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Blind Panoramic Image Quality Assessment Based on Project-Weighted Local Binary Pattern

  • Yumeng Xia
  • Yongfang WangEmail author
  • Peng Ye
Conference paper
  • 43 Downloads
Part of the Communications in Computer and Information Science book series (CCIS, volume 1181)

Abstract

The majority of existing objective panoramic image quality assessment algorithms are based on peak signal to noise ratio (PSNR) or structural similarity (SSIM). However, they are not highly consistent with human perception. In this paper, a new blind panoramic image quality assessment metric is proposed based on project-weighted gradient local binary pattern histogram (PWGLBP), which explores the structure degradation in sphere by combining with the nonlinear transformation relationship between the projected plane and sphere. Finally, support vector regression (SVR) is adopted to learn a quality predictor from feature space to quality score space. The experimental results demonstrate the superiority of our proposed metric compared with state-of-the-art objective PIQA methods.

Keywords

Panoramic image Blind quality assessment Local binary pattern Nonlinear transformation Support vector regression 

Notes

Acknowledgment

This work was supported by Natural Science Foundation of China under Grant No. 61671283, 61301113.

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Copyright information

© Springer Nature Singapore Pte Ltd. 2020

Authors and Affiliations

  1. 1.Shanghai Institute for Advanced Communication and Data ScienceShanghai UniversityShanghaiChina
  2. 2.School of Communication and Information EngineeringShanghai UniversityShanghaiChina

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