FSIM

FSIM: A feature similarity index for image quality assessment. Image quality assessment (IQA) aims to use computational models to measure the image quality consistently with subjective evaluations. The well-known structural similarity index brings IQA from pixel- to structure-based stage. In this paper, a novel feature similarity (FSIM) index for full reference IQA is proposed based on the fact that human visual system (HVS) understands an image mainly according to its low-level features. Specifically, the phase congruency (PC), which is a dimensionless measure of the significance of a local structure, is used as the primary feature in FSIM. Considering that PC is contrast invariant while the contrast information does affect HVS’ perception of image quality, the image gradient magnitude (GM) is employed as the secondary feature in FSIM. PC and GM play complementary roles in characterizing the image local quality. After obtaining the local quality map, we use PC again as a weighting function to derive a single quality score. Extensive experiments performed on six benchmark IQA databases demonstrate that FSIM can achieve much higher consistency with the subjective evaluations than state-of-the-art IQA metrics


References in zbMATH (referenced in 27 articles )

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  1. Guo, Yumeng; Zeng, Li; Wang, Jiaxi; Shen, Zhaoqiang: Image reconstruction method for exterior circular cone-beam CT based on weighted directional total variation in cylindrical coordinates (2020)
  2. Jin, Lianghai; Song, Enmin; Zhang, Wenhua: Denoising color images based on local orientation estimation and CNN classifier (2020)
  3. Li, Yunyi; Liu, Li; Zhao, Yu; Cheng, Xiefeng; Gui, Guan: Nonconvex nonsmooth low-rank minimization for generalized image compressed sensing via group sparse representation (2020)
  4. Wang, Yugang; Huang, Ting-Zhu; Zhao, Xi-Le; Jiang, Tai-Xiang: Video deraining via nonlocal low-rank regularization (2020)
  5. Huang, Jianping; Wang, Lihui; Zhu, Yuemin: Compressed sensing MRI reconstruction with multiple sparsity constraints on radial sampling (2019)
  6. Zhou, Wujie: Blind stereo image quality evaluation based on convolutional network and saliency weighting (2019)
  7. Zhou, Yu; Guo, Hainan: Collaborative block compressed sensing reconstruction with dual-domain sparse representation (2019)
  8. Chen, Yong; Huang, Ting-Zhu; Zhao, Xi-Le; Deng, Liang-Jian: Hyperspectral image restoration using framelet-regularized low-rank nonnegative matrix factorization (2018)
  9. Geng, Tianyu; Sun, Guiling; Xu, Yi; He, Jingfei: Truncated nuclear norm minimization based group sparse representation for image restoration (2018)
  10. Li, Bin; Shen, Chenyang; Chi, Yujie; Yang, Ming; Lou, Yifei; Zhou, Linghong; Jia, Xun: Multienergy cone-beam computed tomography reconstruction with a spatial spectral nonlocal means algorithm (2018)
  11. Wu, Meiyin; Chen, Li; Tian, Jing: A hybrid learning-based framework for blind image quality assessment (2018)
  12. Wu, Weiwen; Zhang, Yanbo; Wang, Qian; Liu, Fenglin; Luo, Fulin; Yu, Hengyong: Spatial-spectral cube matching frame for spectral CT reconstruction (2018)
  13. Yuan, Jianjun: An improved variational model for denoising magnetic resonance images (2018)
  14. Zhang, Yan-Shan; Zhang, Feng; Li, Bing-Zhao: Image restoration method based on fractional variable order differential (2018)
  15. Brunet, Dominique; Channappayya, Sumohana S.; Wang, Zhou; Vrscay, Edward R.; Bovik, Alan C.: Optimizing image quality (2017)
  16. Guo, Yumeng; Zeng, Li; Wang, Chengxiang; Zhang, Lingli: Image reconstruction model for the exterior problem of computed tomography based on weighted directional total variation (2017)
  17. Khodabakhshi Rafsanjani, Hossein; Sedaaghi, Mohammad Hossein; Saryazdi, Saeid: Efficient diffusion coefficient for image denoising (2016)
  18. Li, Ming; Xiao, Di; Zhang, Yushu: Attack and improvement of the fidelity preserved fragile watermarking of digital images (2016)
  19. Pistonesi, Silvina; Martinez, Jorge; Ojeda, Silvia M.: A sensibility study of the autobinomial model estimation methods based on a feature similarity index (2016)
  20. Zemliachenko, Alexander; Lukin, Vladimir; Ponomarenko, Nikolay; Egiazarian, Karen; Astola, Jaakko: Still image/video frame lossy compression providing a desired visual quality (2016)

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