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Perceptual Blind Panoramic Image Quality Assessment Based on Super-Pixel

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Digital Multimedia Communications (IFTC 2023)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 2067))

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Abstract

Blind objective quality assessment of panoramic images (PIQA) is a great challenge to perform highly consistent with human perception without the original panoramic images. In this paper, we propose a perceptual blind PIQA method based super-pixel, which exploits the equirectangular projection (ERP) and human perception characteristics for panoramic image to boost up the quality assessment performance. In particular, in order to make use of the local features of panoramic image, panoramic weights based on super-pixel is designed by combining ERP format and human perception. In addition, we propose panoramic-weighted structural features to predict the visual quality of panoramic images, which can reflect spherical quality accurately. Finally, we fuse and map extracted features into quality scores by applying support vector regression (SVR). The experiments demonstrate the effectiveness and superiority of our proposed metric compared with state-of-the-art PIQA methods on the public panoramic image datasets.

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Acknowledgments

This work was supported by National Natural Science Foundation of China under Grant No. 61671283, U2033218.

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Authors and Affiliations

  1. School of Communication and Information Engineering, Shanghai University, Shanghai, 200444, China

    Shuyu **ao, Yongfang Wang & Yinhan Wang

  2. School of Computer Science and Technology, Donghua University, Shanghai, 201620, China

    Zhijun Fang

Authors
  1. Shuyu **ao
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  2. Yongfang Wang
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  3. Yinhan Wang
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  4. Zhijun Fang
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Corresponding author

Correspondence to Yinhan Wang .

Editor information

Editors and Affiliations

  1. Shanghai Jiao Tong Univeristy, Shanghai, China

    Guangtao Zhai

  2. Shanghai Jiao Tong University, Shanghai, China

    Jun Zhou

  3. Communication University of China, Bei**g, China

    Long Ye

  4. Shanghai Jiao Tong University, Shanghai, China

    Hua Yang

  5. Shanghai University, Shanghai, China

    ** An

  6. Shanghai Jiao Tong University, Shanghai, China

    **aokang Yang

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**ao, S., Wang, Y., Wang, Y., Fang, Z. (2024). Perceptual Blind Panoramic Image Quality Assessment Based on Super-Pixel. In: Zhai, G., Zhou, J., Ye, L., Yang, H., An, P., Yang, X. (eds) Digital Multimedia Communications. IFTC 2023. Communications in Computer and Information Science, vol 2067. Springer, Singapore. https://doi.org/10.1007/978-981-97-3626-3_3

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