cpbd 1.0.7

关于

cpbd是一种基于感知的无参考目标图像清晰度度量。 基于模糊检测的累积概率developed at the Image, Video and Usability Laboratory of Arizona State University。

[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).

这个软件是reference MATLAB implementation的python端口。 近似MATLAB的专有实现的行为 sobel运算符，它使用一个实现inspired by GNU Octave。

参考文献

cpbd在下面的文章中有详细的描述：

• 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.
• 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)
• 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.

学分

如果您使用此代码发布研究结果，我恳请您 参考原始作者的论文 上一节以及它们在 参考文献。另见参考文献的版权声明 在license file中实现。 谢谢您！

安装

$ pip install cpbd

用法

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

开发

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

使用调用解释器快速运行测试：

$ python setup.py test

在不同的解释器下测试库：

$ tox

性能

下图显示了此端口的准确性 当在 images 在LIVE database：