# -*- coding: utf-8 -*-
"""
Metrics
=======
Defines various metrics:
- :func:`colour.utilities.metric_mse`
- :func:`colour.utilities.metric_psnr`
References
----------
- :cite:`Wikipedia2004` : Wikipedia. (2004). Peak signal-to-noise ratio.
Retrieved March 5, 2018, from https://en.wikipedia.org/wiki/\
Peak_signal-to-noise_ratio
- :cite:`Wikipedia2003c` : Wikipedia. (2003). Mean squared error. Retrieved
March 5, 2018, from https://en.wikipedia.org/wiki/Mean_squared_error
"""
from __future__ import division, unicode_literals
import numpy as np
from colour.utilities import as_float_array
__author__ = 'Colour Developers'
__copyright__ = 'Copyright (C) 2013-2019 - Colour Developers'
__license__ = 'New BSD License - http://opensource.org/licenses/BSD-3-Clause'
__maintainer__ = 'Colour Developers'
__email__ = 'colour-science@googlegroups.com'
__status__ = 'Production'
__all__ = ['metric_mse', 'metric_psnr']
[docs]def metric_mse(a, b):
"""
Computes the mean squared error (MSE) or mean squared deviation (MSD)
between given *array_like* :math:`a` and :math:`b` variables.
Parameters
----------
a : array_like
:math:`a` variable.
b : array_like
:math:`b` variable.
Returns
-------
float
Mean squared error (MSE).
References
----------
:cite:`Wikipedia2003c`
Examples
--------
>>> a = np.array([0.48222001, 0.31654775, 0.22070353])
>>> b = a * 0.9
>>> metric_mse(a, b) # doctest: +ELLIPSIS
0.0012714...
"""
return np.mean((as_float_array(a) - as_float_array(b)) ** 2)
[docs]def metric_psnr(a, b, max_a=1):
"""
Computes the peak signal-to-noise ratio (PSNR) between given *array_like*
:math:`a` and :math:`b` variables.
Parameters
----------
a : array_like
:math:`a` variable.
b : array_like
:math:`b` variable.
max_a : numeric, optional
Maximum possible pixel value of the :math:`a` variable.
Returns
-------
float
Peak signal-to-noise ratio (PSNR).
References
----------
:cite:`Wikipedia2004`
Examples
--------
>>> a = np.array([0.48222001, 0.31654775, 0.22070353])
>>> b = a * 0.9
>>> metric_psnr(a, b) # doctest: +ELLIPSIS
28.9568515...
"""
return 10 * np.log10(max_a ** 2 / metric_mse(a, b))