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MSU Video Quality Measurement tools:
metric performance

MSU Graphics & Media Lab (Video Group)

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MSU Video Quality Measurement tool metrics performance

Metrics GPU acceleration

Now SSIM-based algorithms show best subjective quality correlation among other video quality algorithms. To increase performance of SSIM-based metrics, these algorithms was implemented on graphics hardware. For implementations CUDA technology was used. SSIM, 3-SSIM, and MS-SSIM metrics implemented for now. Our CPU Implementation of the metrics above are using Intel IPP. We don’t know precision and operations order of these algorithm in the IPP, so metric values for GPU implementations can slightly differ from the CPU. These implementations can be found in the metric list in the GUI or via -metr ssim_cuda, 3ssim_cuda, msssim_cuda parameters via console line interface.
Data for this graph was obtained in the following way:

  1. Speed was measured 3 times for each metric, PC configuration and resolution via console interface of VQMT PRO version
  2. Result was calculated as median value between 3 values from the first step
Speedup results provided in the graphs below as fps graph and speedup graphs:


SSIM metric speedup graph

SSIM metric fps graph

3-SSIM metric speedup graph

3-SSIM metric fps graph

MS-SSIM metric speedup graph

MS-SSIM metric fps graph

Metric speed performance

Measurements of large files can take a very long time. We are always trying to maximize speed of metric implementations using features like multi-threading, SSE\MMX optimizations, high-performance libraries, GPU acceleration. In example, using console interface it is able compute four most popular metrics (PSNR, SSIM, 3-SSIM, MS-SSIM) in almost the same time as the slowest of them.
Data for this graph was obtained in the following way:

  1. Speed was measured 3 times for each metric, PC configuration and resolution via console interface of VQMT PRO version
  2. Time for multi-metric measurement was obtained via multiple "-metr" parameters in the console interface
  3. Result was calculated as median value between 3 values from the first step
Here we provide metric performance graph for different resolutions for two PC configurations:


Speed graph for PC Configuration: Intel Core i7 920 @ 2.67 GHz, 12GB RAM, NVIDIA GTX 580
High Resolution Video - 720p & 1080p

Speed graph for PC Configuration: Intel Core i7 920 @ 2.67 GHz, 12GB RAM, NVIDIA GTX 580
Low Resolution Video - CIF & SD

Speed graph for PC Configuration: Intel Core Quad Q6600 @ 2.4 GHz, 4GB RAM, NVIDIA GTX 285
High Resolution Video - 720p & 1080p

Speed graph for PC Configuration: Intel Core Quad Q6600 @ 2.4 GHz, 4GB RAM, NVIDIA GTX 285
Low Resolution Video - CIF & SD

Metric speed performance with correlation

We are also providing Speed/Correlation plot, which is allows user to understand difference between metrics.
Information about subjective quality you can find here.
Data provided for 1080p resolution and following configuration: Intel Core i7 920 @ 2.67 GHz, 12GB RAM, NVIDIA GTX 580.


Metrics speed/correlation plot for 1080p resolution on Intel Core i7 with NVIDIA GTX 580

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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