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Video Quality Indicators
Here you can download the indicators. You can download them for free, use it for research, but please refer to its original source (corresponding paper + link to this site).
Nawała, Jakub; Janowski, Lucjan; Leszczuk, Mikołaj
Modeling of quality of experience in no-reference model Journal Article
In: Journal of Telecommunications and Information Technology, 2017.
@article{nawala2017modeling,
title = {Modeling of quality of experience in no-reference model},
author = {Jakub Nawała and Lucjan Janowski and Mikołaj Leszczuk},
year = {2017},
date = {2017-01-01},
urldate = {2017-01-01},
journal = {Journal of Telecommunications and Information Technology},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
If you refer to individual indicators, please cite specific papers from the table below.
Python Package
The project is available as Python package that performs all the calculations on the video input file.
pip install agh-vqis
Single Executable:
The project is also available as one executable file that performs all the calculations on the .yuv input file or as separated indicators implemented in MATLAB.
Apart from the single executable file that calculates indicators using the RAW videos, you can download here a BASH/BATCH script that utilizes the ffmpeg tools in order to process whole folders of videos saved with normal extensions like: .avi, .mkv, etc.
The only prerequisite is to have the ffmpeg tools installed.
If you need to process the large amount of YUV videos and create the large spread sheet comprising all the results you can download here the python script written by Fredrik Pihl.
In order to use it you need to install the “xlsxwriter” Python module and rename all the video files according to the following convention: NAME_WIDTHxHEIGHT_FPS_….yuv. The last prerequisite is to have the “mitsuLinuxMultithread” binary in the same directory as this script.
min. = 0 (blackout did not occur) max. = 1 (blackout occurred)
Greater value → greater distortion (in this case distortion occurs)
0
Indicator treats every unitary frame as blackout (regardless of color).
Blockiness
min. = 0 max. = 3570 (values goes to infinity therefore the results were limited)
Greater value → less visible distortion
From 0.9 to 1.01
Block Loss
min. = 0 max. ≈ 100 – 200
Greater value → more visible distortion
From 0 to 5
Indicator returns -1 if the image has a width or height less than 256 pixels.
Blur
min. = 0 max. ≈ 70
Greater value → more visible distortion
From 0 to 5
Contrast
min. = 0 max. ≈ 120
Greater value → higher contrast
From 45 to 55
Exposure
min. = 0 max. = 255
Greater value → greater exposure time
From 115 to 125
Flickering
min. = 0 max. = 8
Greater value → more visible distortion
For time window with a length of 8 frames typical value is around 0.125
Indicator operates on observation window = number of consecutive frames taken into account when calculating flicker blocks indicator. Result is returned only at the end of the observation window, during the window, the indicator returns -1.
Freezing
min. = 0 max. = 1
Greater value → greater distortion (in this case distortion occurs)
0
Indicator is coupled with the results of Temporal Activity indicator.
Interlacing
min. = 0 max. = 1
Greater value → greater distortion
0
Letter-boxing
min. = 0 max. = 1
Greater value → greater distortion
0
Value 1 means that the entire frame is smooth (blackout).
Noise
min. = 0 max. = 30
Greater value → greater distortion
From 0 to 3.5
Pillar-boxing
min. = 0 max. = 1
Greater value → greater distortion
0
Value 1 means that the entire frame is smooth (blackout).
Slicing
min. ≈ 0 max. = &infin
≈ 0
Indicator does not work correctly.
Spatial Activity
min. = 0 max. ≈ 270
Greater value → greater Spatial Activity
From 0 to 60
Temporal Activity
min. = 0 max. = 255 (for fullHD)
Greater value → greater Temporal Activity
From 0 to 20
Range of results depends on size of the test image.
Colourfulness
min. = 0 max. ≈ 170
Greater value → more colourful
None
Indicating amount of colourfulness.
Blur Amount
min. = 0 max. = 1
Greater value → less blurred
1.0
Computing the cumulative probability of blur detection.
User-Generated Content
min. = 0 max. = 1
1 → UGC 0 → professional (non-UGC)
1
Indicating whether content was professionally generated or not.
Lip Sync
Coming soon!
List of implemented indicators
Video Indicators
Not all video errors are detected as the indicator list does not include all possible indicators. Moreover, after obtaining the values of the indicators, it is recommended to analyze their time series. It is also worth noting that there is currently no support for resolution changes (although this support is under preparation).