ENGLISH VERSION

MSU Brightness Independent PSNR
(BI-PSNR)

MSU Graphics & Media Lab (Video Group)

Take a look at this article on the new site! Follow the link
https://videoprocessing.ai/vqmt/plugins-bi-psnr.html

About Metrics Python Online help Changelog Screenshots Plugins & SDK

Project, Ideas: Dr. Dmitriy Vatolin
Implementation: Alexey Noskov
Updating and additions: Sergey Grishin

Common idea

BI-PSNR metric is intended for measuring distortions in video taking into account brightness shifts.

Change Log

[!] - Known bug
[+] - New Feature
[*] - Other

Version 1.1
[*] Visualization bug fixed for non-stadard resolution video

Version 1.0
[+] First plugin release

Usage

BI-PSNR metric should be used when one of the sequences has any brightness transformation, which does not change within frame. Example of such transformation is uniform increasing of brightness of contrast for single frame of for all sequence. Such transformations prevent usage of standard metrics because of strong brightness difference between comparing frames. BI-PSNR algorithm calculates brightness transformation, which makes frames similar as possible and calculates standard PSNR and MSE metrics taking into account founded transformation.

Visualization

There are two part of visualization:

  • MSE visualization for frame. Colors of visualization are standard for MSE (in order of error increasing): black-blue-green-red.
  • Brightness transformation plot. X-axis is brightness at the first sequence, Y-axis - brightness on the second one. Green points are values, which corresponds to each other (brightness transformation). Red diagonal is identical transformation (no brightness changes).
Example of visualization
Example of visualization
Visualization of the same frame using standard PSNR
Visualization of the same frame using standard PSNR

Plots

Plots of per-frame PSNR values after the found transformation are drawing after all calculations. Plots are entirely the same as standard per-frame PSNR

Plot's example
Plot's example
Standard PSNR plot for the same sequence
Standard PSNR plot for the same sequence

Algorithm

Table C[i,j] is filling for each frame: C[i,j] = { number of points in the same position, which have brightness i at the first sequence frame and j at the second sequence frame }

Next, for each i (brightness value from the first frame) we find corresponding brightness from the second sequence. Following formula is used to estimate distanse from arbitrary values of i and j:

One can note that this formula is sum of quadratic differences between all pixels of the first sequence with value i and all corresponding pixels from second sequence on the assumption that brightness was shifted to i-j.

When transformation was found, we can find MSE for the frame taking into account this transformation:

Download

MSU Video Quality Measurement Tools

e-mail: 

Other resources

Video resources:

3D and stereo video
Projects on 3D and stereo video processing and analysis
MSU Video Quality Measurement tools
Programs with different objective and subjective video quality metrics implementation 		Codecs comparisons
Objective and subjective quality evaluation
tests for video and image codecs
Ext. link: x264 parameters efficiency comparison
Public MSU video filters
Here are available VirtualDub and AviSynth filters. For a given type of digital video filtration we typically develop a family of different algorithms and implementations. Generally there are also versions optimized for PC and hardware implementations (ASIC/FPGA/DSP). These optimized versions can be licensed to companies. Please contact us for details via video(at)graphics.cs.msu_ru. 		Filters for companies
We are working with Intel, Samsung, RealNetworks and other companies on adapting our filters other video processing algorithms for specific video streams, applications and hardware like TV-sets, graphics cards, etc. Some of such projects are non-exclusive. Also we have internal researches. Please let us know via video(at)graphics.cs.msu_ru if you are interested in acquiring a license for such filters or making a custom R&D project on video processing, compression, computer vision.
Video codecs projects
Different research and development
projects on video codecs 		Other
Other information
Last updated: 12-May-2022

Server size: 8069 files, 1215Mb (Server statistics)

Project updated by
Server Team and MSU Video Group

Project sponsored by YUVsoft Corp.

Project supported by MSU Graphics & Media Lab