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

Xinpeng Huang, Xin Liu, Wenxin Ding, Chunli Meng and Ping An

PLOS ONE, 2022, vol. 17, issue 3, 1-13

Abstract: The distortion measurement plays an important role in panoramic image processing. Most measurement algorithms judge the panoramic image quality by means of weighting the quality of the local areas. However, such a calculation fails to globally reflect the quality of the panoramic image. Therefore, the multi-region adjacent pixels correlation (MRAPC) is proposed as the efficient feature for no-reference panoramic images quality assessment in this paper. Specifically, from the perspective of the statistical characteristics, the differences of the adjacent pixels in panoramic image are proved to be highly related to the degree of distortion and independent of image content. Besides, the difference map has limited pixel value range, which can improve the efficiency of quality assessment. Based on these advantages, the proposed MRAPC feature collaborates with the support vector regression to globally predict the quality of panoramic images. Extensive experimental results show that the proposed no-reference panoramic image quality assessment algorithm achieves higher evaluation performance than the existing algorithms.

Date: 2022
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Persistent link: https://EconPapers.repec.org/RePEc:plo:pone00:0266021

DOI: 10.1371/journal.pone.0266021

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