About

CPBD is a perceptual-based no-reference objective image sharpness metric based on the cumulative probability of blur detection developed at the Image, Video and Usability Laboratory of Arizona State University <https://ivulab.asu.edu/Quality/CPBD>__.

[The metric] is based on the study of human blur perception for
varying contrast values. The metric utilizes a probabilistic model
to estimate the probability of detecting blur at each edge in the
image, and then the information is pooled by computing the
cumulative probability of blur detection (CPBD).

This software is a Python port of the reference MATLAB implementation <http://lina.faculty.asu.edu/Software/CPBDM/CPBDM_Release_v1.0.zip>. To approximate the behaviour of MATLAB's proprietary implementation of the Sobel operator, it uses an implementation inspired by GNU Octave <https://sourceforge.net/p/octave/image/ci/default/tree/inst/edge.m#l196>.

References

CPBD is described in detail in the following papers:

• N. D. Narvekar and L. J. Karam, "A No-Reference Image Blur Metric Based on the Cumulative Probability of Blur Detection (CPBD)," in IEEE Transactions on Image Processing, vol. 20, no. 9, pp. 2678-2683, Sept. 2011. <http://ieeexplore.ieee.org/abstract/document/5739529/>__
• N. D. Narvekar and L. J. Karam, "An Improved No-Reference Sharpness Metric Based on the Probability of Blur Detection," International Workshop on Video Processing and Quality Metrics for Consumer Electronics (VPQM), January 2010, http://www.vpqm.org (pdf) <http://events.engineering.asu.edu/vpqm/vpqm10/Proceedings_VPQM2010/vpqm_p27.pdf>__
• N. D. Narvekar and L. J. Karam, "A no-reference perceptual image sharpness metric based on a cumulative probability of blur detection," 2009 International Workshop on Quality of Multimedia Experience, San Diego, CA, 2009, pp. 87-91. <http://ieeexplore.ieee.org/abstract/document/5246972/>__

Credits

If you publish research results using this code, I kindly ask you to reference the papers of the original authors of the metric as stated in the previous section as well as their reference implementation in your bibliography. See also the copyright statement of the reference implementation in the license file <https://raw.githubusercontent.com/0x64746b/python-cpbd/master/LICENSE.txt>__. Thank you!

Installation

::

$ pip install cpbd

Usage

::

In [1]: import cpbd

In [2]: from scipy import ndimage

In [3]: input_image = ndimage.imread('/tmp/LIVE_Images_GBlur/img4.bmp', mode='L')

In [4]: cpbd.compute(input_image)
Out[4]: 0.75343203230148048

Development

::

$ git clone [email protected]:0x64746b/python-cpbd.git
Cloning into 'python-cpbd'...
$ cd python-cpbd
$ pip install -U '.[dev]'

To quickly run the tests with the invocation interpreter:

::

$ python setup.py test

To test the library under different interpreters:

::

$ tox

Performance

The following graph visualizes the accuracy of this port in comparison with the reference implementation when tested on the images <http://lina.faculty.asu.edu/Software/CPBDM/LIVE_Images_GBlur.zip>__ of the LIVE database <http://live.ece.utexas.edu/research/quality/subjective.htm>__:

.. image:: https://raw.githubusercontent.com/0x64746b/python-cpbd/master/tests/data/performance_LIVE.png :alt: Performance on LIVE database