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RUSSIAN VERSION

Noise Estimation metric
(Noise Estimator)

MSU Graphics & Media Lab (Video Group)

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

 

Common idea


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

 

Usage


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

  • MAD
  • Block-Based
  • Spatio-Temporal Gradients
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:

 

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


See also MSU Video Quality Metric

e-mail: 

 

Other resources

Video resources:

Public MSU video filters
Here are available VirtualDub and AviSynth filters. Commonly we develop a whole family of some kind of a filter. 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.
MSU 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.
Codecs comparisons
Objective and subjective quality evaluation
tests for video and image codecs
Ext. link: x264 parameters efficiency comparison
Video quality metrics
Programs with different objective and subjective video quality metrics implementation
Video codecs projects
Different research and development
projects on video codecs
Other
Other information

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Last updated: 07-December-2007

Search (Russian):
Server size: 7629 files, 938Mb (Server statistics)

Project updated by
Server Team and MSU Video Group


Project sponsored by YUVsoft Corp.

Project supported by MSU Graphics & Media Lab

 
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