Image quality assessment: Difference between revisions
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==Standard Images and Video== | ==Standard Images and Video== | ||
\(\DeclareMathOperator{\mean}{mean}\) | |||
* MSE | * MSE | ||
*:\[MSE = \mean((I_1 - I_2)^2)\] | |||
* PSNR | * PSNR | ||
*:\[PSNR=10\log_{10}(\frac{R^2}{MSE})\] | |||
**where R^2 is the maximum fluctuation (e.g. 1.0 for [0-1] float images, 255 for uint8). | |||
* SSIM | * SSIM | ||
Revision as of 12:40, 27 August 2020
Methods for Image quality assessment
The standard metrics are mean-squared error, peak signal to noise ratio (psnr), and structural similarity (ssim).
Standard Images and Video
\(\DeclareMathOperator{\mean}{mean}\)
- MSE
- \[MSE = \mean((I_1 - I_2)^2)\]
- PSNR
- \[PSNR=10\log_{10}(\frac{R^2}{MSE})\]
- where R^2 is the maximum fluctuation (e.g. 1.0 for [0-1] float images, 255 for uint8).
- SSIM
Foveated Quality Assessment
- Lee et al.[1] propose Foveated signal to noise ratio (FSNR) which measures the signal to noise ratio in a curvilinear space. However they do not provide the exact equations to compute the curvilienar space.
Spherical Quality Assessment
- Yu et al.[2] propose Spherical PSNR (SPSNR) and WSPSNR.