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Entropy-Based Combined Metric for Automatic Objective Quality Assessment of Stitched Panoramic Images.

Krzysztof Okarma1, Wojciech Chlewicki1, Mateusz Kopytek1

  • 1Department of Signal Processing and Multimedia Engineering, West Pomeranian University of Technology in Szczecin, 70-313 Szczecin, Poland.

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Summary

A new objective metric enhances stitched image quality assessment for virtual reality and remote sensing. This method combines existing features with image entropy analysis for improved accuracy.

Keywords:
image analysisimage entropyimage quality assessmentpanoramic imagesstitched images

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Area of Science:

  • Computer Vision
  • Image Processing
  • Remote Sensing

Background:

  • Stitched images are crucial for virtual reality and remote sensing applications.
  • Image quality degradation in stitched images arises from distortions like ghosting and blurring.
  • Existing image quality metrics are insufficient for the unique distortions in stitched images.

Purpose of the Study:

  • To develop a novel objective image quality metric tailored for stitched images.
  • To address the limitations of general-purpose image quality assessment in specific applications.
  • To improve the accuracy of evaluating panoramic image quality.

Main Methods:

  • A combined metric integrating features from recent methods.
  • Incorporation of local and global image entropy analysis.
  • Validation using the ISIQA database with subjective Mean Opinion Scores.

Main Results:

  • The proposed metric demonstrated improved correlation with subjective evaluations.
  • Significant enhancement in the accuracy of objective quality assessment.
  • Effective identification of quality degradation in stitched panoramic images.

Conclusions:

  • The developed metric offers a more reliable approach to assessing stitched image quality.
  • This advancement is vital for enhancing user experience in virtual reality and remote sensing.
  • The findings highlight the importance of specialized metrics for specific image types and applications.