOS).

Main Methods:

  • Development of the NITS-IQA database comprising 414 images (405 distorted, 9 original).
  • Inclusion of nine distortion types, each with five degradation levels.
  • Conducting a subjective test experiment to collect individual quality ratings.
  • Calculation of Mean Opinion Scores (MOS) from subjective ratings.

Main Results:

  • The NITS-IQA database provides a valuable benchmark for IQA algorithm development.
  • Analysis of Pearson, Spearman, and Kendall rank correlations between an IQA technique and MOS.
  • Demonstration of the database's utility in assessing IQA algorithm performance.

Conclusions:

  • The NITS-IQA database addresses the gap in existing resources for IQA research.
  • The database facilitates the development and validation of robust image quality assessment methods.
  • The presented analysis highlights the importance of comprehensive datasets in advancing IQA techniques.