sureal 0.1.0

Subjective quality scores recovery from noisy measurements.

SUREAL - Subjective Recovery Analysis

SUREAL is a toolbox developed by Netflix for recovering quality scores from noisy measurements obtained by subjective tests. Read this paper for some background.

Prerequisites & Installation

SUREAL requires a number of Python packages pre-installed:

• numpy (>=1.12.0)
• scipy (>=0.17.1)
• matplotlib (>=2.0.0)
• pandas (>=0.19.2)
• scikit-learn (>=0.18.1)

First, upgrade pip to the newest version:

sudo -H pip install --upgrade pip

Then install the required Python packages:

pip install --user numpy scipy matplotlib pandas scikit-learn

Add the python/src subdirectories to the environment variable PYTHONPATH:

export PYTHONPATH="$(pwd)/python/src:$PYTHONPATH"

You can also add it to the environment permanently, by appending to ~/.bashrc:

echo export PYTHONPATH="$(pwd)/python/src:$PYTHONPATH" >> ~/.bashrc
source ~/.bashrc

Under macOS, use ~/.bash_profile instead.

Testing

The package has thus far been tested on Ubuntu 16.04 LTS and macOS 10.13. After installation, run:

./unittest

Usage in Command Line

Under root directory, run ./run_subj to print usage information:

usage: run_subj subjective_model dataset_filepath 

Below are two example usages:

./run_subj MLE resource/dataset/NFLX_dataset_public_raw_last4outliers.py
./run_subj MLE_CO resource/dataset/VQEGHD3_dataset_raw.py

Here subjective_model are the available subjective models offered in the package, including:

• MOS - Standard mean opinion score
• MLE - Full maximum likelihood estimation (MLE) model that takes into account both subjects and contents
• MLE_CO - MLE model that takes into account only subjects ("Content-Oblivious")
• DMOS - Differential MOS, as defined in ITU-T P.910
• DMOS_MLE - apply MLE on DMOS
• DMOS_MLE_CO - apply MLE_CO on DMOS
• SR_MOS - Apply subject rejection (SR), as defined in ITU-R BT.500, before calculating MOS
• ZS_SR_MOS - Apply z-score transformation, followed by SR, before calculating MOS
• SR_DMOS - Apply SR, before calculating DMOS
• ZS_SR_DMOS - Apply z-score transformation, followed by SR, before calculating DMOS

dataset_filepath is the path to a dataset file. There are two ways to construct a dataset file. The first way is only useful when the subjective test is full sampling, i.e. every subject views every distorted video. For example:

from vmaf.config import VmafConfig
ref_videos = [
    {'content_id': 0, 'content_name': 'checkerboard', 'path': 
        VmafConfig.test_resource_path('yuv', 'checkerboard_1920_1080_10_3_0_0.yuv')},
    {'content_id': 1, 'content_name': 'flat', 'path': 
        VmafConfig.test_resource_path('yuv', 'flat_1920_1080_0.yuv')},
]
dis_videos = [
    {'content_id': 0, 'asset_id': 0, 'os': [100, 100, 100, 100, 100], 'path': 
        VmafConfig.test_resource_path('yuv', 'checkerboard_1920_1080_10_3_0_0.yuv')},
    {'content_id': 0, 'asset_id': 1, 'os': [40, 45, 50, 55, 60],  'path': 
        VmafConfig.test_resource_path('yuv', 'checkerboard_1920_1080_10_3_1_0.yuv')},
    {'content_id': 1, 'asset_id': 2, 'os': [90, 90, 90, 90, 90],  'path': 
        VmafConfig.test_resource_path('yuv', 'flat_1920_1080_0.yuv')},
    {'content_id': 1, 'asset_id': 3, 'os': [70, 75, 80, 85, 90],  'path': 
        VmafConfig.test_resource_path('yuv', 'flat_1920_1080_10.yuv')},
]
ref_score = 100

In this example, ref_videos is a list of reference videos. Each entry is a dictionary, and must have keys content_id, content_name and path (the path to the reference video file). dis_videos is a list of distorted videos. Each entry is a dictionary, and must have keys content_id (the same content ID as the distorted video's corresponding reference video), asset_id, os (stands for "opinion score"), and path (the path to the distorted video file). The value of os is a list of scores, reach voted by a subject, and must have the same length for all distorted videos (since it is full sampling). ref_score is the score assigned to a reference video, and is required when differential score is calculated, for example, in DMOS.

The second way is more general, and can be used when the test is full sampling or partial sampling (i.e. not every subject views every distorted video). The only difference from the first way is that, the value of os is now a dictionary, with the key being a subject ID, and the value being his/her voted score for particular distorted video. For example:

'os': {'Alice': 40, 'Bob': 45, 'Charlie': 50, 'David': 55, 'Elvis': 60}

Since partial sampling is allowed, it is not required that every subject ID is present in every os dictionary.

Usage in a Python Script

More examples of using the subjective models in a Python script can be found in /python/script/run_subjective_models.py. Run the script first to get a sense of what it does.