The released version of V-MEON was implemented and has been tested in Ubuntu 16.04 with Python 3.5, torch 0.3.0.post4, and torchvision 0.2.0.
The full list of python packages in our Python virtual environment can be found below:
| Package | Version |
|---|---|
| cycler | 0.10.0 |
| decorator | 4.2.1 |
| dill | 0.2.7.1 |
| ffmpeg-python | 0.1.17 |
| matplotlib | 2.1.2 |
| networkx | 2.1 |
| numpy | 1.14.0 |
| pandas | 0.22.0 |
| Pillow | 5.0.0 |
| pip | 18.0 |
| pyparsing | 2.2.0 |
| python-dateutil | 2.6.1 |
| pytz | 2017.3 |
| PyWavelets | 0.5.2 |
| PyYAML | 3.12 |
| scikit-image | 0.13.1 |
| scipy | 1.0.0 |
| setuptools | 38.4.0 |
| six | 1.11.0 |
| torch | 0.3.0.post4 |
| torchvision | 0.2.0 |
| wheel | 0.30.0 |
An example of the usage of the V-MEON codes can be found in Main.py.
V-MEON for blind video quality assessment, Version 1.0 Copyright(c) 2018 Wentao Liu, Zhengfang Duanmu All Rights Reserved.
Permission to use, copy, or modify this software and its documentation for educational and research purposes only and without fee is hereby granted, provided that this copyright notice and the original authors' names appear on all copies and supporting documentation. This program shall not be used, rewritten, or adapted as the basis of a commercial software or hardware product without first obtaining permission of the authors. The authors make no representations about the suitability of this software for any purpose. It is provided "as is" without explicit or implied warranty.
This is an implementation of the V-MEON model for predicting the perceptual quality of a video compressed by H.264, HEVC, or MPEG4_Visual
Please refer to the following paper and readme.txt with suggested usage
W. Liu, Z. Duanmu, and Z. Wang, "End-to-end blind quality assessment of compressed videos using deep neural networks," to appear on ACM Multimedia 2018, Seoul, Korea, Oct. 2018.
Kindly report any suggestions or corrections to w238liu@uwaterloo.ca