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Subjective Recovery Analysis

Project description

SUREAL - Subjective Recovery Analysis

Version on pypi Build Status

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

Currently, SUREAL supports Python 2.7 and 3.7.

Installation

SUREAL can be either installed through pip (available via PyPI), or locally.

Installation through pip

To install SUREAL via pip, run:

pip install sureal

Local installation

To install locally, first, download the source. Under the root directory, (perferrably in a virtualenv), install the requirements:

pip install -r requirements.txt

Under Ubuntu, you may also need to install the python-tk (Python 2) or python3-tk (Python 3) packages via apt.

To test the source code before installing, run:

python -m unittest discover -s test -p '*_test.py'

The code thus far has been tested on Ubuntu 16.04 LTS and macOS 10.13.

Lastly, install SUREAL by:

pip install .

If you want to edit the source, use pip install --editable . or pip install -e . instead. Having --editable allows the changes made in the source to be picked up immediately without re-running pip install .

Usage in command line

Run:

sureal

This will print usage information:

usage: subjective_model dataset_filepath [--output-dir output_dir] [--print]

If --output-dir is given, plots will be written to the output directory.

If --print is enabled, output statistics will be printed on the command-line and / or the output directory.

Below are two example usages:

sureal MLE resource/dataset/NFLX_dataset_public_raw_last4outliers.py
sureal 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](https://www.itu.int/rec/T-REC-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](https://www.itu.int/rec/R-REC-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 files

dataset_filepath is the path to a dataset file. Dataset files may be .py or .json files. The following examples use .py files, but JSON-formatted files can be constructed in a similar fashion.

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:

ref_videos = [
    {
      'content_id': 0, 'content_name': 'checkerboard',
      'path': 'checkerboard_1920_1080_10_3_0_0.yuv'
    },
    {
      'content_id': 1, 'content_name': 'flat',
      'path': 'flat_1920_1080_0.yuv'
    },
]
dis_videos = [
    {
      'content_id': 0, 'asset_id': 0,
      'os': [100, 100, 100, 100, 100],
      'path': 'checkerboard_1920_1080_10_3_0_0.yuv'
    },
    {
      'content_id': 0, 'asset_id': 1,
      'os': [40, 45, 50, 55, 60],
      'path': 'checkerboard_1920_1080_10_3_1_0.yuv'
    },
    {
      'content_id': 1, 'asset_id': 2,
      'os': [90, 90, 90, 90, 90],
      'path': 'flat_1920_1080_0.yuv'
    },
    {
      'content_id': 1, 'asset_id': 3,
      'os': [70, 75, 80, 85, 90],
      'path': '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 Python code

See here for an example script to use SUREAL in Google Collab notebook.

For developers

SUREAL uses tox to manage automatic testing and continuous integration with Travis CI on Github, and setupmeta for new version release, packaging and publishing. Refer to DEVELOPER.md for more details.

Project details


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