psnr-hvsm 0.0.7

Accelerated implementation of the PSNR-HVS-M image metric

psnr_hvsm

Accelerated Python package for computing the PSNR-HVS-M image metric.

This is an implementation of the PSNR-HVS and PSNR-HVS-M metrics developed by Nikolay Ponomarenko.

Bibliography

• Egiazarian, Karen, et al. "New full-reference quality metrics based on HVS." Proceedings of the Second International Workshop on Video Processing and Quality Metrics. Vol. 4. 2006.
• Ponomarenko, Nikolay, et al. "On between-coefficient contrast masking of DCT basis functions." Proceedings of the third international workshop on video processing and quality metrics. Vol. 4. 2007.

Installation

psnr_hvsm supports Python 3.6-3.9. Packages are distributed on PyPi. Be sure to have an up-to-date pip to be able to install the correct packages on Linux:

python -m pip install --upgrade pip

pip install psnr_hvsm

Usage

Command line

Command line support is an extra that pulls opencv-python-headless:

pip install psnr_hvsm[command_line]

python -m psnr_hvsm original.png distorted.png

As a library

The function psnr_hvs_hvsm accepts images as single-channel floating-point NumPy arrays. The images need to be normalised, i.e. the values need to be in the range [0,1]. This can be achieved by converting the image to float and dividing by the maximum value given the bit depth. For 8 bits per component this is 255.

The images must be padded to a multiple of 8 in each dimension.

import cv2
from psnr_hvsm import psnr_hvs_hvsm

image1 = cv2.imread('tests/baboon.png', cv2.IMREAD_GRAYSCALE).astype(float) / 255
image2 = cv2.imread('tests/baboon_msk.png', cv2.IMREAD_GRAYSCALE).astype(float) / 255

psnr_hvs, psnr_hvsm = psnr_hvs_hvsm(image1, image2)

print(psnr_hvs, psnr_hvsm)

34.427054505764424 51.64722121999962

If you need to measure PSNR-HVS and PSNR-HVS-M on an RGB image, you need to convert it to an YUV colorspace and pass in only the luma component.

Other exported functions

• psnr_hvs - returns only the PSNR-HVS value
• psnr_hvsm - returns only the PSNR-HVS-M value
• hvs_mse_tile - compute the weighted MSE of two 8x8 tiles
• hvsm_mse_tile - compute the weighted MSE with masking correction of two 8x8 tiles
• hvs_mse - compute HVS scores on all 8x8 tiles in the images, returns an array of numbers
• hvsm_mse - compute HVS-M scores on all 8x8 tiles in the images, returns an array of numbers

Building

Dependencies

psnr_hvsm has several dependencies:

• FFTW3 >= 3.3.9
• pybind11
• xtensor
• xtensor-python

All of the above can be automatically resolved by running deps.ps1, which is a cross-platform PowerShell script (i.e. it can also be run under Linux if you have PowerShell installed).

Development mode

To install in development mode:

pip install --upgrade -r requirements.txt

Creating Python wheel

pip install --upgrade -r requirements-build.txt
python setup.py bdist_wheel

Running tests on different versions of Python using tox

pip install --upgrade -r requirements-tox.txt
tox --parallel auto