Perception based Image Quality Evaluator
No-Reference Quality Metrics
Description
The Perception based Image Quality Evaluator (PIQE) metric is opinion-unaware and unsupervised, which means it does not require a trained model. PIQE can measure the quality of images with arbitrary distortion and in most cases performs similar to NIQE. PIQE estimates block-wise distortion and measures the local variance of perceptibly distorted blocks to compute the quality score. (Source: Mathworks)
Interpretation
The lower the PIQE score, the better. A PIQE value of 0 is the best.
Limits
PIQE is less computationally efficient than NIQE or BRISQUE, but it provides local measures of quality in addition to a global quality score.
Example
Images from a traffic surveillance camera in Germany is used to show the PIQE results.
Standard image with PIQE score 72.81
Dark image with PIQE score 73.83
Dark image with PIQE score 75,73
Tools and Libraries
Python
There is one python repository implementing PIQE.
MATLAB
The MATLAB Image Processing Toolbox contains a function to calculate the PIQE score:
standard = imread('Reference_Image.png');
dark = imread('Image_Dark.png');
sun = imread('Image_Sunshine.png');
score = piqe(standard);
fprintf('\nThe PIQE score for the standard image %0.4f\n', score);
score = niqe(dark);
fprintf('\nThe PIQE score for the dark image %0.4f\n', score);
score = niqe(sun);
fprintf('\nThe PIQE score for the sunny image %0.4f\n', score);
A detailed description can be found at the Mathworks Website.