Chinese Journal of Liquid Crystals and Displays, Volume. 38, Issue 3, 356(2023)

No-reference image quality assessment based on feature tokenizer and Transformer

Wei SONG1、*, Jia-jin LI1, Xiao-chen LIU1, Zhi-xiang LIU1, and Shao-hua SHI2
Author Affiliations
  • 1College of Information,Shanghai Ocean University,Shanghai 201306,China
  • 2East China Sea Survey Center,State Oceanic Administration,Shanghai 200137,China
  • show less
    References(43)

    [13] ATHAR S, WANG Z L, WANG Z. Deep neural networks for blind image quality assessment: addressing the data challenge[J/OL]. arXiv, 12161(2109).

    [18] VASWANI A, SHAZEER N, PARMAR N et al. Attention is all you need[C], 6000-6010(2017).

    [20] DOSOVITSKIY A, BEYER L, KOLESNIKOV A et al. An image is worth 16×16 words: transformers for image recognition at scale[C](2021).

    [26] IOFFE S, SZEGEDY C. Batch normalization: accelerating deep network training by reducing internal covariate shift[C], 448-456(2015).

    [27] XIAO T T, SINGH M, MINTUN E et al. Early convolutions help transformers see better[C], 30392-30400(2021).

    [38] KINGMA D P, BA J. Adam: a method for stochastic optimization[C], 1-15(2015).

    Tools

    Get Citation

    Copy Citation Text

    Wei SONG, Jia-jin LI, Xiao-chen LIU, Zhi-xiang LIU, Shao-hua SHI. No-reference image quality assessment based on feature tokenizer and Transformer[J]. Chinese Journal of Liquid Crystals and Displays, 2023, 38(3): 356

    Download Citation

    EndNote(RIS)BibTexPlain Text
    Save article for my favorites
    Paper Information

    Category: Research Articles

    Received: Jun. 29, 2022

    Accepted: --

    Published Online: Apr. 3, 2023

    The Author Email: Wei SONG (wsong@shou.edu.cn)

    DOI:10.37188/CJLCD.2022-0220

    Topics