PSNR

This metric, which is used often in practice, called peak-to-peak signal-to-noise ratio — PSNR.

,

where MaxErr - maximum possible absolute value of color components difference, w - video width, h - video height. Generally, this metric is equivalent to Mean Square Error, but it is more convenient to use because of logarithmic scale. It has the same disadvantages as the MSE metric.
In MSU VQMT you can calculate PSNR for all YUV and RGB components and for L component of LUV color space.
In MSU VQMT there are four PSNR implementations. "PSNR" and "APSNR" use the correct way of PSNR calculation and take maximum possible absolute value of color difference as MaxErr. But this way of calculation gives an unpleasant effect after color depth conversion. If color depth is simply increased from 8 to 16 bits, the "PSNR" and "APSNR" will change, because MaxErr should change according to maximum possible absolute value of color difference (255 for 8 bit components and 255 + 255/256 for 16 bit components). Thus "PSNR (256)" and "APSNR (256)" are implemented. They would not change because they use upper boundary of color difference as MaxErr. The upper boundary is 256. This approach is less correct but it is used often because it is fast. Here are the rules of MaxErr definition:

• "PSNR" and "APSNR" - MaxErr varies on color components bits usage:
  • 255 for 8 bit components
  • 255 + 3/4 for 10 bit components
  • 255 + 63/64 for 14 bit components
  • 255 + 255/256 for 16 bit components
  • 100 for L component of LUV color space
  Notes
  1. If bits depth differs for two compared videos, then maximum bits usage is taken to select MaxErr.
  2. All color space conversions are assumed to lead to 8 bit images. It means that if, for example, you are measuring R-RGB PSNR for 14 bit YUV file, then 255 will be taken as MaxErr.
• "PSNR (256)" and "APSNR (256)" - MaxErr is selected according to the next rules:
  • 256 for YUV and RGB color spaces
  • 100 for L component of LUV color space

This way of average PSNR calculation is used in "PSNR" and "PSNR (256)". But sometimes it is needed to take simple average of all the per frame PSNR values. "APSNR" and "APSNR (256)" are implemented for this case and calculate average PSNR by simply averaging per frame PSNR values.
The next table summarizes the differences:

Source

Processed

Y-YUV PSNR

MSAD

The value of this metric is the mean absolute difference of the color components in the correspondent points of image. This metric is used for testing codecs and filters.

Source

Processed

MSAD

Delta

The value of this metric is the mean difference of the color components in the correspondent points of image. This metric is used for testing codecs and filters.

Source

Processed

Delta

MSU Blurring Metric

This metric allows you to compare power of blurring of two images. If value of the metric for first picture is greater, than for second it means that second picture is more blurred, than first one.

Source

Processed

MSU Blurring Metric

MSU Blocking Metric

This metric was created to measure subjective blocking effect in video sequence. For example, in contrast areas of the frame blocking is not appreciable, but in smooth areas these edges are conspicuous. This metric also contains heuristic method for detecting objects edges, which are placed to the edge of the block. In this case metric value is pulled down, it allows to measure blocking more precisely. We use information from previous frames to achieve better accuracy.

Source

MSU Blocking Metric

SSIM Index

SSIM Index is based on measuring of three components (luminance similarity, contrast similarity and structural similarity) and combining them into result value.
Original paper

Original	Compressed
SSIM (fast)	SSIM (precise)

There are 2 implementation of SSIM in our program: fast and precise. The fast one is equal to our previous SSIM implementation. The difference is that the fast one uses box filter, while the precise one uses Gauss blur.
Notes:

1. Fast implementation visualization seems to be shifted. This effect originates from the sum calculation algorithm for the box filter. The sum is calculated over the block to the bottom-left or up-left of the pixel (depending on if the image is bottom-up or top-down).
2. SSIM metric has 2 coefficients. They depend on the maximum value of the image color component. They are calculated using the following equations:
   • C1 = 0.01 * 0.01 * video1Max * video2Max
   • C1 = 0.03 * 0.03 * video1Max * video2Max
   where video1Max is the maximum value of a given color component for the first video, video2Max is the maximum value of the same color component for the second video. Maximum value of a color component is calculated in the same way as for PSNR:
   • videoMax = 255 for 8 bit color components
   • videoMax = 255 + 3/4 for 10 bit color components
   • videoMax = 255 + 63/64 for 14 bit color components
   • videoMax = 255 + 255/256 for 16 bit color components

MultiScale SSIM INDEX

MultiScale SSIM INDEX based on SSIM metric of several downscaled levels of original images. Result is weighted average of those metrics.
Original paper

Original frame	Compressed frame
MSSSIM (fast)	MSSSIM (precise)

More bright areas corresponds to greater difference.

