Why my value of MSE and PSNR have 3 values?
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Atifah Samuri
el 30 de Dic. de 2021
My code is shown below:
InputImage=imread('ifa.jpg');
ReconstructedImage=imread('ifamedifilt.jpg');
n=size(InputImage);
M=n(1);
N=n(2);
MSE = sum(sum((InputImage-ReconstructedImage).^2))/(M*N);
PSNR = 10*log10(256*256/MSE);
fprintf('\nMSE: %7.2f ', MSE);
fprintf('\nPSNR: %9.7f dB', PSNR);
and these are the values:
MSE: 0.88
MSE: 7.45
MSE: 1.94
PSNR: 48.7440467 dB
PSNR: 39.4403644 dB
PSNR: 45.2839345 dB
First image is ifa.jpg and the next one is ifamedifilt.jpg
![](https://www.mathworks.com/matlabcentral/answers/uploaded_files/848510/image.jpeg)
![](https://www.mathworks.com/matlabcentral/answers/uploaded_files/848515/image.jpeg)
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DGM
el 31 de Dic. de 2021
Editada: DGM
el 31 de Dic. de 2021
The images are RGB. The results are 1x1x3 -- one result per channel.
If your goal were to get a single value for the whole image, you can do that.
MSE = sum((InputImage(:)-ReconstructedImage(:)).^2)/(M*N);
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DGM
el 31 de Dic. de 2021
Editada: DGM
el 31 de Dic. de 2021
That's taking the MSE of all elements of the array when compared to the corresponding element in the other array. It does it by reshaping the arrays into vectors. You could do the same thing by doing
MSE = sum((InputImage-ReconstructedImage).^2,'all')/(M*N);
Either way, the point is to reduce the numerator to a scalar. It's not picking one particular channel, nor is it converting to grayscale. An error in the blue channel has the same weight as an error in red or green, which is not the case when doing luma conversion with rgb2gray(). It all depends what's desired.
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yanqi liu
el 31 de Dic. de 2021
Editada: yanqi liu
el 31 de Dic. de 2021
yes,sir,it is rgb type,may be use rgb2gray to make it gray type,such as
InputImage=double(rgb2gray(imread('https://www.mathworks.com/matlabcentral/answers/uploaded_files/848510/image.jpeg')));
ReconstructedImage=double(rgb2gray(imread('https://www.mathworks.com/matlabcentral/answers/uploaded_files/848515/image.jpeg')));
n=size(InputImage);
M=n(1);
N=n(2);
MSE = sum(sum((InputImage-ReconstructedImage).^2))/(M*N);
PSNR = 10*log10(256*256/MSE);
fprintf('\nMSE: %7.2f ', MSE);
fprintf('\nPSNR: %9.7f dB', PSNR);
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