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 Contrast Sensitivity Function (CSF),
• PSNR-HVS-M: Peak Signal-to-Noise Ratio taking into account Contrast Sensitivity Function (CSF) and between-coefficient contrast masking of DCT basis functions.

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

The OpenCV library (http://opencv.willowgarage.com/wiki/) needs to be installed to 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 the building step:

make

This command has the effect of creating the build directory, calling cmake within it and building VQMT. The binary may then be found in build/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, progressively scanned, and 8 bits per sample
• ProcessedVideo: the processed video as raw YUV video file, progressively scanned, 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 Contrast Sensitivity Function (CSF) (PSNR-HVS)
• PSNRHVSM: Peak Signal-to-Noise Ratio taking into account Contrast Sensitivity Function (CSF) and between-coefficient contrast masking of DCT basis functions (PSNR-HVS-M)

Example:

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

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 it to get the output)
• PSNRHVS and PSNRHVSM are always computed at the same time (but you still need to 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 or royalty fees, to use, copy, modify, and distribute the software provided and its documentation for research purpose only, provided that this copyright notice and the original authors' names appear on all copies and supporting documentation. The software provided may not be commercially distributed. In no event shall the Ecole Polytechnique Fédérale de Lausanne (EPFL) be liable to any party for direct, indirect, special, incidental, or consequential damages arising out of the use of the software and its documentation. The Ecole Polytechnique Fédérale de Lausanne (EPFL) specifically disclaims any warranties. The software provided hereunder is on an "as is" basis and the Ecole Polytechnique Fédérale de Lausanne (EPFL) has no obligation to provide maintenance, support, updates, enhancements, or modifications.

• Z. Wang, A.C. Bovik, H.R. Sheikh, and E.P. Simoncelli, "Image quality assessment: from error visibility to structural similarity," IEEE Transactions on Image Processing, vol. 13, no. 4, pp. 600–612, April 2004.
• Z. Wang, E.P. Simoncelli, and A.C. Bovik, "Multiscale structural similarity for 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," IEEE Transactions 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 of the 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," in Proceedings of the Third International Workshop on Video Processing and Quality Metrics for Consumer Electronics, January 2007.