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No-reference panoramic image quality assessment based on multi-region adjacent pixels correlation.

Xinpeng Huang1, Xin Liu1, Wenxin Ding1

  • 1Shanghai Institute for Advanced Communication and Data Science, School of Communication and Information Engineering, Shanghai University, Shanghai, China.

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Summary
This summary is machine-generated.

This study introduces a new method for assessing panoramic image quality without a reference image. The multi-region adjacent pixels correlation (MRAPC) feature effectively measures distortion for better quality evaluation.

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Area of Science:

  • Computer Vision
  • Image Processing
  • Signal Processing

Background:

  • Panoramic image quality assessment is crucial for image processing.
  • Current methods often rely on local area weighting, failing to capture global image quality.
  • A need exists for efficient, no-reference quality assessment methods.

Purpose of the Study:

  • To propose a novel feature, multi-region adjacent pixels correlation (MRAPC), for no-reference panoramic image quality assessment.
  • To demonstrate the effectiveness of MRAPC in globally reflecting panoramic image quality.
  • To improve the efficiency and accuracy of panoramic image quality evaluation.

Main Methods:

  • Utilizing statistical characteristics of adjacent pixel differences in panoramic images.
  • Developing the multi-region adjacent pixels correlation (MRAPC) feature.
  • Employing support vector regression for global quality prediction based on MRAPC.

Main Results:

  • Adjacent pixel differences are highly correlated with distortion and independent of image content.
  • The MRAPC feature offers improved efficiency due to a limited pixel value range.
  • The proposed algorithm demonstrates superior performance compared to existing no-reference methods.

Conclusions:

  • The MRAPC feature is an efficient and effective tool for no-reference panoramic image quality assessment.
  • Global quality prediction is more accurate when considering MRAPC.
  • The proposed method advances the field of panoramic image quality evaluation.