pysparse.evaluation package¶
Submodules¶
pysparse.evaluation.error module¶
-
pysparse.evaluation.error.mse(o, r)[source]¶ Mean Squared Error
The Mean Squared Error (MSE) is expressed as
\[{\rm MSE} = \frac{1}{MN}\sum_{i=1}^{M}\sum_{j=0}^{N}[{\bm I}(i,j), \hat{\bm I}(i, j)]^2 \]Parameters: - o (ndarray) – Orignal signal matrix.
- r (ndarray) – Reconstructed signal matrix
Returns: MSE – Mean Squared Error
Return type:
-
pysparse.evaluation.error.rmse(o, r)[source]¶ Root Mean Squared Error
The Root Mean Squared Error (MSE) is expressed as
\[{\rm RMSE} = \sqrt{\frac{1}{MN}\sum_{i=1}^{M}\sum_{j=0}^{N}[{\bm I}(i,j), \hat{\bm I}(i, j)]^2} \]Parameters: - o (ndarray) – Orignal signal matrix.
- r (ndarray) – Reconstructed signal matrix
Returns: RMSE – Root Mean Squared Error
Return type:
pysparse.evaluation.snr module¶
-
pysparse.evaluation.snr.psnr(o, r, Vpeak=None, mode='simple')[source]¶ Peak Signal-to-Noise Ratio
The Peak Signal-to-Noise Ratio (PSNR) is expressed as
\[{\rm PSNR} = 10 \log10(\frac{V_{peak}^2}{\rm MSE}) \]For float data, \(V_{peak} = 1\);
For interges, \(V_{peak} = 2^{nbits}\), e.g. uint8: 255, uint16: 65535 …
Parameters: - o (array_like) – Reference data array. For image, it’s the original image.
- r (array_like) – The data to be compared. For image, it’s the reconstructed image.
- Vpeak (float, int or None, optional) – The peak value. If None, computes automaticly.
- mode (str or None, optional) – ‘simple’ or ‘rich’. ‘simple’ (default) –> just return psnr i.e. ‘rich’ –> return psnr, mse, Vpeak, imgtype.
Returns: PSNR – Peak Signal to Noise Ratio value.
Return type: