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This software provides fast implementations of the following objective metrics:

• PSNR: Peak Signal-to-Noise Ratio,
• SSIM: Structural Similarity,
• MS-SSIM: Multi-Scale Structural Similarity,
• VIFp: Visual Information Fidelity, pixel domain version
• PSNR-HVS: Peak Signal-to-Noise Ratio taking into account ContrastSensitivity Function (CSF),
• PSNR-HVS-M: Peak Signal-to-Noise Ratio taking into account ContrastSensitivity Function (CSF) and between-coefficient contrast masking of DCTbasis functions.

In this software, the above metrics are implemented in C++ with the help ofOpenCV and are based on the original Matlab implementations provided by theirdevelopers.The source code of this software can be compiled on any platform andonly requires the OpenCV library (core and imgproc modules).This software allows performing video quality assessment without using Matlaband shows better performance than Matlab in terms of run time.

The OpenCV library (http://opencv.willowgarage.com/wiki/) needs to be installedto be able to compile this code. Only the core and imgproc modules are required.

CMake is required in order to build VQMT. A Makefile is provided to ease thebuilding step:

make

This command has the effect of creating thebuild directory, callingcmake within it and building VQMT. The binary may then be found inbuild/bin/Release.

vqmt (or VQMT.exe on Windows) OriginalVideo ProcessedVideo Height Width NumberOfFrames ChromaFormat Output Metrics

• OriginalVideo: the original video as raw YUV video file, progressivelyscanned, and 8 bits per sample
• ProcessedVideo: the processed video as raw YUV video file, progressivelyscanned, and 8 bits per sample
• Height: the height of the video
• Width: the width of the video
• NumberOfFrames: the number of frames to process
• ChromaFormat: the chroma subsampling format. 0: YUV400, 1: YUV420,2: YUV422, 3: YUV444
• Output: the name of the output file(s)
• Metrics: the list of metrics to use

Available metrics:

• PSNR: Peak Signal-to-Noise Ratio (PNSR)
• SSIM: Structural Similarity (SSIM)
• MSSSIM: Multi-Scale Structural Similarity (MS-SSIM)
• VIFP: Visual Information Fidelity, pixel domain version (VIFp)
• PSNRHVS: Peak Signal-to-Noise Ratio taking into account ContrastSensitivity Function (CSF) (PSNR-HVS)
• PSNRHVSM: Peak Signal-to-Noise Ratio taking into account ContrastSensitivity Function (CSF) and between-coefficient contrast masking of DCTbasis functions (PSNR-HVS-M)

Example:

VQMT.exe original.yuv processed.yuv 1088 1920 250 1 results PSNR SSIM MSSSIMVIFP

will create the following output files in CSV (comma-separated values) format:

• results_pnsr.csv
• results_ssim.csv
• results_msssim.csv
• results_vifp.csv

Notes:

• SSIM comes for free when MSSSIM is computed (but you still need to specify itto get the output)
• PSNRHVS and PSNRHVSM are always computed at the same time (but you still needto specify both to get the two outputs)
• When using MSSSIM, the height and width of the video have to be multiple of 16
• When using VIFP, the height and width of the video have to be multiple of 8

Permission is hereby granted, without written agreement and without license orroyalty fees, to use, copy, modify, and distribute the software provided and itsdocumentation for research purpose only, provided that this copyright notice andthe original authors' names appear on all copies and supporting documentation.The software provided may not be commercially distributed. In no event shall theEcole Polytechnique Fédérale de Lausanne (EPFL) be liable to any party fordirect, indirect, special, incidental, or consequential damages arising out ofthe use of the software and its documentation.The Ecole Polytechnique Fédérale de Lausanne (EPFL) specifically disclaims anywarranties.The software provided hereunder is on an "as is" basis and the EcolePolytechnique Fédérale de Lausanne (EPFL) has no obligation to providemaintenance, support, updates, enhancements, or modifications.

• Z. Wang, A.C. Bovik, H.R. Sheikh, and E.P. Simoncelli, "Image qualityassessment: from error visibility to structural similarity," IEEETransactions on Image Processing, vol. 13, no. 4, pp. 600–612, April 2004.
• Z. Wang, E.P. Simoncelli, and A.C. Bovik, "Multiscale structural similarityfor image quality assessment," in IEEE Asilomar Conference on Signals,Systems and Computers, November 2003, vol. 2, pp. 1398–1402.
• H.R. Sheikh and A.C. Bovik, "Image information and visual quality," IEEETransactions on Image Processing, vol. 15, no. 2, pp. 430-444, February 2006.
• K. Egiazarian, J. Astola, N. Ponomarenko, V. Lukin, F. Battisti, and M.Carli, "New full-reference quality metrics based on HVS," in Proceedings ofthe Second International Workshop on Video Processing and Quality Metrics,2006.
• N. Ponomarenko, F. Silvestri, K. Egiazarian, M. Carli, J. Astola, and V.Lukin, "On between-coefficient contrast masking of DCT basis functions," inProceedings of the Third International Workshop on Video Processing andQuality Metrics for Consumer Electronics, January 2007.