Perception based Image Quality Evaluator (PIQE)

No-reference image quality assessment score for Python

Converted from and tested against the MATLAB function.

Based on the paper:

Venkatanath N, Praneeth D, Maruthi Chandrasekhar Bh, S. S. Channappayya and S. S. Medasani, "Blind image quality evaluation using perception based features," 2015 Twenty First National Conference on Communications (NCC), Mumbai, India, 2015, pp. 1-6, doi: 10.1109/NCC.2015.7084843.

Syntax

pip install pypiqe

from pypiqe import piqe

score, activityMask, noticeableArtifactMask, noiseMask = piqe(I)

Description

Examples

Input Arguments

Output Arguments

Algorithm

PIQE calculates the no-reference quality score for an image through block-wise distortion estimation, using these steps:

1. Compute the Mean Subtracted Contrast Normalized (MSCN) coefficient for each pixel in the image using the algorithm proposed by N. Venkatanath and others [1].
2. Divide the input image into nonoverlapping blocks of size 16-by-16.
3. Identify high spatially active blocks based on the variance of the MSCN coefficients.
4. Generate activityMask using the identified high spatially active blocks.
5. In each block, evaluate distortion due to blocking artifacts and noise using the MSCN coefficients.
6. Use threshold criteria to classify the blocks as distorted blocks with blocking artifacts, distorted blocks with Gaussian noise, and undistorted blocks.
7. Generate noticeableArtifactsMask from the distorted blocks with blocking artifacts and noiseMask from the distorted blocks with Gaussian noise.
8. Compute the PIQE score for the input image as the mean of scores in the distorted blocks.
9. The quality scale of the image based on its PIQE score is given in this table.