Image Error Measurements

version (123 KB) by Michael Chan
Measures the differences between 2 images, and measurement of image quality.


Updated 23 Nov 2010

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main executing reference usage: usage_errorMeasurementsOfImages.m
The objective is to measure the differences between 2 images, and measurement of image quality.
1. Mean squared error, MSE
2. Root Mean squared error, RMSE
3. Peak signal to signal noise ratio, PSNR
4. Mean absolute error, MAE
5. Signal to signal noise ratio, SNR
6. Universal Image Quality Index
7. Enhancement Measurement Error, EME
8. Pearson Correlation Coefficient

Sample output:
PSNR = +13.81915 dB
MSE = 108.53790
RMSE = 10.41815
Universal Image Quality Index = 0.16077
EME (original image) = 14.50599
EME (noisy image) = 8.48040
PearsonCorrelationCoefficient (originalImage vs noisyImage) = 30959.27033
PearsonCorrelationCoefficient (originalImage vs originalImage) = 50624.00000
SNR = -10.28091 dB
MAE = 19.82882

Caveat: For reference purposes.

The author also recommends:

The author appreciates suggestions and errata. Please do not hesitate to send suggestions and feedback for improvement for the framework construction to the email provided.


Thank you.

Michael Chan JT

Cite As

Michael Chan (2022). Image Error Measurements (, MATLAB Central File Exchange. Retrieved .

MATLAB Release Compatibility
Created with R2010a
Compatible with any release
Platform Compatibility
Windows macOS Linux

Inspired: EMEE

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