PSNR-HVS-M-for-python

A python implementation of PSNR that takes the Human visual system into account. This code is based on Nikolay Ponomarenko matlab implementation of the PSNR-HVS & PSNR-m algorithm.

PSNR-HVS-M is Peak Signal to Noise Ratio taking into account Contrast Sensitivity Function (CSF) and between-coefficient contrast masking of DCT basis functions.

Ported by :

Eduardo Prado (t2ac32)

Original work by:

Nikolay Ponomarenko http://www.ponomarenko.info/psnrhvsm.htm

Installation

Clone the repository or dowload the zip. Add the 'psnrhvsm' folder to your project

In you .py file imports add this line.

'from psnrhvsm import psnrhvsm'

Quick test:

Or use the jupyter notebook included in the main folder the test images used in the paper are also included.

USAGE:

Input :

1. img1: the first image being compared
2. img2: the second image being compared
3. wstep: step of 8x8 window to calculate DCT coefficients. Default value is 8.

Output:

• p_hvs_m: the PSNR-HVS-M value between 2 images.
  • If one of the images being compared is regarded as perfect quality, then PSNR-HVS-M can be considered as the quality measure of the other image.
  • If compared images are visually undistingwished,then PSNR-HVS-M = 100000.
• p_hvs: the PSNR-HVS value between 2 images.

Default Usage:

       Given 2 test images img1 and img2, whose dynamic range is 0-255
    
       [p_hvs_m, p_hvs] = psnrhvsm(img1, img2);

See the results:

         p_hvs_m  # Gives the PSNR-HVS-M value
         p_hvs    # Gives the PSNR-HVS value

Descriptions of PSNR-HVS and PSNR-HVS-M are given in following papers:

[1] K. Egiazarian, J. Astola, N. Ponomarenko, V. Lukin, F. Battisti, M. Carli, New full-reference quality metrics based on HVS, CD-ROM Proceedings of the Second International Workshop on Video Processing and Quality Metrics, Scottsdale, USA, 2006, 4 p.

[2] Nikolay Ponomarenko, Flavia Silvestri, Karen Egiazarian, Marco Carli, Jaakko Astola, Vladimir Lukin, On between-coefficient contrast masking of DCT basis functions, CD-ROM Proceedings of the Third International Workshop on Video Processing and Quality Metrics for Consumer Electronics VPQM-07, Scottsdale, Arizona, USA, 25-26 January, 2007, 4 p.