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Full-Reference Image Quality Assessment with Transformer and DISTS

Author

Listed:
  • Pei-Fen Tsai

    (Computer Science Department, National Yang Ming Chiao Tung University, No. 1001, Daxue Rd., Est Dist., Hsinchu City 300093, Taiwan)

  • Huai-Nan Peng

    (Computer Science Department, National Yang Ming Chiao Tung University, No. 1001, Daxue Rd., Est Dist., Hsinchu City 300093, Taiwan)

  • Chia-Hung Liao

    (Computer Science Department, National Yang Ming Chiao Tung University, No. 1001, Daxue Rd., Est Dist., Hsinchu City 300093, Taiwan)

  • Shyan-Ming Yuan

    (Computer Science Department, National Yang Ming Chiao Tung University, No. 1001, Daxue Rd., Est Dist., Hsinchu City 300093, Taiwan)

Abstract

To improve data transmission efficiency, image compression is a commonly used method with the disadvantage of accompanying image distortion. There are many image restoration (IR) algorithms, and one of the most advanced algorithms is the generative adversarial network (GAN)-based method with a high correlation to the human visual system (HVS). To evaluate the performance of GAN-based IR algorithms, we proposed an ensemble image quality assessment (IQA) called ATDIQA (Auxiliary Transformer with DISTS IQA) to give weights on multiscale features global self-attention transformers and local features of convolutional neural network (CNN) IQA of DISTS. The result not only performed better on the perceptual image processing algorithms (PIPAL) dataset with images by GAN IR algorithms but also has good model generalization over LIVE and TID2013 as traditional distorted image datasets. The ATDIQA ensemble successfully demonstrates its performance with a high correlation with the human judgment score of distorted images.

Suggested Citation

  • Pei-Fen Tsai & Huai-Nan Peng & Chia-Hung Liao & Shyan-Ming Yuan, 2023. "Full-Reference Image Quality Assessment with Transformer and DISTS," Mathematics, MDPI, vol. 11(7), pages 1-15, March.
    • Handle: RePEc:gam:jmathe:v:11:y:2023:i:7:p:1599-:d:1107616
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    References listed on IDEAS

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    1. Xining Zhu & Lin Zhang & Lijun Zhang & Xiao Liu & Ying Shen & Shengjie Zhao, 2020. "GAN-Based Image Super-Resolution with a Novel Quality Loss," Mathematical Problems in Engineering, Hindawi, vol. 2020, pages 1-12, February.
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