There are two algorithms realized for MultiScale SSIM - fast and precise, as for SSIM metric. Как и для метрики SSIM, реализовано два варианта MSSSIM быстрый (fast) и точный (precise). The difference is that the fast one uses box filter, while the precise one uses Gauss blur.
Notes:

1. Due to result metric calculated as multiplication of several metric values below 1.0 visualization seems to be dark. Fast implementation visualization seems to be shifted. This effect originates from the sum calculation algorithm for the box filter. The sum is calculated over the block to the bottom-left or up-left of the pixel (depending on if the image is bottom-up or top-down).
2. Levels weights (0 corresponds to original frame, while 4 corresponds to higher level):
   • WEIGHTS[0] = 0.0448;
   • WEIGHTS[1] = 0.2856;
   • WEIGHTS[2] = 0.3001;
   • WEIGHTS[3] = 0.2363;
   • WEIGHTS[4] = 0.1333;

3-Component SSIM INDEX

3-Component SSIM Index based on region division of source frames. There are 3 types of regions - edges, textures and smooth regions. Result metric calculated as weighted average of SSIM metric for those regions. In fact human eye can see difference on textured or edge regions precisely than on smooth regions. Division based on gradient magnitude in every pixel of images.
Original paper

Original frame	Compressed frame
3-SSIM regions division	3-SSIM metric

More bright areas corresponds to greater difference.

MSE

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Source

Processed

Y-YUV MSE

Information About YUV <=> RGB Tables

    Y = (0.257 * R) + (0.504 * G) + (0.098 * B) + 16 

    U = -(0.148 * R) - (0.291 * G) + (0.439 * B) + 128 

    V =  (0.439 * R) - (0.368 * G) - (0.071 * B) + 128 
        

    R = 1.164 * (Y - 16) + 1.596 * (V - 128)

    G = 1.164 * (Y - 16) - 0.391 * (U - 128) - 0.813 * (V - 128)

    B = 1.164 * (Y - 16) + 2.018 * (U - 128)
        

    Y = 0.299 * R + 0.587 * G + 0.114 * B

    U = -(0.147) * R - 0.289 * G + 0.436 * B

    V = 0.615 * R - 0.515 * G - 0.100 * B
        

    R = Y + 1.14 * V

    G = Y - 0.395 * U - 0.581 * V
                                         
    B = Y + 2.032 * U
        

Other resources

Call for MPEG4-AVC/H.264, HEVC codecs 2013!
First Report on S3D quality assessment (5 films) is released!
Appendix to Eighth MSU Н.264 codec comparison is released

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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 Cartoon Restore MSU Noise Estimation MSU Frame Rate Conversion MSU Image Restoration MSU Denoising (Top!) MSU Old Cinema MSU Deblocking (Top!) MSU Smart Brightness and Contrast (Top!) MSU Smart Sharpen (Top!) MSU Noise generation MSU Noise estimation MSU Motion Estimation Information MSU Subtitles removal MSU Logo removal (Top!) MSU Deflicker (Top!) MSU Field Shift Fixer AviSynth plug-in MSU StegoVideo MSU Cartoonizer (Top!) MSU SmartDeblocking (Top!) MSU Color Enhancement MSU Old Color Restoration MSU TV Commercial Detector MSU filters FAQ (Read me!) MSU filters statistics	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. 3D Displays Video Generation 3D Displays Video Capturing Stereo Video Depth Map Generation Automatic Objects Segmentation Semiautomatic Objects Segmentation New Frame Rate Conversion New Deinterlacer MSU-Samsung Deinterlacing Project Digital TV Signal Enhancement Old Film Recovery Tuner TV Restore Panorama Video2Photo SuperResolution SuperPrecision MSU-Samsung image and video resampling MSU-Samsung Frame Rate Conversion Motion Phase filter Deshaker (video stabilization) Film Grain/Degrain filter Deblurring filter Video Content Search
Video codecs projects Different research and development projects on video codecs MSU Lossless Video Codec (Top!) MSU Screen Capture Lossless Codec (Top!) MSU MPEG-2 Video Codec x264 Codec Improvement	Other Other information Crazy gallery (filters screams :) License for commercial usage of MSU VideoGroup Public Software (please be careful: some soft like metrics has another license!)

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Last updated: 25-August-2011

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