常用的图像质量评估指标

文章目录

  • MSE
  • RMSE
  • PSNR
  • SSIM

本文针对二维数据,如图像等

MSE

  • Mean Square Error(均方差)
  • equation:
    M S E = 1 m n ∑ i = 0 m − 1 ∑ j = 0 n − 1 ( y i , j − y ^ i , j ) 2 MSE = \frac{1}{mn} \sum_{i=0}^{m-1}\sum_{j=0}^{n-1}(y_{i,j}-\hat{y}_{i,j})^2 MSE=mn1i=0m1j=0n1(yi,jy^i,j)2

RMSE

  • Root Mean Square Error(均方根误差)
  • equation:
    R M S E = 1 m n ∑ i = 0 m − 1 ∑ j = 0 n − 1 ( y i , j − y ^ i , j ) 2 RMSE = \sqrt{\frac{1}{mn} \sum_{i=0}^{m-1}\sum_{j=0}^{n-1}(y_{i,j}-\hat{y}_{i,j})^2} RMSE=mn1i=0m1j=0n1(yi,jy^i,j)2

PSNR

  • Peak Signal to Noise Ratio(峰值信噪比)
  • equation:
    P S N R = 10 × l o g 10 ( M A X I 2 M S E ) M A X I = 2 n − 1 PSNR = 10×log_{10}(\frac{MAX_{I}^2}{MSE}) \\ MAX_{I}=2^{n}-1 PSNR=10×log10(MSEMAXI2)MAXI=2n1

SSIM

  • Structural SIMliarity(结构相似性)
  • equation:
    在这里插入图片描述
    在这里插入图片描述

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