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=0∑m−1j=0∑n−1(yi,j−y^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=0∑m−1j=0∑n−1(yi,j−y^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=2n−1
SSIM
- Structural SIMliarity(结构相似性)
- equation:
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