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RUSSIAN VERSION

MSU Scene Change Detector
(SCD)

MSU Graphics & Media Lab (Video Group)

Project, Ideas: Dr. Dmitriy Vatolin, Alexander Parshin
Implementation: Ivan Glazistov

 

Common Description


Scene Change Detector is made to automatic identification of scene boundaries in video sequence.

 

Usage


The plugin implements four algorithms of similarity measurements between two adjacency frames in video sequence:

  1. Pixel-level frames comparison
  2. Global Histogram comparison
  3. Block-Based Histogram comparison
  4. Motion-Based similarity measure
The choice of the algorithm can be made in Settings. Numbers from 1 up to 4 corresponds to each algorithm.

Default and recommended value is 3 (Block-Based Histogram).

 

Visualisation


Y-plane is drawing during the visualization. Brightness of scene boundary frames is increased.

Example of visualization:

 

Plots

Metric's plot is making after all measurements. "One" value means that current frame is the first frame in scene, other frames have "zero" values. Sequence average value is the number of detected scene changes.

Plot's example:

 

Algorithm


Pixel-level comparison

Similarity measure of two frames is the sum of absolute differences (SAD) between corresponding pixels values.

Global Histogram

The histogram is obtained by counting the number of pixels in frame with specified brightness level. The difference between two histograms is then determined calculating SAD of number of pixels on each brightness level.

Block-Based Histogram

Each frame is divided into 16x16 pixel blocks. Brightness distribution histogram is constructed for each block. Then similarity measure for each block is obtained. Average value of these measures is accepted as a frames similarity measure.

Motion-Based

Motion Estimation algorithm with block size 16x16 pixels is performed for two adjacency frames at the first stage. After that average value of motion vector errors is accepted as a finally similarity measure.

 

Download

e-mail: 

 

Other resources

Video resources:

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.
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
MSU Video Quality Measurement tools
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: 28-April-2009

Search (Russian):
Server size: 7898 files, 968Mb (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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