Hot news:

If you find a spelling error, please select an incorrect text and press Ctrl+Enter. Thank you!

Compression project >> Video Area Home
RUSSIAN VERSION

MSU Noise Estimation Metric
(NE)

MSU Graphics & Media Lab (Video Group)

Project, idea: Dr. Dmitriy Vatolin, Sergey Grishin
Algorithm, implementation: Kumok Boris
Updating and additions: Sheludko Victor, Sergey Putilin, Sergey Grishin


Common idea



Metrics Noise Estimator is intended for calculation of noise level for each frame of video sequence.


Changelog


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

0.3
[*] Incorrect processing of non-standard resolution video bug fixed

0.2
[*] Bug with incorrect (identical) values for some videos is fixed.
[*] Home page is fixed (was incorrect)
[*] Command line name is changed (became easier to use)

0.1
[*] First released version.


Usage



The metrics realizes three various algorithms of definition of noise level:

The choice of which algorithm to use can be made in Settings. To algorithms there correspond figures from 0 up to 2.


Visualization



Visualization of the metrics does not carry any information.


Plots


By results of job of the metrics the plot of frame-accurate value of noise level is constructed. Final value of the metrics is average arithmetic of all frame-accurate values.

Plot's example
Plot's example

Algorithm



MAD

For each frame do HAAR wavelet decomposition. Than evaluate median of HH-component's absolute values. Final value of the metrics is the normalized median.

Block-Based

Frames are tessellated into a number of 8x8 blocks. Standard deviations of intensity (measures of intensity variation) are computed for all the blocks and sorted. The block with the smallest standard deviation has the least change of intensity. The smaller the standard deviation, the smother the block. The intensity variation of a smooth block may be due to noise, in which the standard deviation of the block is close to that of the Gaussian noise added. Normalized average arithmetic values of 30 % of all blocks with the least values grows is the final value of the metric.

Spatio-Temporal Gradients

For each frame is doing wavelet decomposition. computing temporal and spatial histograms. The initial estimation of noise level is defined by value at which temporal or spatial histogram achieves the maximal value. The decision of whether to use the spatial or temporal histogram is based on the deviation of the histogram from the Rayleigh distribution. Then this estimation is corrected, using Kolmogorov-Smirnoff test. The normalized corrected estimation is the final value of the metric.

Download




MSU Video Quality Measurement Tools


e-mail: 


Other resources


Video resources:

Bookmark this page:   Add to Del.icio.us Add to Del.icio.us     Digg It Digg It     reddit reddit

 
Last updated: 09-December-2016

Search (Russian):
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